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Beginning with the 2011 growing season, this activity has\\nbeen extended to other provinces in support of a national crop inventory.\\nTo date this approach can consistently deliver a crop inventory that meets\\nthe overall target accuracy of at least 85% at a final spatial resolution of\\n30m (56m in 2009 and 2010).\\n","license":"proprietary","providers":[{"name":"Agriculture and Agri-Food Canada","roles":["producer","licensor"],"url":"https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/AAFC_ACI"}],"extent":{"spatial":{"bbox":[[-135.17,36.83,-51.24,62.25]]},"temporal":{"interval":[["2009-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/AAFC/ACI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/AAFC_ACI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/AAFC_ACI#terms-of-use"},{"rel":"source","href":"http://www.agr.gc.ca/atlas/data_donnees/agr/annualCropInventory/tif/"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"AHN/AHN2_05M_INT","title":"AHN Netherlands 0.5m DEM, Interpolated","description":"The AHN DEM is a 0.5m DEM covering the Netherlands. 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There is one image for each modeled age and sex category based\\non the 2010 round of Census.\\n\\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-basic-demographic-characteristics-rev11/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H46M34XX"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Basic_Demographic_Characteristics"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Basic_Demographic_Characteristics"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CIESIN_population_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Basic_Demographic_Characteristics#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H46M34XX"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CIESIN/GPWv411/GPW_Data_Context","title":"GPWv411: Data Context (Gridded Population of the World Version 4.11)","description":"The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\\non 30 arc-second (approximately 1km) grid cells. 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The input data are\\nextrapolated to produce population estimates for each modeled year.\\n\\n\\nCategorizes pixels with estimated zero population based on\\ninformation provided in the census documents.\\n\\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H42Z13KG"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Data_Context"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Data_Context"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CIESIN_ancillary_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Data_Context#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H42Z13KG"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CIESIN/GPWv411/GPW_Land_Area","title":"GPWv411: Land Area (Gridded Population of the World Version 4.11)","description":"The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\\non 30 arc-second (approximately 1km) grid cells. 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The input data are\\nextrapolated to produce population estimates for each modeled year.\\n\\n\\nThis data grids contains per-pixel data containing land surface area estimates.\\n\\n[General Documentation](https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-quality-indicators/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H4Z60M4Z"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Land_Area"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Land_Area"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CIESIN_ancillary_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Land_Area#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H4Z60M4Z"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area","title":"GPWv411: Mean Administrative Unit Area (Gridded Population of the World Version 4.11)","description":"The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\\non 30 arc-second (approximately 1km) grid cells. 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Pixels that have the same value reflect the same\\ndata source, most often a country or territory.\\n\\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-national-identifier-grid-rev11/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H4TD9VDP"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_National_Identifier_Grid"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_National_Identifier_Grid"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CIESIN_ancillary_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_National_Identifier_Grid#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H4TD9VDP"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CIESIN/GPWv411/GPW_Population_Count","title":"GPWv411: Population Count (Gridded Population of the World Version 4.11)","description":"The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\\non 30 arc-second (approximately 1km) grid cells. 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There is one image for each modeled year.\\n\\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H4JW8BX5"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Count"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Population_Count"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CIESIN_population_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Count#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H4JW8BX5"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CIESIN/GPWv411/GPW_Population_Density","title":"GPWv411: Population Density (Gridded Population of the World Version 4.11)","description":"The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\\non 30 arc-second (approximately 1km) grid cells. 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There is one image for each modeled year.\\n\\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H49C6VHW"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Density"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Population_Density"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CIESIN_population_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Density#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H49C6VHW"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Count","title":"GPWv411: Adjusted to Match 2015 Revision of UN WPP Country Totals (Gridded Population of the World Version 4.11)","description":"The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\\non 30 arc-second (approximately 1km) grid cells. 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There is one image for each modeled year.\\n\\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H4F47M65"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_UNWPP-Adjusted_Population_Density"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Density"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CIESIN_population_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_UNWPP-Adjusted_Population_Density#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H4F47M65"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CIESIN/GPWv411/GPW_Water_Area","title":"GPWv411: Water Area (Gridded Population of the World Version 4.11)","description":"The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\\non 30 arc-second (approximately 1km) grid cells. 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The input data are\\nextrapolated to produce population estimates for each modeled year.\\n\\n\\nIdentifies water pixels; non-water pixels are masked\\n\\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-basic-demographic-characteristics-rev11/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H42Z13KG"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Water_Mask"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Water_Mask"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CIESIN_ancillary_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Water_Mask#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H42Z13KG"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/CORINE/V20/100m","title":"Copernicus CORINE Land Cover","description":"The CORINE (coordination of information on the environment) Land Cover\\n(CLC) inventory was initiated in 1985 to standardize data collection on land\\nin Europe to support environmental policy development. The project is\\ncoordinated by the European Environment Agency (EEA) in the frame of the EU\\nCopernicus programme and implemented by national teams. The number of\\nparticipating countries has increased over time currently including 33 (EEA)\\nmember countries and six cooperating countries (EEA39) with a total area of\\nover 5.8 Mkm2.\\n\\nCLC2018 is one of the datasets produced within the frame the Corine Land Cover\\nprogramme referring to land cover / land use status of year 2018.\\nThe reference year of the first CLC inventory was 1990 and the first update\\ncreated in 2000. Later, the update cycle has become 6 years. Satellite\\nimagery provides the geometrical and thematic basis for mapping with in-situ\\ndata as essential ancillary information. The basic technical parameters of\\nCLC (i.e. 44 classes in nomenclature, 25 hectares minimum mapping unit\\n(MMU), and 100 meters minimum mapping width) have not changed since the\\nbeginning, therefore the results of the different inventories are\\ncomparable.\\n\\nThe time period covered by each asset is:\\n* 1990 asset: 1989 to 1998\\n* 2000 asset: 1999 to 2001\\n* 2006 asset: 2005 to 2007\\n* 2012 asset: 2011 to 2012\\n* 2018 asset: 2017 to 2018\\n","license":"proprietary","providers":[{"name":"EEA/Copernicus","roles":["producer","licensor"],"url":"https://land.copernicus.eu/pan-european/corine-land-cover/view"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_CORINE_V20_100m"}],"extent":{"spatial":{"bbox":[[-81.77,-29.2,94.13,73.81]]},"temporal":{"interval":[["1986-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/CORINE/V20/100m"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_CORINE_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_CORINE_V20_100m#terms-of-use"},{"rel":"source","href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-1990?tab=download"},{"rel":"source","href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2000?tab=download"},{"rel":"source","href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2006?tab=download"},{"rel":"source","href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=download"},{"rel":"source","href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/Landcover/100m/Proba-V-C3/Global","title":"Copernicus Global Land Cover Layers: CGLS-LC100 collection 3","description":"The Copernicus Global Land Service (CGLS) is earmarked as a component of\\nthe Land service to operate a multi-purpose service component that provides\\na series of bio-geophysical products on the status and evolution of land\\nsurface at global scale.\\n\\nThe Dynamic Land Cover map at 100 m resolution (CGLS-LC100) is a new product\\nin the portfolio of the CGLS and delivers a global land cover map at 100 m\\nspatial resolution. The CGLS Land Cover product provides a primary land\\ncover scheme. Next to these discrete classes, the product also includes\\ncontinuous field layers for all basic land cover classes that provide\\nproportional estimates for vegetation/ground cover for the land cover types.\\nThis continuous classification scheme may depict areas of heterogeneous\\nland cover better than the standard classification scheme and, as such,\\ncan be tailored for application use (e.g. forest monitoring, crop monitoring,\\nbiodiversity and conservation, monitoring environment and security in Africa,\\nclimate modelling, etc.).\\n\\n\\nThese consistent Land Cover maps (v3.0.1) are provided for the period\\n2015-2019 over the entire Globe, derived from the PROBA-V 100 m time-series, a\\ndatabase of high quality land cover training sites and several ancillary\\ndatasets, reaching an accuracy of 80% at Level1 over al years. It is planned\\nto provide yearly updates from 2020 through the use of a Sentinel time-series.\\n\\nSee also:\\n\\n* [Algorithm Theoretical Basis Document](https://doi.org/10.5281/zenodo.3938968)\\n\\n* [Product User Manual](https://doi.org/10.5281/zenodo.3938963)\\n\\n* [Validation Report](https://doi.org/10.5281/zenodo.3938974)\\n","license":"proprietary","providers":[{"name":"Copernicus","roles":["producer","licensor"],"url":"https://land.copernicus.eu/global/lcviewer"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2019-12-31T23:59:59Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/Landcover/100m/Proba-V-C3/Global"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_Landcover_100m_Proba-V_Global_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global#terms-of-use"},{"rel":"source","href":"https://lcviewer.vito.be/download"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.3939038"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.3518026"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.3518036"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.3518038"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.3939050"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S1_GRD","keywords":["Copernicus","SAR","ESA"],"title":"Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling","description":"The Sentinel-1 mission provides data from a dual-polarization\\nC-band Synthetic Aperture Radar (SAR) instrument. This collection\\nincludes the S1 Ground Range Detected (GRD) scenes, processed\\nusing the Sentinel-1 Toolbox to generate a calibrated, ortho-corrected\\nproduct. The collection is updated daily. New assets are ingested\\nwithin two days after they become available.\\n\\nThis collection\\ncontains all of the GRD scenes. Each scene has one of 3 resolutions\\n(10, 25 or 40 meters), 4 band combinations (corresponding to\\nscene polarization) and 3 instrument modes. Use of the collection\\nin a mosaic context will likely require filtering down to a homogeneous\\nset of bands and parameters. See [this article](https://developers.google.com/earth-engine/sentinel1)\\nfor details of collection use and preprocessing. Each scene contains\\neither 1 or 2 out of 4 possible polarization bands, depending\\non the instrument\'s polarization settings. The possible combinations\\nare single band VV or HH, and dual band VV+VH and HH+HV:\\n\\n 1. VV: single co-polarization, vertical transmit/vertical receive\\n 2. HH: single co-polarization, horizontal transmit/horizontal receive\\n 3. VV + VH: dual-band cross-polarization, vertical transmit/horizontal\\n receive\\n 4. HH + HV: dual-band cross-polarization, horizontal transmit/vertical\\n receive\\n\\nEach scene also includes an additional \'angle\' band that contains\\nthe approximate viewing incidence angle in degrees at every point.\\nThis band is generated by interpolating the \'incidenceAngle\' property\\nof the \'geolocationGridPoint\' gridded field provided with each asset.\\n\\nEach scene was pre-processed with [Sentinel-1 Toolbox](https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1)\\nusing the following steps:\\n\\n 1. Thermal noise removal\\n 2. Radiometric calibration\\n 3. Terrain correction using SRTM 30 or ASTER DEM for areas greater than\\n 60 degrees latitude, where SRTM is not available.\\nThe final terrain-corrected values are converted to decibels via log scaling\\n(10*log10(x)).\\n\\nFor more information about these pre-processing steps, please refer to the\\n[Sentinel-1 Pre-processing\\narticle](https://developers.google.com/earth-engine/sentinel1). For further\\nadvice on working with Sentinel-1 imagery, see [Guido Lemoine\'s tutorial on SAR\\nbasics](https://developers.google.com/earth-engine/tutorials/community/sar-basics)\\nand [Mort Canty\'s tutorial on SAR change\\ndetection](https://developers.google.com/earth-engine/tutorials/community/detecting-changes-in-sentinel-1-imagery-pt-1).\\n\\nThis collection is computed on-the-fly. If you want to use the underlying\\ncollection with raw power values (which is updated faster), see\\nCOPERNICUS/S1_GRD_FLOAT.\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-10-03T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S1_GRD"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/s1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S2","title":"Sentinel-2 MSI: MultiSpectral Instrument, Level-1C","deprecated":true,"description":"Sentinel-2 is a wide-swath, high-resolution, multi-spectral\\nimaging mission supporting Copernicus Land Monitoring studies,\\nincluding the monitoring of vegetation, soil and water cover,\\nas well as observation of inland waterways and coastal areas.\\n\\nThe Sentinel-2 data contain 13 UINT16 spectral bands representing\\nTOA reflectance scaled by 10000. See the [Sentinel-2 User Handbook](https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook)\\nfor details. In addition, three QA bands are present where one\\n(QA60) is a bitmask band with cloud mask information. For more\\ndetails, [see the full explanation of how cloud masks are computed.](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks)\\n\\nEach Sentinel-2 product (zip archive) may contain multiple\\ngranules. Each granule becomes a separate Earth Engine asset.\\nEE asset ids for Sentinel-2 assets have the following format:\\nCOPERNICUS/S2/20151128T002653_20151128T102149_T56MNN. Here the\\nfirst numeric part represents the sensing date and time, the\\nsecond numeric part represents the product generation date and\\ntime, and the final 6-character string is a unique granule identifier\\nindicating its UTM grid reference (see [MGRS](https://en.wikipedia.org/wiki/Military_Grid_Reference_System)).\\n\\nThe Level-2 data produced by ESA can be found in the collection\\n[COPERNICUS/S2_SR](COPERNICUS_S2_SR).\\n\\nClouds can be mostly removed by using\\n[COPERNICUS/S2_CLOUD_PROBABILITY](COPERNICUS_S2_CLOUD_PROBABILITY).\\n\\nFor more details on Sentinel-2 radiometric resolution, [see this page](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric).\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-1c"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2"}],"extent":{"spatial":{"bbox":[[-180,-56,180,83]]},"temporal":{"interval":[["2015-06-23T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/sentinel2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S2_CLOUD_PROBABILITY","title":"Sentinel-2: Cloud Probability","description":"The S2 cloud probability is created with the\\n[sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector)\\nlibrary (using [LightGBM](https://github.com/microsoft/LightGBM)).\\nAll bands are upsampled using bilinear interpolation to 10m resolution\\nbefore the gradient boost base algorithm is applied. The resulting\\n`0..1` floating point probability is scaled to `0..100` and stored as\\na UINT8. Areas missing any or all of the bands are masked out.\\nHigher values are more likely to be clouds or highly reflective surfaces\\n(e.g. roof tops or snow).\\n\\nSentinel-2 is a wide-swath, high-resolution, multi-spectral\\nimaging mission supporting Copernicus Land Monitoring studies,\\nincluding the monitoring of vegetation, soil and water cover,\\nas well as observation of inland waterways and coastal areas.\\n\\nThe Level-2 data can be found in the collection\\n[COPERNICUS/S2_SR](COPERNICUS_S2_SR). The Level-1B data can be found in the collection\\n[COPERNICUS/S2](COPERNICUS_S2). Additional metadata is available on assets in those\\ncollections.\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus/SentinelHub","roles":["producer","licensor"],"url":"https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-1c"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY"}],"extent":{"spatial":{"bbox":[[-180,-56,180,83]]},"temporal":{"interval":[["2015-06-23T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_CLOUD_PROBABILITY"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S2_CLOUD_PROBABILITIES_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S2_SR","title":"Sentinel-2 MSI: MultiSpectral Instrument, Level-2A","description":"Sentinel-2 is a wide-swath, high-resolution, multi-spectral\\nimaging mission supporting Copernicus Land Monitoring studies,\\nincluding the monitoring of vegetation, soil and water cover,\\nas well as observation of inland waterways and coastal areas.\\n\\nThe Sentinel-2 L2 data are downloaded from scihub. They were\\ncomputed by running sen2cor. WARNING: ESA did not produce L2 data\\nfor all L1 assets, and earlier L2 coverage is not global.\\n\\nThe assets contain\\n12 UINT16 spectral bands representing SR scaled by 10000 (unlike in L1 data,\\nthere is no B10). There are also several more L2-specific bands (see band\\nlist for details). See the\\n[Sentinel-2 User Handbook](https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook)\\nfor details. In addition, three QA bands are present where one\\n(QA60) is a bitmask band with cloud mask information. For more\\ndetails, [see the full explanation of how cloud masks are computed.](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks)\\n\\nEE asset ids for Sentinel-2 L2 assets have the following format:\\nCOPERNICUS/S2_SR/20151128T002653_20151128T102149_T56MNN. Here the\\nfirst numeric part represents the sensing date and time, the\\nsecond numeric part represents the product generation date and\\ntime, and the final 6-character string is a unique granule identifier\\nindicating its UTM grid reference (see [MGRS](https://en.wikipedia.org/wiki/Military_Grid_Reference_System)).\\n\\nClouds can be removed by using\\n[COPERNICUS/S2_CLOUD_PROBABILITY](COPERNICUS_S2_CLOUD_PROBABILITY).\\n\\nFor more details on Sentinel-2 radiometric resolution, [see this page](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric).\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR"}],"extent":{"spatial":{"bbox":[[-180,-56,180,83]]},"temporal":{"interval":[["2017-03-28T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S2_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S3/OLCI","title":"Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution","description":"The Ocean and Land Color Instrument (OLCI) Earth Observation\\nFull Resolution (EFR) dataset contains top of atmosphere radiances\\nat 21 spectral bands with center wavelengths ranging between\\n0.4μm and 1.02μm at spatial resolution of 300m with\\nworldwide coverage every ~2 days.\\n\\nOLCI is one of the instruments\\nin the ESA/EUMETSAT Sentinel-3 mission for measuring sea-surface\\ntopography, sea- and land-surface temperature, ocean color and\\nland color with high-end accuracy and reliability to support\\nocean forecasting systems, as well as environmental and climate\\nmonitoring.\\n\\nThe Sentinel-3 OLCI instrument is based on the\\noptomechanical and imaging design of ENVISAT\'s MERIS. It is designed\\nto retrieve the spectral distribution of upwelling radiance just\\nabove the sea surface (the water-leaving radiance).\\n\\nOLCI observation is performed simultaneously in 21 spectral bands\\nranging from the visible to the near-infrared (400 to 1029nm).\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-10-18T19:25:42Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S3/OLCI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/S3_OLCI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/NRTI/L3_AER_AI","title":"Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### NRTI/L3_AER_AI\\n\\nThis dataset provides near real-time high-resolution imagery of the UV Aerosol Index\\n(UVAI), also called the Absorbing Aerosol Index (AAI).\\n\\nThe AAI is based on wavelength-dependent changes in Rayleigh scattering in\\nthe UV spectral range for a pair of wavelengths. The difference between\\nobserved and modelled reflectance results in the AAI. When the AAI is\\npositive, it indicates the presence of UV-absorbing aerosols like dust and\\nsmoke. It is useful for tracking the evolution of episodic aerosol plumes from\\ndust outbreaks, volcanic ash, and biomass burning.\\n\\nThe wavelengths used have very low ozone absorption, so unlike aerosol optical\\nthickness measurements, AAI can be calculated in the presence of clouds.\\nDaily global coverage is therefore possible.\\n\\nFor this L3 AER_AI product, the absorbing_aerosol_index is calculated with a\\npair of measurements at the 354 nm and 388 nm wavelengths.\\n\\n### NRTI L3 Product\\n\\nTo make our NRTI L3 products, we use [harpconvert](http://www.stcorp.nl/beat/documentation/harp/harpconvert.html)\\nto grid the data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'absorbing_aerosol_index_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(absorbing_aerosol_index,sensor_altitude,sensor_azimuth_angle,\\n sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)\'\\nS5P_NRTI_L2__AER_AI_20181113T080042_20181113T080542_05618_01_010200_20181113T083707.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T11:17:44Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_AER_AI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_AER_AI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/NRTI/L3_AER_LH","title":"Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### NRTI/L3_AER_LH\\n\\nThis dataset provides offline high-resolution imagery of the UV Aerosol Index\\n(UVAI), also called the Absorbing Layer Height (ALH).\\n\\nThe ALH is very sensitive to cloud contamination. However, aerosols and clouds can\\nbe difficult to distinguish, and ALH is computed for all FRESCO effective cloud\\nfractions smaller than 0.05. Cloud masks are available from FRESCO and VIIRS, and\\nare strongly recommended to filter for residual clouds. A sunglint mask is also\\navailable to screen sunglint regions, which are not filtered beforehand.\\n\\nIt is known that high surface albedos negatively influence the ALH, biasing the ALH\\ntowards the surface. In general, the ALH over (dark) oceans is considered reliable\\nto within the requirement of 1000 m or 100 hPa. Over land, especially bright surfaces,\\nthe accuracy may be lower, and the use of the ALH product over bright surfaces like\\ndeserts is not advisable.\\n\\nFor this L3 AER_LH product, the aerosol_mid_pressure is calculated with a\\npair of measurements at the 354 nm and 388 nm wavelengths. The\\n[COPERNICUS/S5P/OFFL/L3_SO2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2)\\nproduct has the absorbing_aerosol_index calculated using the 340 nm and 380\\nnm wavelengths.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'aerosol_height_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(aerosol_height,aerosol_pressure,aerosol_optical_depth,\\n sensor_zenith_angle,sensor_azimuth_angle,solar_azimuth_angle,solar_zenith_angle)\'\\nS5P_NRTI_L2__AER_LH_20191202T233055_20191202T233555_11074_01_010302_20191203T012120.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T11:17:44Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_AER_LH"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_AER_LH_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/NRTI/L3_CLOUD","title":"Sentinel-5P NRTI CLOUD: Near Real-Time Cloud","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### NRTI/L3_CLOUD\\n\\nThis dataset provides near real-time high-resolution imagery of cloud parameters.\\n\\nThe TROPOMI/S5P cloud properties retrieval is based on the OCRA and ROCINN\\nalgorithms currently being used in the operational GOME and GOME-2 products.\\nOCRA retrieves the cloud fraction using measurements in the UV/VIS spectral\\nregions and ROCINN retrieves the cloud height (pressure) and optical thickness\\n(albedo) using measurements in and around the oxygen A-band at 760 nm. Version 3.0\\nof the algorithms are used, which are based on a more realistic\\ntreatment of clouds as optically uniform layers of light-scattering particles.\\nAdditionally, the cloud parameters are also provided for a cloud model which\\nassumes the cloud to be a Lambertian reflecting boundary.\\n[[More information](http://www.tropomi.eu/data-products/cloud)]\\n\\n### NRTI L3 Product\\n\\nTo make our NRTI L3 products, we use [harpconvert](http://www.stcorp.nl/beat/documentation/harp/harpconvert.html)\\nto grid the data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'cloud_fraction>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(cloud_fraction,cloud_top_pressure,cloud_top_height,\\ncloud_base_pressure,cloud_base_height,cloud_optical_depth,surface_albedo,\\nsensor_azimuth_angle,sensor_zenith_angle,solar_azimuth_angle,\\nsolar_zenith_angle)\'\\nS5P_NRTI_L2__CLOUD__20190208T230503_20190208T231003_06860_01_010105_20190209T005255.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-05T23:24:16Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_CLOUD"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_CLOUD_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/NRTI/L3_CO","title":"Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### NRTI/L3_CO\\n\\nThis dataset provides near real-time high-resolution imagery of CO\\nconcentrations.\\n\\nCarbon monoxide (CO) is an important atmospheric trace gas for understanding\\ntropospheric chemistry. In certain urban areas, it is a major atmospheric\\npollutant. Main sources of CO are combustion of fossil fuels, biomass burning,\\nand atmospheric oxidation of methane and other hydrocarbons. Whereas fossil\\nfuel combustion is the main source of CO at northern mid-latitudes, the oxidation\\nof isoprene and biomass burning play an important role in the tropics. TROPOMI\\non the Sentinel 5 Precursor (S5P) satellite observes the CO global abundance\\nexploiting clear-sky and cloudy-sky Earth radiance measurements in the 2.3 µm\\nspectral range of the shortwave infrared (SWIR) part of the solar spectrum.\\nTROPOMI clear sky observations provide CO total columns with sensitivity to the\\ntropospheric boundary layer. For cloudy atmospheres, the column sensitivity\\nchanges according to the light path. [[More information](http://www.tropomi.eu/data-products/carbon-monoxide)]\\n\\n### NRTI L3 Product\\n\\nTo make our NRTI L3 products, we use [harpconvert](http://www.stcorp.nl/beat/documentation/harp/harpconvert.html)\\nto grid the data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'CO_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(CO_column_number_density,H2O_column_number_density,cloud_height,\\nsensor_altitude,sensor_azimuth_angle, sensor_zenith_angle,\\nsolar_azimuth_angle,solar_zenith_angle)\'\\nS5P_NRTI_L2__CO_____20181122T000018_20181122T000518_05741_01_010200_20181122T004844.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-11-22T12:00:13Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_CO"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_CO_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/NRTI/L3_HCHO","title":"Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### NRTI/L3_HCHO\\n\\nThis dataset provides near real-time high-resolution imagery of atmospheric\\nformaldehyde (HCHO) concentrations.\\n\\nFormaldehyde is an intermediate gas in almost all oxidation chains of non-methane\\nvolatile organic compounds (NMVOC), leading eventually to CO2 . Non-Methane Volatile\\nOrganic Compounds (NMVOCs) are, together with NOx , CO and CH4 , among the\\nmost important precursors of tropospheric O3 . The major HCHO source in the remote\\natmosphere is CH4 oxidation. Over the continents, the oxidation of higher NMVOCs\\nemitted from vegetation, fires, traffic and industrial sources results in important\\nand localized enhancements of the HCHO levels. The seasonal and inter-annual\\nvariations of the formaldehyde distribution are principally related to temperature\\nchanges and fire events, but also to changes in anthropogenic activities. HCHO concentrations\\nin the boundary layer can be directly related to the release of short-lived hydrocarbons, which\\nmostly cannot be observed directly from space. [[More information](http://www.tropomi.eu/data-products/formaldehyde)]\\n\\n### NRTI L3 Product\\n\\nTo make our NRTI L3 products, we use [harpconvert](http://www.stcorp.nl/beat/documentation/harp/harpconvert.html)\\nto grid the data.\\n\\nExample harpconvert invocation for one tile:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(tropospheric_HCHO_column_number_density,\\n tropospheric_HCHO_column_number_density_amf,\\n HCHO_slant_column_number_density,cloud_fraction,sensor_altitude,\\n sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,\\n solar_zenith_angle)\'\\nS5P_NRTI_L2__HCHO___20181017T181013_20181017T181513_05241_01_010102_20181017T185718.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-10-02T07:58:03Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_HCHO"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_HCHO_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/NRTI/L3_NO2","title":"Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### NRTI/L3_NO2\\n\\nThis dataset provides near real-time high-resolution imagery of NO2 concentrations.\\n\\nNitrogen oxides (NO2 and NO) are important trace gases in the Earth’s atmosphere,\\npresent in both the troposphere and the stratosphere. They enter the atmosphere\\nas a result of anthropogenic activities (notably fossil fuel combustion and\\nbiomass burning) and natural processes (wildfires, lightning, and microbiological processes in\\nsoils). Here, NO2 is used to represent concentrations\\nof collective nitrogen oxides because during daytime, i.e. in the presence of\\nsunlight, a photochemical cycle involving ozone (O3 ) converts NO into NO2 \\nand vice versa on a timescale of minutes. The TROPOMI NO2 processing system is based\\non the algorithm developments for the DOMINO-2 product and for the EU QA4ECV NO2 \\nreprocessed dataset for OMI, and has been adapted for TROPOMI. This\\nretrieval-assimilation-modelling system uses the 3-dimensional global TM5-MP\\nchemistry transport model at a resolution of 1x1 degree as an essential element.\\n[[More information](http://www.tropomi.eu/data-products/nitrogen-dioxide)]\\n\\n### NRTI L3 Product\\n\\nTo make our NRTI L3 products, we use [harpconvert](http://www.stcorp.nl/beat/documentation/harp/harpconvert.html)\\nto grid the data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'tropospheric_NO2_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(NO2_column_number_density,tropospheric_NO2_column_number_density,\\n stratospheric_NO2_column_number_density,NO2_slant_column_number_density,\\n tropopause_pressure,absorbing_aerosol_index,cloud_fraction,\\n sensor_altitude,sensor_azimuth_angle,\\n sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)\'\\nS5P_NRTI_L2__NO2____20181107T013042_20181107T013542_05529_01_010200_20181107T021824.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T10:05:44Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_NO2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_NO2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/NRTI/L3_O3","title":"Sentinel-5P NRTI O3: Near Real-Time Ozone","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### NRTI/L3_O3\\n\\nThis dataset provides near-real-time high-resolution imagery of total\\ncolumn ozone concentrations. See also `COPERNICUS/S5P/OFFL/L3_O3_TCL`\\nfor the tropospheric column data.\\n\\nIn the stratosphere, the ozone layer shields the biosphere from dangerous\\nsolar ultraviolet radiation. In the troposphere, it acts as an efficient\\ncleansing agent, but at high concentration it also becomes harmful to the\\nhealth of humans, animals, and vegetation. Ozone is also an important\\ngreenhouse-gas contributor to ongoing climate change. Since the discovery\\nof the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol\\nregulating the production of chlorine-containing ozone-depleting substances,\\nozone has been routinely monitored from the ground and from space.\\n\\nFor this product, there are two algorithms that deliver total ozone: GDP for\\nthe near real-time and GODFIT for the offline products. GDP is currently\\nbeing used for generating the operational total ozone products from GOME,\\nSCIAMACHY and GOME-2; while GODFIT is being used in the ESA CCI and the\\nCopernicus C3S projects.\\n[More information](http://www.tropomi.eu/data-products/total-ozone-column).\\n[Product user manual](https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Ozone-Total-Column)\\n\\n### NRTI L3 Product\\n\\nTo make our NRTI L3 products, we use [harpconvert](http://www.stcorp.nl/beat/documentation/harp/harpconvert.html)\\nto grid the data.\\n\\nThe qa value is adjusted before running harpconvert to satisfy all of the\\nfollowing criteria:\\n\\n* ozone_total_vertical_column in [0, 0.45]\\n* ozone_effective_temperature in [180, 260]\\n* fitted_root_mean_square > 0.01\\n\\nExample harpconvert invocation:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'O3_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(O3_column_number_density,O3_column_number_density_amf,\\nO3_slant_column_number_density,O3_effective_temperature,cloud_fraction,\\nsensor_azimuth_angle,sensor_zenith_angle,solar_azimuth_angle,\\nsolar_zenith_angle)\'\\nS5P_NRTI_L2__O3_____20180710T230038_20180710T230538_03840_01_010000_20180711T005227.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T11:02:44Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_O3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_O3_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/NRTI/L3_SO2","title":"Sentinel-5P NRTI SO2: Near Real-Time Sulphur Dioxide","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### NRTI/L3_SO2\\n\\nThis dataset provides near real-time high-resolution imagery of atmospheric\\nsulfur dioxide (SO2 ) concentrations.\\n\\nSulphur dioxide (SO2 ) enters the Earth’s atmosphere through both natural and\\nanthropogenic processes. It plays a role in chemistry on a local and global\\nscale and its impact ranges from short-term pollution to effects on climate.\\nOnly about 30% of the emitted SO2 comes from natural sources; the majority\\nis of anthropogenic origin. SO2 emissions adversely affect human health and\\nair quality. SO2 has an effect on climate through radiative forcing, via\\nthe formation of sulphate aerosols. Volcanic SO2 emissions can also pose a\\nthreat to aviation, along with volcanic ash. S5P/TROPOMI samples the Earth’s\\nsurface with a revisit time of one day with unprecedented spatial resolution\\nof 3.5 x 7 km which allows the resolution of fine details including the\\ndetection of much smaller SO2 plumes. [[More information](http://www.tropomi.eu/data-products/sulphur-dioxide)]\\n\\n### NRTI L3 Product\\n\\nTo make our NRTI L3 products, we use [harpconvert](http://www.stcorp.nl/beat/documentation/harp/harpconvert.html)\\nto grid the data.\\n\\nThe qa value is adjusted before running harpconvert to satisfy all of the\\nfollowing criteria:\\n\\n* snow_ice < 0.5\\n* sulfurdioxide_total_air_mass_factor_polluted > 0.1\\n* sulfurdioxide_total_vertical_column > -0.001\\n* qa_value > 0.5\\n* cloud_fraction_crb < 0.3\\n* solar_zenith_angle < 60\\n\\nThe 15km SO2 band is ingested only when solar_zenith_angle < 70.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'SO2_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(SO2_column_number_density,SO2_column_number_density_amf,\\n SO2_slant_column_number_density,cloud_fraction, sensor_altitude,\\n sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,\\n solar_zenith_angle)\'\\nS5P_NRTI_L2__SO2____20190129T101503_20190129T102003_06711_01_010105_20190129T111328.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T11:17:44Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_SO2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_SO2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_AER_AI","title":"Sentinel-5P OFFL AER AI: Offline UV Aerosol Index","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### OFFL/L3_AER_AI\\n\\nThis dataset provides offline high-resolution imagery of the UV Aerosol Index\\n(UVAI), also called the Absorbing Aerosol Index (AAI).\\n\\nThe AAI is based on wavelength-dependent changes in Rayleigh scattering in\\nthe UV spectral range for a pair of wavelengths. The difference between\\nobserved and modelled reflectance results in the AAI. When the AAI is\\npositive, it indicates the presence of UV-absorbing aerosols like dust and\\nsmoke. It is useful for tracking the evolution of episodic aerosol plumes from\\ndust outbreaks, volcanic ash, and biomass burning.\\n\\nThe wavelengths used have very low ozone absorption, so unlike aerosol optical\\nthickness measurements, AAI can be calculated in the presence of clouds.\\nDaily global coverage is therefore possible.\\n\\nFor this L3 AER_AI product, the absorbing_aerosol_index is calculated with a\\npair of measurements at the 354 nm and 388 nm wavelengths. The\\n[COPERNICUS/S5P/OFFL/L3_SO2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2)\\nproduct has the absorbing_aerosol_index calculated using the 340 nm and 380\\nnm wavelengths.\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find areas within the product\'s bounding\\nbox with data using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'absorbing_aerosol_index_validity>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'absorbing_aerosol_index_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(absorbing_aerosol_index,sensor_altitude,sensor_azimuth_angle,\\n sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)\'\\nS5P_OFFL_L2__AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-04T13:34:21Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_AER_AI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_AER_AI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_AER_LH","title":"Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### OFFL/L3_AER_LH\\n\\nThis dataset provides offline high-resolution imagery of the UV Aerosol Index\\n(UVAI), also called the Absorbing Layer Height (ALH).\\n\\nThe ALH is very sensitive to cloud contamination. However, aerosols and clouds can\\nbe difficult to distinguish, and ALH is computed for all FRESCO effective cloud\\nfractions smaller than 0.05. Cloud masks are available from FRESCO and VIIRS, and\\nare strongly recommended to filter for residual clouds. A sunglint mask is also\\navailable to screen sunglint regions, which are not filtered beforehand.\\n\\nIt is known that high surface albedos negatively influence the ALH, biasing the ALH\\ntowards the surface. In general, the ALH over (dark) oceans is considered reliable\\nto within the requirement of 1000 m or 100 hPa. Over land, especially bright surfaces,\\nthe accuracy may be lower, and the use of the ALH product over bright surfaces like\\ndeserts is not advisable.\\n\\nFor this L3 AER_LH product, the aerosol_mid_pressure is calculated with a\\npair of measurements at the 354 nm and 388 nm wavelengths. The\\n[COPERNICUS/S5P/OFFL/L3_SO2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2)\\nproduct has the absorbing_aerosol_index calculated using the 340 nm and 380\\nnm wavelengths.\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find areas within the product\'s bounding\\nbox with data using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'aerosol_height_validity>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__AER_LH_20190404T042423_20190404T060554_07630_01_010300_20190410T062552.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'aerosol_height_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(aerosol_height,aerosol_pressure,aerosol_optical_depth,\\n sensor_zenith_angle,sensor_azimuth_angle,solar_azimuth_angle,solar_zenith_angle)\'\\nS5P_OFFL_L2__AER_LH_20190404T042423_20190404T060554_07630_01_010300_20190410T062552.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-04T13:34:21Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_AER_LH"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_AER_LH_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_CH4","title":"Sentinel-5P OFFL CH4: Offline Methane","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### OFFL/L3_CH4\\n\\nThis dataset provides offline high-resolution imagery of methane\\nconcentrations.\\n\\nMethane (CH4 ) is, after carbon dioxide (CO2 ), the most important contributor\\nto the anthropogenically enhanced greenhouse effect. Roughly three-quarters\\nof methane emissions are anthropogenic and as such it is important to continue\\nthe record of satellite based measurements. TROPOMI aims at providing CH4 \\ncolumn concentrations with high sensitivity to the Earth’s surface, good\\nspatiotemporal coverage, and sufficient accuracy to facilitate inverse\\nmodeling of sources and sinks. TROPOMI uses absorption information from the\\nOxygen-A Band (760nm) and the SWIR spectral range to monitor CH4 abundances\\nin the Earth\'s atmosphere. [[More information](http://www.tropomi.eu/data-products/methane)]\\n\\nCurrently, the following data quality issues are known, are not covered by\\nthe quality flags, and should be kept in mind when looking at the methane\\nproduct and also at preliminary validation results. For more details, see\\nthe [MPC VDAF website](http://mpc-vdaf.tropomi.eu).\\n\\nFiltering on qa_value < 0.5 does not remove all pixels considered bad.\\nSome pixels with too low methane concentrations are still present:\\n\\n - Single TROPOMI overpasses show stripes of erroneous CH4 values in the\\n flight direction.\\n\\n - Not all pixels above inland water are filtered.\\n\\n - Uncertainties for the XCH4 is only based on the single sounding\\n precision due to measurement noise. For applications requiring an\\n overall uncertainty estimate, we propose to multiply the provided error\\n by a factor 2, which reflects the scatter of single sounding errors in\\n the TCCON validation.\\n\\n - The current data release only provides XCH4 over land. Glint ocean\\n observations will be added in the next data release.\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find which areas within the product\'s\\nbounding box contain data by using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'CH4_column_volume_mixing_ratio_dry_air_validity>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__CH4____20190223T202409_20190223T220540_07072_01_010202_20190301T221106.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'CH4_column_volume_mixing_ratio_dry_air_validity>50;\\nderive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(CH4_column_volume_mixing_ratio_dry_air, aerosol_height,\\naerosol_optical_depth, sensor_azimuth_angle, sensor_zenith_angle,\\nsolar_azimuth_angle, solar_zenith_angle)\'\\nS5P_OFFL_L2__CH4____20190223T202409_20190223T220540_07072_01_010202_20190301T221106.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-02-08T08:13:16Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CH4"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_CH4_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_CLOUD","title":"Sentinel-5P OFFL CLOUD: Near Real-Time Cloud","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n### OFFL/L3_CLOUD\\n\\nThis dataset provides offline high-resolution imagery of cloud parameters.\\n\\nThe TROPOMI/S5P cloud properties retrieval is based on the OCRA and ROCINN\\nalgorithms currently being used in the operational GOME and GOME-2 products.\\nOCRA retrieves the cloud fraction using measurements in the UV/VIS spectral\\nregions and ROCINN retrieves the cloud height (pressure) and optical thickness\\n(albedo) using measurements in and around the oxygen A-band at 760 nm. Version 3.0\\nof the algorithms are used, which are based on a more realistic\\ntreatment of clouds as optically uniform layers of light-scattering particles.\\nAdditionally, the cloud parameters are also provided for a cloud model which\\nassumes the cloud to be a Lambertian reflecting boundary.\\n[[More information](http://www.tropomi.eu/data-products/cloud)]\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find which areas within the product\'s\\nbounding box contain data by using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'cloud_fraction>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__CLOUD__20180705T095218_20180705T113348_03760_01_010000_20180712T082510.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'cloud_fraction>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(cloud_fraction,cloud_top_pressure,cloud_top_height,\\ncloud_base_pressure,cloud_base_height,cloud_optical_depth,surface_albedo,\\nsensor_azimuth_angle,sensor_zenith_angle,solar_azimuth_angle,\\nsolar_zenith_angle)\'\\nS5P_OFFL_L2__CLOUD__20180705T095218_20180705T113348_03760_01_010000_20180712T082510.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-04T11:34:21Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CLOUD"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_CLOUD_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_CO","title":"Sentinel-5P OFFL CO: Offline Carbon Monoxide","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n# OFFL/L3_CO\\n\\nThis dataset provides offline high-resolution imagery of CO concentrations.\\n\\nCarbon monoxide (CO) is an important atmospheric trace gas for understanding\\ntropospheric chemistry. In certain urban areas, it is a major atmospheric\\npollutant. Main sources of CO are combustion of fossil fuels, biomass burning,\\nand atmospheric oxidation of methane and other hydrocarbons. Whereas fossil\\nfuel combustion is the main source of CO at northern mid-latitudes, the oxidation\\nof isoprene and biomass burning play an important role in the tropics. TROPOMI\\non the Sentinel 5 Precursor (S5P) satellite observes the CO global abundance\\nexploiting clear-sky and cloudy-sky Earth radiance measurements in the 2.3 µm\\nspectral range of the shortwave infrared (SWIR) part of the solar spectrum.\\nTROPOMI clear sky observations provide CO total columns with sensitivity to the\\ntropospheric boundary layer. For cloudy atmospheres, the column sensitivity\\nchanges according to the light path. [[More information](http://www.tropomi.eu/data-products/carbon-monoxide)]\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find areas within the product\'s bounding\\nbox with data using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'CO_column_number_density_validity>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__CO_____20181031T060643_20181031T074813_05432_01_010200_20181106T052542.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'CO_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(CO_column_number_density,H2O_column_number_density,cloud_height,\\nsensor_altitude,sensor_azimuth_angle, sensor_zenith_angle,\\nsolar_azimuth_angle,solar_zenith_angle)\'\\nS5P_OFFL_L2__CO_____20181031T060643_20181031T074813_05432_01_010200_20181106T052542.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-06-28T10:24:07Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CO"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_CO_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_HCHO","title":"Sentinel-5P OFFL HCHO: Offline Formaldehyde","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n# OFFL/L3_HCHO\\n\\nThis dataset provides offline high-resolution imagery of atmospheric\\nformaldehyde (HCHO) concentrations.\\n\\nFormaldehyde is an intermediate gas in almost all oxidation chains of non-methane\\nvolatile organic compounds (NMVOC), leading eventually to CO2 . Non-Methane Volatile\\nOrganic Compounds (NMVOCs) are, together with NOx , CO and CH4 , among the\\nmost important precursors of tropospheric O3 . The major HCHO source in the remote\\natmosphere is CH4 oxidation. Over the continents, the oxidation of higher NMVOCs\\nemitted from vegetation, fires, traffic and industrial sources results in important\\nand localized enhancements of the HCHO levels. The seasonal and inter-annual\\nvariations of the formaldehyde distribution are principally related to temperature\\nchanges and fire events, but also to changes in anthropogenic activities. HCHO concentrations\\nin the boundary layer can be directly related to the release of short-lived hydrocarbons, which\\nmostly cannot be observed directly from space. [[More information](http://www.tropomi.eu/data-products/formaldehyde)]\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find areas within the product\'s bounding\\nbox with data using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__HCHO___20190116T171037_20190116T185207_06531_01_010105_20190123T104749.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nExample harpconvert invocation for one tile:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(tropospheric_HCHO_column_number_density,\\n tropospheric_HCHO_column_number_density_amf,\\n HCHO_slant_column_number_density,cloud_fraction,sensor_altitude,\\n sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,\\n solar_zenith_angle)\'\\nS5P_OFFL_L2__HCHO___20190116T171037_20190116T185207_06531_01_010105_20190123T104749.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-05T12:14:36Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_HCHO"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_NRTI_HCHO_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_NO2","title":"Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n# OFFL/L3_NO2\\n\\nThis dataset provides offline high-resolution imagery of NO2 concentrations.\\n\\nNitrogen oxides (NO2 and NO) are important trace gases in the Earth’s atmosphere,\\npresent in both the troposphere and the stratosphere. They enter the atmosphere\\nas a result of anthropogenic activities (notably fossil fuel combustion and\\nbiomass burning) and natural processes (wildfires, lightning, and microbiological processes in\\nsoils). Here, NO2 is used to represent concentrations\\nof collective nitrogen oxides because during daytime, i.e. in the presence of\\nsunlight, a photochemical cycle involving ozone (O3 ) converts NO into NO2 \\nand vice versa on a timescale of minutes. The TROPOMI NO2 processing system is based\\non the algorithm developments for the DOMINO-2 product and for the EU QA4ECV NO2 \\nreprocessed dataset for OMI, and has been adapted for TROPOMI. This\\nretrieval-assimilation-modelling system uses the 3-dimensional global TM5-MP\\nchemistry transport model at a resolution of 1x1 degree as an essential element.\\n[[More information](http://www.tropomi.eu/data-products/nitrogen-dioxide)]\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find areas within the product\'s bounding\\nbox with data using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'tropospheric_NO2_column_number_density_validity>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__NO2____20181010T074409_20181010T092539_05135_01_010100_20181016T092316.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'tropospheric_NO2_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(NO2_column_number_density,tropospheric_NO2_column_number_density,\\n stratospheric_NO2_column_number_density,NO2_slant_column_number_density,\\n tropopause_pressure,absorbing_aerosol_index,cloud_fraction,\\n sensor_altitude,sensor_azimuth_angle,\\n sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)\'\\nS5P_OFFL_L2__NO2____20181010T074409_20181010T092539_05135_01_010100_20181016T092316.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-06-28T10:24:07Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_NO2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_NO2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_O3","title":"Sentinel-5P OFFL O3: Offline Ozone","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n# OFFL/L3_O3\\n\\nThis dataset provides offline high-resolution imagery of total column ozone\\nconcentrations. See also `COPERNICUS/S5P/OFFL/L3_O3_TCL` for the tropospheric\\ncolumn data.\\n\\nIn the stratosphere, the ozone layer shields the biosphere from dangerous\\nsolar ultraviolet radiation. In the troposphere, it acts as an efficient\\ncleansing agent, but at high concentration it also becomes harmful to the\\nhealth of humans, animals, and vegetation. Ozone is also an important\\ngreenhouse-gas contributor to ongoing climate change. Since the discovery\\nof the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol\\nregulating the production of chlorine-containing ozone-depleting substances,\\nozone has been routinely monitored from the ground and from space.\\n\\nFor this product, there are two algorithms that deliver total ozone: GDP for\\nthe near real-time and GODFIT for the offline products. GDP is currently\\nbeing used for generating the operational total ozone products from GOME,\\nSCIAMACHY and GOME-2; while GODFIT is being used in the ESA CCI and the\\nCopernicus C3S projects.\\n[More information](http://www.tropomi.eu/data-products/total-ozone-column).\\n[Product user manual](https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Ozone-Total-Column)\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find areas within the product\'s bounding\\nbox with data using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'O3_column_number_density_validity>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__O3_____20181005T225147_20181006T003317_05073_01_010102_20181012T001415.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nThe qa value is adjusted before running harpconvert to satisfy all of the\\nfollowing criteria:\\n\\n* ozone_total_vertical_column in [0, 0.45]\\n* ozone_effective_temperature in [180, 260]\\n* ring_scale_factor in [0, 0.15]\\n* effective_albedo in [-0.5, 1.5]\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'O3_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(O3_column_number_density,O3_effective_temperature,cloud_fraction,\\nsensor_altitude,sensor_azimuth_angle, sensor_zenith_angle,\\nsolar_azimuth_angle,solar_zenith_angle)\'\\nS5P_OFFL_L2__O3_____20181005T225147_20181006T003317_05073_01_010102_20181012T001415.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-09-08T21:19:29Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_O3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_O3_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_O3_TCL","title":"Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n# OFFL/L3_O3_TCL\\n\\nThis dataset provides offline tropospheric high-resolution imagery of ozone\\nconcentrations between 20N and 20S. See also `COPERNICUS/S5P/OFFL/L3_O3` and\\n`COPERNICUS/S5P/NRTI/L3_O3` for the total column data.\\n\\nIn the stratosphere, the ozone layer shields the biosphere from dangerous\\nsolar ultraviolet radiation. In the troposphere, it acts as an efficient\\ncleansing agent, but at high concentration it also becomes harmful to the\\nhealth of humans, animals, and vegetation. Ozone is also an important\\ngreenhouse-gas contributor to ongoing climate change. Since the discovery\\nof the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol\\nregulating the production of chlorine-containing ozone-depleting substances,\\nozone has been routinely monitored from the ground and from space.\\n\\nFor this product, the convective cloud differential (ccd) and\\ncloud slicing (csa) algorithm were used.\\n[Product user manual](https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Ozone-Tropospheric-Column).\\n[More information](http://www.tropomi.eu/data-products/tropospheric-ozone-column).\\n\\nUnlike all other S5P products, this product was ingested directly without using\\n`harpconvert`, as it\'s already a gridded product. Pixels with qa_value<70\\nare masked out.\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-04-30T10:50:26Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_O3_TCL"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_O3_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"COPERNICUS/S5P/OFFL/L3_SO2","title":"Sentinel-5P OFFL SO2: Offline Sulphur Dioxide","description":"# Sentinel-5 Precursor\\n\\nSentinel-5 Precursor is a satellite launched on 13 October 2017\\nby the European Space Agency to monitor air pollution. The onboard sensor\\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\\n\\nAll of the S5P datasets, except CH4 , have two versions: Near Real-Time (NRTI) and Offline\\n(OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL\\nassets, but appear more quickly after acquisition. The OFFL assets contain data\\nfrom a single orbit (which, due to half the earth being dark, contains\\ndata only for a single hemisphere).\\n\\nBecause of noise on the data, negative vertical column values are often\\nobserved in particular over clean regions or for low SO2 emissions. It is\\nrecommended not to filter these values except for outliers, i.e.\\nfor vertical columns lower than -0.001 mol/m^2.\\n\\nThe original Sentinel 5P Level 2 (L2) data is binned\\nby time, not by latitude/longitude. To make it possible to ingest the data\\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\\na single grid per orbit (that is, no aggregation across products\\nis performed).\\n\\nSource products spanning the antimeridian are ingested as two Earth Engine\\nassets, with suffixes _1 and _2.\\n\\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\\ntool using the `bin_spatial` operation. The source data is filtered to\\nremove pixels with QA values less than:\\n\\n* 80% for AER_AI\\n* 75% for the tropospheric_NO2_column_number_density band of NO2\\n* 50% for all other datasets except for O3 and SO2\\n\\nThe O3_TCL product is ingested directly (without running harpconvert).\\n\\n\\n# OFFL/L3_SO2\\n\\nThis dataset provides offline high-resolution imagery of atmospheric sulfur\\ndioxide (SO2 ) concentrations.\\n\\nSulphur dioxide (SO2 ) enters the Earth’s atmosphere through both natural and\\nanthropogenic processes. It plays a role in chemistry on a local and global\\nscale and its impact ranges from short-term pollution to effects on climate.\\nOnly about 30% of the emitted SO2 comes from natural sources; the majority\\nis of anthropogenic origin. SO2 emissions adversely affect human health and\\nair quality. SO2 has an effect on climate through radiative forcing, via\\nthe formation of sulphate aerosols. Volcanic SO2 emissions can also pose a\\nthreat to aviation, along with volcanic ash. S5P/TROPOMI samples the Earth’s\\nsurface with a revisit time of one day with unprecedented spatial resolution\\nof 3.5 x 7 km which allows the resolution of fine details including the\\ndetection of much smaller SO2 plumes. [[More information](http://www.tropomi.eu/data-products/sulphur-dioxide)]\\n\\nFor this L3 SO2 product, the absorbing_aerosol_index is calculated with a\\npair of measurements at the 340 nm and 380 wavelengths. The\\n[COPERNICUS/S5P/OFFL/L3_AER_AI](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI)\\nproduct has the absorbing_aerosol_index calculated using the 354 nm and 388\\nnm wavelengths.\\n\\n### OFFL L3 Product\\n\\nTo make our OFFL L3 products, we find areas within the product\'s bounding\\nbox with data using a command like this:\\n\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'SO2_column_number_density_validity>50;derive(datetime_stop {time})\'\\nS5P_OFFL_L2__SO2____20181228T231131_20181229T005301_06265_01_010105_20190104T083244.nc\\ngrid_info.h5\\n```\\n\\nWe then merge all the data into one large mosaic (area-averaging values for\\npixels that may have different values for different times). From the\\nmosaic, we create a set of tiles containing orthorectified raster data.\\n\\nThe qa value is adjusted before running harpconvert to satisfy all of the\\nfollowing criteria:\\n\\n* snow_ice < 0.5\\n* sulfurdioxide_total_air_mass_factor_polluted > 0.1\\n* sulfurdioxide_total_vertical_column > -0.001\\n* qa_value > 0.5\\n* cloud_fraction_crb < 0.3\\n* solar_zenith_angle < 60\\n\\nThe 15km SO2 band is ingested only when solar_zenith_angle < 70.\\n\\nExample harpconvert invocation for one tile:\\n```\\nharpconvert --format hdf5 --hdf5-compression 9\\n-a \'SO2_column_number_density_validity>50;derive(datetime_stop {time});\\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\\nkeep(SO2_column_number_density,SO2_column_number_density_amf,\\n SO2_slant_column_number_density,absorbing_aerosol_index,cloud_fraction, sensor_altitude,\\n sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,\\n solar_zenith_angle)\'\\nS5P_OFFL_L2__SO2____20181228T231131_20181229T005301_06265_01_010105_20190104T083244.nc\\noutput.h5\\n```\\n","license":"proprietary","providers":[{"name":"European Union/ESA/Copernicus","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-05T11:53:01Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_SO2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/COPERNICUS_S5P_OFFL_SO2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CPOM/CryoSat2/ANTARCTICA_DEM","title":"CryoSat-2 Antarctica 1km DEM","description":"This dataset is a digital elevation model (DEM) of the\\nAntarctic ice sheet and ice shelves based on\\nobservations recorded by the CryoSat-2 satellite\\nradar altimeter between July 2010 and July 2016.\\n\\nThe DEM is formed from spatio-temporal fits to elevation\\nmeasurements accumulated within 1, 2, and 5 km grid cells,\\nand is posted at the modal resolution of 1 km. The median\\nand root mean square difference between the DEM and 2.3*10⁷\\nairborne laser altimeter measurements acquired during\\nNASA Operation IceBridge campaigns are -0.30 and 13.50 m,\\nrespectively.\\n\\nThe DEM uncertainty rises in regions of high slope,\\nespecially where elevation measurements were acquired in\\nlow-resolution mode; taking this into account, we estimate\\nthe average accuracy to be 9.5 m.\\n","license":"proprietary","providers":[{"name":"CPOM","roles":["producer","licensor"],"url":"http://www.cpom.ucl.ac.uk/csopr/icesheets2/dems.php?ais_subject=dem&user_type=normal"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CPOM_CryoSat2_ANTARCTICA_DEM"}],"extent":{"spatial":{"bbox":[[-180,-88,180,-60]]},"temporal":{"interval":[["2010-07-01T00:00:00Z","2016-07-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CPOM/CryoSat2/ANTARCTICA_DEM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CRYOSAT2_DEM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CPOM_CryoSat2_ANTARCTICA_DEM#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5194/tc-2017-223"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CSIRO/SLGA","title":"SLGA: Soil and Landscape Grid of Australia (Soil Attributes)","description":"The Soil and Landscape Grid of Australia (SLGA) is a comprehensive dataset\\nof soil attributes across Australia at 3 arc-second resolution (~90m\\npixels). The surfaces are the outcomes from modelling that describe the\\nspatial distribution of the soil attributes using existing soil data and\\nenvironmental covariates. See [Viscarra Rossel et al (2015)](http://www.publish.csiro.au/sr/SR14366)\\nfor further details. The SLGA can be used in studies of vadose zone\\nprocesses, including solute transport, groundwater and nutrient fluxes\\nbeyond the root zone, as well as a wide spectrum of ecological,\\nhydrological, and broader environmental applications.\\n\\nEach product contains six digital soil attribute maps and their upper and\\nlower confidence limits, representing the soil attribute at six depths:\\n0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, and 100-200cm. These depths and\\nsoil attributes are consistent with the specifications of\\n[GlobalSoilMap](https://www.isric.org/projects/globalsoilmapnet).\\n\\nThis collection has 12 images. Ten of them contain data for GSM primary\\nsoil attributes; the other two contain regolith depth and soil depth GSM\\nattributes.\\n\\n| Attribute | Description | Code | # of Bands |\\n|----------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|------|------------|\\n| Bulk Density (whole earth) | Bulk density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method | BDW | 18 |\\n| Organic Carbon | Mass fraction of carbon by weight in the < 2mm soil material as determined by dry combustion at 900 Celsius | SOC | 18 |\\n| Clay | < 2μm mass fraction of the <2mm soil material determined using the pipette method | CLY | 18 |\\n| Silt | 2-20μm mass fraction of the < 2mm soil material determined using the pipette method | SLT | 18 |\\n| Sand | 20μm - 2mm mass fraction of the < 2mm soil material determined using the pipette method | SND | 18 |\\n| pH (CaCl2) | pH of 1:5 soil/0.01M calcium chloride extract | pHc | 18 |\\n| Available Water Capacity | Available water capacity computed for each of the specified depth increments | AWC | 18 |\\n| Total Nitrogen | Mass fraction of total nitrogen in the soil by weight | NTO | 18 |\\n| Total Phosphorus | Mass fraction of total phosphorus in the soil by weight | PTO | 18 |\\n| Effective Cation Exchange Capacity | Cations extracted using barium chloride (BaCl2) plus exchangeable H + Al | ECE | 18 |\\n| Depth of Regolith | Depth to hard rock. Depth is inclusive of all regolith. | DER | 3 |\\n| Depth of Soil | Depth of soil profile (A & B horizons) | DES | 3 |\\n","license":"proprietary","providers":[{"name":"CSIRO/SLGA","roles":["producer","licensor"],"url":"http://www.clw.csiro.au/aclep/soilandlandscapegrid/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CSIRO_SLGA"}],"extent":{"spatial":{"bbox":[[113,-44.15,154,-9.97]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2013-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CSIRO/SLGA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/SLGA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CSIRO_SLGA#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546EE212B0048"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/547523BB0801A"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546EEE35164BF"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546F48D6A6D48"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546F29646877E"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546F17EC6AB6E"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546ED604ADD8A"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546F564AE11F9"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546F617719CAF"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546F091C11777"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/55C9472F05295"},{"rel":"cite-as","href":"https://doi.org/10.4225/08/546F540FE10AA"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CSP/ERGo/1_0/Global/ALOS_CHILI","title":"Global ALOS CHILI (Continuous Heat-Insolation Load Index)","description":"CHILI is a surrogate for effects of insolation and topographic shading on\\nevapotranspiration represented by calculating insolation at early afternoon,\\nsun altitude equivalent to equinox. 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It is based on the 30m \\"AVE\\" band of JAXA\'s ALOS\\nDEM (available in EE as JAXA/ALOS/AW3D30_V1_1).\\n","license":"proprietary","providers":[{"name":"Conservation Science Partners","roles":["producer","licensor"],"url":"http://www.csp-inc.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_ALOS_topoDiversity"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_topoDiversity"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CSP_ERGo_topoDiversity_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_ALOS_topoDiversity#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CSP/ERGo/1_0/Global/SRTM_CHILI","title":"Global SRTM CHILI (Continuous Heat-Insolation Load Index)","description":"CHILI is a surrogate for effects of insolation and topographic shading on\\nevapotranspiration represented by calculating insolation at early afternoon,\\nsun altitude equivalent to equinox. 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It was calculated using the Shannon diversity index at\\nmultiple-scales (km): 115.8, 89.9, 35.5, 13.1, 5.6, 2.8, and 1.2. It is\\nbased on the USGS\'s 10m NED DEM (available in EE as USGS/NED).\\n","license":"proprietary","providers":[{"name":"Conservation Science Partners","roles":["producer","licensor"],"url":"http://www.csp-inc.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_physioDiversity"}],"extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/physioDiversity"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CSP_ERGo_physioDiversity_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_physioDiversity#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CSP/ERGo/1_0/US/physiography","title":"US Physiography","description":"The Physiography dataset represents the spatial intersection of landforms\\n(available in EE as ERGo/1_0/US/landforms) and lithology (available in EE\\nas ERGo/1_0/US/lithology) data layers. It provides 247 unique combinations\\nout of a possible 270. The values for each type are formed by concatenating\\nthe landform and lithology types (e.g., 1101 is \\"Peak/ridge\\" landform on\\n\\"carbonate\\" lithology). This data layer is sometimes referred to as\\ncharacterizing \\"land facets\\".\\n\\nThe landforms layer is based on the USGS\'s 10m NED DEM (available in EE\\nas USGS/NED). The lithology layer is not basen on any DEM.\\n","license":"proprietary","providers":[{"name":"Conservation Science Partners","roles":["producer","licensor"],"url":"http://www.csp-inc.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_physiography"}],"extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/physiography"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CSP_ERGo_physiography_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_physiography#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CSP/ERGo/1_0/US/topoDiversity","title":"US NED Topographic Diversity","description":"Topographic diversity (D) is a surrogate variable that represents the\\nvariety of temperature and moisture conditions available to species as local\\nhabitats. It expresses the logic that a higher variety of topo-climate\\nniches should support higher diversity (especially plant) and support\\nspecies persistence given climatic change.\\n\\nTo calculate D, the multi-scale Topographic Position Index (mTPI), being a\\ndominant control of soil moisture (T), was used for measuring hillslope\\nposition. The mTPI was combined with the square-root transform for\\nmTPI>0 (T’) and with the standard deviation of the Continuous\\nHeat-Insolation Load Index (CHILI), calculated at multiple scales (C’) as:\\nD = 1 – ((1-T’) * (1-C’). It is based on the USGS\'s 10m NED DEM (available\\nin EE as USGS/NED).\\n","license":"proprietary","providers":[{"name":"Conservation Science Partners","roles":["producer","licensor"],"url":"http://www.csp-inc.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_topoDiversity"}],"extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/topoDiversity"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CSP_ERGo_topoDiversity_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_topoDiversity#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"CSP/HM/GlobalHumanModification","title":"CSP gHM: Global Human Modification","description":"The global Human Modification dataset (gHM) provides a cumulative measure of human modification\\nof terrestrial lands globally at 1 square-kilometer resolution. The gHM\\nvalues range from 0.0-1.0 and are calculated by estimating the proportion of a given location\\n(pixel) that is modified, the estimated intensity of modification\\nassociated with a given type of human modification or \\"stressor\\".\\n5 major anthropogenic stressors circa 2016 were mapped using 13\\nindividual datasets:\\n\\n* human settlement (population density, built-up areas)\\n* agriculture (cropland, livestock)\\n* transportation (major, minor, and two-track roads; railroads)\\n* mining and energy production\\n* electrical infrastructure (power lines, nighttime lights)\\n\\nPlease see the paper for additional methodological details.\\nThis asset was re-projected to WGS84 for use in Earth Engine.\\n","license":"proprietary","providers":[{"name":"Conservation Science Partners","roles":["producer","licensor"],"url":"http://www.csp-inc.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/CSP_HM_GlobalHumanModification"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-01-01T00:00:00Z","2016-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/CSP/HM/GlobalHumanModification"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CSP_HM_GlobalHumanModification_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_HM_GlobalHumanModification#terms-of-use"},{"rel":"source","href":"http://gdra-tnc.org/current/"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"ECMWF/CAMS/NRT","title":"Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time","description":"The Copernicus Atmosphere Monitoring Service provides the capacity to continuously monitor\\nthe composition of the Earth’s atmosphere at global and regional scales. The main global\\nnear-real-time production system is a data assimilation and forecasting suite providing two\\n5-day forecasts per day for aerosols and chemical compounds that are part of the chemical\\nscheme.\\n\\nCurrently only two parameters are available,\\n1. Total Aerosol Optical Depth at 550nm surface\\n2. Particulate matter d < 25 um surface\\nNote that system:time_start refers to forecast time.\\n","license":"proprietary","providers":[{"name":"European Centre for Medium-Range Weather Forecasts (ECMWF)","roles":["producer","licensor"],"url":"https://apps.ecmwf.int/datasets/data/cams-nrealtime"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-06-21T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/CAMS/NRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ECMWF_CAMS_NRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT#terms-of-use"},{"rel":"license","href":"https://ads.atmosphere.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf"},{"rel":"cite-as","href":"https://doi.org/10.1029/2008JD011235"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"ECMWF/ERA5/DAILY","title":"ERA5 Daily aggregates - Latest climate reanalysis produced by ECMWF / Copernicus Climate Change Service","description":"ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate.\\nReanalysis combines model data with observations from across the world into\\na globally complete and consistent dataset. ERA5 replaces its predecessor,\\nthe ERA-Interim reanalysis.\\n\\n\\nERA5 DAILY provides aggregated values for each day for seven ERA5 climate\\nreanalysis parameters: 2m air temperature, 2m dewpoint temperature, total\\nprecipitation, mean sea level pressure, surface pressure, 10m u-component\\nof wind and 10m v-component of wind. Additionally, daily minimum and maximum\\nair temperature at 2m has been calculated based on the hourly 2m air\\ntemperature data. Daily total precipitation values are given as daily sums.\\nAll other parameters are provided as daily averages.\\n\\nERA5 data is available from 1979 to three months from real-time. More information\\nand more ERA5 atmospheric parameters can be found at the\\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu).\\n","license":"proprietary","providers":[{"name":"ECMWF / Copernicus Climate Change Service","roles":["producer","licensor"],"url":"https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-02T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/DAILY"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ECMWF_ERA5_DAILY_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"ECMWF/ERA5_LAND/HOURLY","title":"ERA5-Land hourly - ECMWF climate reanalysis","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables\\nover several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by\\nreplaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\\ndata with observations from across the world into a globally complete and consistent dataset\\nusing the laws of physics. Reanalysis produces data that goes several decades back in time,\\nproviding an accurate description of the climate of the past. This dataset includes all 50\\nvariables as available on CDS.\\n\\n\\nPlease note that the convention for accumulations used in ERA5-Land differs with that for ERA5.\\nThe accumulations are treated the same as those in ERA-Interim or ERA-Interim/Land, i.e., they\\nare accumulated from the beginning of the forecast to the end of the forecast step. This happens\\nwithin every day and gets reset on midnight. The Earth Engine Data team added 19 additional bands,\\none for each of the accumulation bands, with the hourly values computed as the difference\\nbetween two consecutive forecast steps.\\n","license":"proprietary","providers":[{"name":"Climate Data Store","roles":["producer","licensor"],"url":"https://cds.climate.copernicus.eu/cdsapp"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T01:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/HOURLY"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ERA5_LAND_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"ECMWF/ERA5_LAND/MONTHLY","title":"ERA5-Land monthly averaged - ECMWF climate reanalysis","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables\\nover several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by\\nreplaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\\ndata with observations from across the world into a globally complete and consistent dataset\\nusing the laws of physics. Reanalysis produces data that goes several decades back in time,\\nproviding an accurate description of the climate of the past. This dataset includes all 50\\nvariables as available on CDS.\\n\\n\\nThe data presented here is a subset of the full ERA5-Land dataset post-processed by ECMWF.\\nMonthly-mean averages have been pre-calculated to facilitate many applications requiring easy and\\nfast access to the data, when sub-monthly fields are not required.\\n","license":"proprietary","providers":[{"name":"Climate Data Store","roles":["producer","licensor"],"url":"https://cds.climate.copernicus.eu/cdsapp"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ERA5_LAND_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"ECMWF/ERA5_LAND/MONTHLY_BY_HOUR","title":"ERA5-Land monthly averaged by hour of day - ECMWF climate reanalysis","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables\\nover several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by\\nreplaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\\ndata with observations from across the world into a globally complete and consistent dataset\\nusing the laws of physics. Reanalysis produces data that goes several decades back in time,\\nproviding an accurate description of the climate of the past. This dataset includes all 50\\nvariables as available on CDS.\\n\\n\\nThe data presented here is a subset of the full ERA5-Land dataset post-processed by ECMWF.\\nMonthly-mean averages have been pre-calculated to facilitate many applications requiring easy and\\nfast access to the data, when sub-monthly fields are not required.\\n\\nPlease note that the convention for accumulations used in ERA5-Land differs with that for ERA5.\\nThe accumulations are treated the same as those in ERA-Interim or ERA-Interim/Land, i.e., they\\nare accumulated from the beginning of the forecast to the end of the forecast step. This happens\\nwithin every day and gets reset on midnight. The Earth Engine Data team added 19 additional bands,\\none for each of the accumulation bands, with the hourly values computed as the difference\\nbetween two consecutive forecast steps.\\n","license":"proprietary","providers":[{"name":"Climate Data Store","roles":["producer","licensor"],"url":"https://cds.climate.copernicus.eu/cdsapp"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T01:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY_BY_HOUR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ERA5_LAND_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"ECMWF/ERA5/MONTHLY","title":"ERA5 Monthly aggregates - Latest climate reanalysis produced by ECMWF / Copernicus Climate Change Service","description":"ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate.\\nReanalysis combines model data with observations from across the world into\\na globally complete and consistent dataset. ERA5 replaces its predecessor,\\nthe ERA-Interim reanalysis.\\n\\n\\nERA5 MONTHLY provides aggregated values for each month for seven ERA5\\nclimate reanalysis parameters: 2m air temperature, 2m dewpoint temperature,\\ntotal precipitation,\\nmean sea level pressure, surface pressure, 10m u-component of wind and\\n10m v-component of wind. Additionally, monthly minimum and maximum air\\ntemperature at 2m has been calculated based on the hourly 2m air temperature\\ndata. Monthly total precipitation values are given as monthly sums.\\nAll other parameters are provided as monthly averages.\\n\\nERA5 data is available from 1979 to three months from real-time. More information\\nand more ERA5 atmospheric parameters can be found at the\\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu).\\n","license":"proprietary","providers":[{"name":"ECMWF / Copernicus Climate Change Service","roles":["producer","licensor"],"url":"https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/MONTHLY"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ECMWF_ERA5_DAILY_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"EO1/HYPERION","title":"EO-1 Hyperion Hyperspectral Imager","description":"Hyperion is a high resolution hyperspectral imager producing 220 unique\\nspectral channels ranging from 0.357 to 2.576 micrometers with a 10-nm\\nbandwidth. The instrument operates in a pushbroom fashion, with a spatial\\nresolution of 30 meters for all bands and a standard scene width of 7.7\\nkilometers.\\n\\nThis dataset contains level 1A radiance images, radiometrically calibrated\\nand orthorectified. The SWIR bands have a scaling factor of 80 and the VNIR\\nbands have a scaling factor of 40 applied.\\n\\n- VNIR bands (B008-B057, 426.82nm - 925.41nm): L = Digital Number / 40\\n\\n- SWIR bands (B077-B224, 912.45nm - 2395.50nm): L = Digital Number / 80\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://eo1.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/EO1_HYPERION"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-05-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/EO1/HYPERION"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/HYPERION_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/EO1_HYPERION#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"ESA/CCI/FireCCI/5_1","title":"FireCCI51: MODIS Fire_cci Burned Area Pixel product, version 5.1","description":"The MODIS Fire_cci Burned Area pixel product version 5.1 (FireCCI51)\\nis a monthly global ~250m spatial resolution dataset\\ncontaining information on burned area as well as ancillary data. It is based\\non surface reflectance in the Near Infrared (NIR) band from the MODIS\\ninstrument onboard the Terra satellite, as well as active fire information from\\nthe same sensor of the Terra and Aqua satellites.\\n\\nThe burned area algorithm uses a two-phase hybrid approach. In a first step\\npixels with a high probability of being burned (called \\"seeds\\") are detected\\nbased on the active fires. In a second one, a contextual growing is applied to\\ncompletely detect the fire patch. This growing phase is controlled by an\\nadaptive thresholding, where thresholds are computed based on the specific\\ncharacteristics of the area surrounding each seed. The variable used to guide\\nthe whole detection process is the NIR drop between pre- and post-fire images.\\n\\nThe dataset includes for each pixel the estimated day of the first detection of\\nthe fire, the confidence level of that detection, and the land cover that has\\nbeen burned (extracted from the ESA CCI Land Cover dataset v2.0.7). In addition,\\nan observation flag is provided to identify the pixels that were not processed due\\nto the lack of valid observations or because they belong to a non-burnable land\\ncover.\\n\\nFireCCI51 was developed as part of the ESA Climate Change Initiative (CCI)\\nProgramme, and it is also part of the Copernicus Climate Change Service (C3S).\\n","license":"proprietary","providers":[{"name":"European Space Agency (ESA) Climate Change Initiative (CCI) Programme, Fire ECV","roles":["producer","licensor"],"url":"https://climate.esa.int/en/projects/fire/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/ESA_CCI_FireCCI_5_1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2019-12-31T23:59:59Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/ESA/CCI/FireCCI/5_1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ESA_CCI_FireCCI_5_1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/ESA_CCI_FireCCI_5_1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5285/58f00d8814064b79a0c49662ad3af537"},{"rel":"cite-as","href":"https://doi.org/10.1016/j.rse.2019.111493"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"ESA/GLOBCOVER_L4_200901_200912_V2_3","title":"GlobCover: Global Land Cover Map","description":"GlobCover 2009 is a global land cover map based on ENVISAT\'s Medium\\nResolution Imaging Spectrometer (MERIS) Level 1B data acquired in full\\nresolution mode with a spatial resolution of approximately 300 meters.\\n","license":"proprietary","providers":[{"name":"ESA","roles":["producer","licensor"],"url":"http://dup.esrin.esa.int/page_globcover.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/ESA_GLOBCOVER_L4_200901_200912_V2_3"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2009-01-01T00:00:00Z","2010-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/ESA/GLOBCOVER_L4_200901_200912_V2_3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ESA_GLOBCOVER_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/ESA_GLOBCOVER_L4_200901_200912_V2_3#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"FAO/GHG/1/DROSA_A","title":"UN FAO Drained Organic Soils Area (Annual)","description":"The two related FAO datasets on Drained Organic Soils provide estimates of:\\n\\n1. DROSA-A, area of\\nOrganic Soils (in hectares) drained for agricultural activities (cropland and grazed grassland)\\n\\n2. DROSE-A, carbon (C) and nitrous oxide (N2O) estimates (in gigagrams) from the agricultural drainage of\\norganic soils under these land uses. Annual data are available at 0.0083333 X 0.0083333 resolution (~\\n1km at the equator), with global coverage for the period 1992 - 2018.\\n\\nFAOSTAT estimates follow the Intergovernmental Panel on Climate Change Guidelines\\n(IPCC) and use histosols as proxy for the presence of organic soils and annual land cover maps as time-\\ndependent component. Additionally, soils characteristics, land use, and climate information are applied\\nin the analysis. The carbon emissions can be converted to CO2, multiplying pixel values by the ratio of\\nthe molecular weight of carbon dioxide (CO2) to that of C (44/12).\\n\\nOrganic soils develop in wet soil ecosystems. They include tropical and boreal peatlands, high-latitude\\nbogs, ferns, and mires. Organic soils cover globally a mere 3 percent of the terrestrial land area but\\nrepresent up to 30 percent of the total soil carbon, thus playing an important role in maintaining the\\nearth’s carbon balance. Agriculture is a major cause of drainage of organic soils around the world.\\nDrainage exposes to aerobic conditions the organic matter of organic soils that oxidizes releasing large\\namounts of harmful greenhouse gases (GHG) to the atmosphere.\\n\\nDROSA-A and DROSE-A are the basis for country and regional statistics on drained organic soils\\ndisseminated in three FAOSTAT datasets (Cultivation of Organic Soils; Cropland; and Grassland).\\n","license":"proprietary","providers":[{"name":"FAO UN","roles":["producer","licensor"],"url":"http://fao.org/economic/ess/environment/data/organic-soils/la/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/FAO_GHG_1_DROSA_A"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1992-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/FAO/GHG/1/DROSA_A"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/FAO_GHG_1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_GHG_1_DROSA_A#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5194/essd"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"FAO/GHG/1/DROSE_A","title":"Drained Organic Soils Emissions (Annual)","description":"The two related FAO datasets on Drained Organic Soils provide estimates of:\\n\\n1. 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They include tropical and boreal peatlands, high-latitude\\nbogs, ferns, and mires. Organic soils cover globally a mere 3 percent of the terrestrial land area but\\nrepresent up to 30 percent of the total soil carbon, thus playing an important role in maintaining the\\nearth’s carbon balance. Agriculture is a major cause of drainage of organic soils around the world.\\nDrainage exposes to aerobic conditions the organic matter of organic soils that oxidizes releasing large\\namounts of harmful greenhouse gases (GHG) to the atmosphere.\\n\\nDROSA-A and DROSE-A are the basis for country and regional statistics on drained organic soils\\ndisseminated in three FAOSTAT datasets (Cultivation of Organic Soils; Cropland; and Grassland).\\n","license":"proprietary","providers":[{"name":"FAO UN","roles":["producer","licensor"],"url":"http://fao.org/economic/ess/environment/data/organic-soils/la/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/FAO_GHG_1_DROSE_A"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1992-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/FAO/GHG/1/DROSE_A"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/FAO_GHG_1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_GHG_1_DROSE_A#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5194/essd"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"FAO/WAPOR/2/L1_AETI_D","title":"WAPOR Actual Evapotranspiration and Interception","description":"The actual evapotranspiration and interception (ETIa) (dekadal, in mm/day) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). 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The pixel value represents the mean daily NPP for that specific dekad.","license":"proprietary","providers":[{"name":"FAO UN","roles":["producer","licensor"],"url":"https://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_NPP_D"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_NPP_D"}],"extent":{"spatial":{"bbox":[[-30.0044643,-40.0044644,65.0044644,40.0044643]]},"temporal":{"interval":[["2009-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/2/L1_NPP_D"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/FAO_WAPOR_2_L1_NPP_D_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_NPP_D#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"FAO/WAPOR/2/L1_RET_D","title":"WAPOR Dekadal Reference Evapotranspiration","description":"Reference evapotranspiration (RET) is defined as the evapotranspiration from a hypothetical reference crop and it simulates the behaviour of a well-watered grass surface. 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Each pixel represents the daily reference evapotranspiration in mm.","license":"proprietary","providers":[{"name":"FAO UN","roles":["producer","licensor"],"url":"https://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_RET_E"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_E"}],"extent":{"spatial":{"bbox":[[-30.15,-39.9953437,65.13,40.0044643]]},"temporal":{"interval":[["2009-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/2/L1_RET_E"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/FAO_WAPOR_2_L1_RET_E_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_E#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"FAO/WAPOR/2/L1_T_D","title":"WAPOR Dekadal Transpiration","description":"The transpiration (T) data component (dekadal, in mm/day) is the actual transpiration of the vegetation canopy. The value of each pixel represents the average daily actual transpiration for that specific dekad.","license":"proprietary","providers":[{"name":"FAO UN","roles":["producer","licensor"],"url":"https://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_T_D"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_T_D"}],"extent":{"spatial":{"bbox":[[-30.0044643,-40.0044644,65.0044644,40.0044643]]},"temporal":{"interval":[["2009-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/2/L1_T_D"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/FAO_WAPOR_2_L1_T_D_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_T_D#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"FIRMS","title":"FIRMS: Fire Information for Resource Management System","description":"The Earth Engine version of the Fire Information for Resource Management\\nSystem (FIRMS) dataset contains the LANCE fire detection product in\\nrasterized form. The near real-time (NRT) active fire locations are\\nprocessed by LANCE using the standard MODIS MOD14/MYD14 Fire and Thermal\\nAnomalies product. Each active fire location represents the centroid of a\\n1km pixel that is flagged by the algorithm as containing one or more fires\\nwithin the pixel. The data are rasterized as follows: for each FIRMS active\\nfire point, a 1km bounding box (BB) is defined; pixels in the MODIS\\nsinusoidal projection that intersect the FIRMS BB are identified; if\\nmultiple FIRMS BBs intersect the same pixel, the one with higher confidence\\nis retained; in case of a tie, the brighter one is retained.\\n\\nThe data in the near-real-time dataset are not considered to be of science\\nquality.\\n\\nAdditional information can be found [here](https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/about-firms).\\n","license":"proprietary","providers":[{"name":"NASA / LANCE / EOSDIS","roles":["producer","licensor"],"url":"https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/FIRMS"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-11-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/FIRMS"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/FIRMS_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/FIRMS#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"GFW/GFF/V1/fishing_hours","title":"GFW (Global Fishing Watch) Daily Fishing Hours","description":"Fishing effort, measured in hours of inferred fishing activity. 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Each asset\\nis the vessel presence for a given flag state and day, with one band for the\\npresence of each gear type.\\n","license":"proprietary","providers":[{"name":"Global Fishing Watch","roles":["producer","licensor"],"url":"http://globalfishingwatch.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/GFW_GFF_V1_vessel_hours"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/GFW/GFF/V1/vessel_hours"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GFW_GFF_V1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/GFW_GFF_V1_vessel_hours#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"GLCF/GLS_WATER","title":"GLCF: Landsat Global Inland Water","description":"The Global Inland Water dataset shows inland surface water bodies, including\\nfresh and saline lakes, rivers, and reservoirs.\\n\\nFrom the GLS 2000 epoch, 3,650,723 km2 of inland water were identified,\\naround three quarters of which were in North America and Asia. Boreal\\nforests and tundra hold the largest portion of inland water, about 40% of\\nthe global total. The data exhibits strong linear correlation with both the\\nMODIS dataset as well as 30-m resolution datasets over the United States\\nand Canada. Residual errors were due primarily to the seasonality of water\\ncover, snow and ice, and residual clouds.\\n\\nThe dataset contains one or more image for each available Landsat WRS2\\npath/row.\\n\\nDocumentation:\\n\\n* [User\'s guide](https://lpdaac.usgs.gov/documents/145/GFCC_User_Guide_V1.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MEASURES/GFCC/GFCC30WC.001"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/GLCF_GLS_WATER"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2000-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/GLCF/GLS_WATER"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GLCF_GLS_water_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/GLCF_GLS_WATER#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1080/17538947.2015.1026420"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"GRIDMET/DROUGHT","title":"GRIDMET DROUGHT: CONUS drought indices","description":"This dataset contains drought indices derived from the 4-km daily Gridded\\nSurface Meteorological (GRIDMET) dataset. The drought indices provided include\\nthe standardized precipitation index (SPI), the evaporative drought demand\\nindex (EDDI), the standardized precipitation evapotranspiration index (SPEI),\\nthe Palmer Drought Severity Index (PDSI) and Palmer Z Index (Z).\\n\\nSPI, EDDI, and SPEI are supplied on different time scales corresponding to the\\ntime aggregation of precipitation, reference evapotranspiration, and\\nprecipitation minus reference evapotranspiration, respectively. The time scales\\ninclude 14 day, 30 day, 90 day, 180 day, 270 day, 1 year, 2 years and 5 years.\\nThe standardization is done by using a non-parametric standardized probability\\nbased method where plotting positions are used to obtain probabilities which\\nare transformed to indices assuming an inverse-normal distribution. All data\\nare standardized over a common time period of 1981-2016.\\n\\nPDSI and Z are calculated using a modified version of the Palmer formula which\\nuses reference evapotranspiration and precipitation from GRIDMET, and a static\\nsoil water holding capacity layer (top 1500mm) from STATSGO. Modifications to\\nthe coefficients of the original Palmer formula are applied to calculate PDSI.\\nThe baseline period for PDSI and Z calculations is 1979-2018.\\n\\nInterpretation of the drought indices in this dataset is different for PDSI and\\nZ than for SPI, SPEI and EDDI. Utilizing the interpretation from the US Drought\\nmonitor, values of these drought indices have the following meaning:\\n\\nPDSI and z:\\n\\n * 5.0 or more (extremely wet)\\n * 4.0 to 4.99 (very wet)\\n * 3.0 to 3.99 (moderately wet),\\n * 2.0 to 2.99 (slightly wet)\\n * 1.0 to 1.99 (incipient wet spell)\\n * -0.99 to 0.99(near normal)\\n * -1.99 to -1.00 (incipient dry spell)\\n * -2.99 to -2.00 (mild drought)\\n * -3.99 to -3.00 (moderate drought)\\n * -4.99 to -4.00 (severe drought)\\n * -5.0 or less (extreme drought)\\n\\nSPI/SPEI/EDDI:\\n\\n * 2.0 or more (extremely wet)\\n * 1.6 to 1.99 (very wet)\\n * 1.3 to 1.59 (moderately wet),\\n * 0.8 to 1.29 (slightly wet)\\n * 0.5 to 0.79 (incipient wet spell)\\n * -0.49 to 0.49(near normal),\\n * -0.79 to -0.5 (incipient dry spell)\\n * -1.29 to -0.8 (mild drought)\\n * -1.59 to -1.3 (moderate drought)\\n * -1.99 to -1.6 (severe drought)\\n * -2.0 or less (extreme drought).\\n\\nThis dataset contains provisional products that are replaced with\\nupdated versions when the complete source data become available.\\nProducts can be distinguished by the value of the \'status\' property.\\nAt first, assets are ingested with status=\'early\'. After several\\ndays, they are replaced by assets with status=\'provisional\'.\\nAfter about 2 months, they are replaced by the final assets with\\nstatus=\'permanent\'.\\n","license":"proprietary","providers":[{"name":"University of California Merced","roles":["producer","licensor"],"url":"http://www.climatologylab.org/gridmet.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/GRIDMET_DROUGHT"}],"extent":{"spatial":{"bbox":[[-124.9,24.9,-66.8,49.6]]},"temporal":{"interval":[["1980-01-05T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/GRIDMET/DROUGHT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GRIDMET_DROUGHT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/GRIDMET_DROUGHT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"HYCOM/sea_surface_elevation","title":"HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation","description":"The Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid\\nisopycnal-sigma-pressure (generalized) coordinate ocean model. The subset\\nof HYCOM data hosted in EE contains the variables salinity, temperature,\\nvelocity, and elevation. They have been interpolated to a uniform\\n0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity,\\ntemperature, and velocity variables have been interpolated to 40\\nstandard z-levels.\\n","license":"proprietary","providers":[{"name":"NOPP","roles":["producer","licensor"],"url":"https://hycom.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_surface_elevation"}],"extent":{"spatial":{"bbox":[[-180,-80.48,180,80.48]]},"temporal":{"interval":[["1992-10-02T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/sea_surface_elevation"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/HYCOM_ELEVATION_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_surface_elevation#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"HYCOM/sea_temp_salinity","title":"HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity","description":"The Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid\\nisopycnal-sigma-pressure (generalized) coordinate ocean model. The subset\\nof HYCOM data hosted in EE contains the variables salinity, temperature,\\nvelocity, and elevation. They have been interpolated to a uniform\\n0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity,\\ntemperature, and velocity variables have been interpolated to 40\\nstandard z-levels.\\n","license":"proprietary","providers":[{"name":"NOPP","roles":["producer","licensor"],"url":"https://hycom.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_temp_salinity"}],"extent":{"spatial":{"bbox":[[-180,-80.48,180,80.48]]},"temporal":{"interval":[["1992-10-02T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/sea_temp_salinity"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/HYCOM_SST_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_temp_salinity#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"HYCOM/sea_water_velocity","title":"HYCOM: Hybrid Coordinate Ocean Model, Water Velocity","description":"The Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid\\nisopycnal-sigma-pressure (generalized) coordinate ocean model. The subset\\nof HYCOM data hosted in EE contains the variables salinity, temperature,\\nvelocity, and elevation. They have been interpolated to a uniform\\n0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity,\\ntemperature, and velocity variables have been interpolated to 40\\nstandard z-levels.\\n","license":"proprietary","providers":[{"name":"NOPP","roles":["producer","licensor"],"url":"https://hycom.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_water_velocity"}],"extent":{"spatial":{"bbox":[[-180,-80.48,180,80.48]]},"temporal":{"interval":[["1992-10-02T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/sea_water_velocity"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/HYCOM_VELOCITY_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_water_velocity#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"IDAHO_EPSCOR/GRIDMET","title":"GRIDMET: University of Idaho Gridded Surface Meteorological Dataset","description":"The Gridded Surface Meteorological dataset provides\\nhigh spatial resolution (~4-km) daily surface fields of temperature,\\nprecipitation, winds, humidity and radiation across the contiguous\\nUnited States from 1979. The dataset blends the high resolution\\nspatial data from PRISM with the high temporal resolution data\\nfrom the National Land Data Assimilation System (NLDAS) to produce\\nspatially and temporally continuous fields that lend themselves\\nto additional land surface modeling.\\n\\nThis dataset contains provisional products that are replaced with\\nupdated versions when the complete source data become available.\\nProducts can be distinguished by the value of the \'status\' property.\\nAt first, assets are ingested with status=\'early\'. After several\\ndays, they are replaced by assets with status=\'provisional\'.\\nAfter about 2 months, they are replaced by the final assets with\\nstatus=\'permanent\'.\\n","license":"proprietary","providers":[{"name":"University of California Merced","roles":["producer","licensor"],"url":"http://www.climatologylab.org/gridmet.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET"}],"extent":{"spatial":{"bbox":[[-124.9,24.9,-66.8,49.6]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/GRIDMET"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/IDAHO_EPSCOR_GRIDMET_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"IDAHO_EPSCOR/MACAv2_METDATA","title":"MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models","description":"The MACAv2-METDATA dataset is a collection of 20 global\\nclimate models covering the conterminous USA. The Multivariate Adaptive\\nConstructed Analogs (MACA) method is a statistical downscaling\\nmethod which utilizes a training dataset (i.e. a meteorological\\nobservation dataset) to remove historical biases and match spatial\\npatterns in climate model output.\\n\\nThe MACA method was used to downscale the model output from 20\\nglobal climate models (GCMs) of the Coupled Model Inter-Comparison\\nProject 5 (CMIP5) for the historical GCM forcings (1950-2005) and\\nthe future Representative Concentration Pathways (RCPs) RCP 4.5\\nand RCP 8.5 scenarios (2006-2100) from the native resolution of\\nthe GCMS to 4km.\\n","license":"proprietary","providers":[{"name":"University of California Merced","roles":["producer","licensor"],"url":"http://www.climatologylab.org/maca.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA"}],"extent":{"spatial":{"bbox":[[-124.9,24.9,-67,49.6]]},"temporal":{"interval":[["1900-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/MACAv2_METDATA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/IDAHO_EPSCOR_MACAv2_METDATA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY","title":"MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models","description":"The MACAv2-METDATA dataset is a collection of 20 global\\nclimate models covering the conterminous USA. The Multivariate Adaptive\\nConstructed Analogs (MACA) method is a statistical downscaling\\nmethod which utilizes a training dataset (i.e. a meteorological\\nobservation dataset) to remove historical biases and match spatial\\npatterns in climate model output.\\n\\nThe MACA method was used to downscale the model output from 20\\nglobal climate models (GCMs) of the Coupled Model Inter-Comparison\\nProject 5 (CMIP5) for the historical GCM forcings (1950-2005) and\\nthe future Representative Concentration Pathways (RCPs) RCP 4.5\\nand RCP 8.5 scenarios (2006-2100) from the native resolution of\\nthe GCMS to 4km.\\n\\n\\nThis version contains monthly summaries.","license":"proprietary","providers":[{"name":"University of California Merced","roles":["producer","licensor"],"url":"http://www.climatologylab.org/maca.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY"}],"extent":{"spatial":{"bbox":[[-124.9,24.9,-67,49.6]]},"temporal":{"interval":[["1900-01-01T00:00:00Z","2099-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"IDAHO_EPSCOR/TERRACLIMATE","title":"TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho","description":"TerraClimate is a dataset of monthly climate and climatic water balance for\\nglobal terrestrial surfaces. It uses climatically aided interpolation,\\ncombining high-spatial resolution climatological normals from the\\n[WorldClim dataset](http://worldclim.org/version2), with coarser spatial\\nresolution, but time-varying data from\\n[CRU Ts4.0](http://data.ceda.ac.uk//badc/cru/data/cru_ts/) and the\\n[Japanese 55-year Reanalysis (JRA55)](http://jra.kishou.go.jp/JRA-55/index_en.html).\\nConceptually, the procedure applies interpolated time-varying anomalies\\nfrom CRU Ts4.0/JRA55 to the high-spatial resolution climatology of\\nWorldClim to create a high-spatial resolution dataset that covers a broader\\ntemporal record.\\n\\nTemporal information is inherited from CRU Ts4.0 for most global land\\nsurfaces for temperature, precipitation, and vapor pressure. However,\\nJRA55 data is used for regions where CRU data had zero climate stations\\ncontributing (including all of Antarctica, and parts of Africa,\\nSouth America, and scattered islands). For primary climate variables of\\ntemperature, vapor pressure, and precipitation, the University of Idaho\\nprovides additional data on the number of stations (between 0 and 8) that\\ncontributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used\\nexclusively for solar radiation and wind speeds.\\n\\nTerraClimate additionally produces monthly surface water balance datasets\\nusing a water balance model that incorporates reference evapotranspiration,\\nprecipitation, temperature, and interpolated plant extractable soil water\\ncapacity. A modified Thornthwaite-Mather climatic water-balance model and\\nextractable soil water storage capacity data was used at a 0.5° grid from\\nWang-Erlandsson et al. (2016).\\n\\nData Limitations:\\n\\n 1. Long-term trends in data are inherited from parent datasets.\\n TerraClimate should not be used directly for independent assessments of\\n trends.\\n\\n 2. TerraClimate will not capture temporal variability at finer scales than\\n parent datasets and thus is not able to capture variability in\\n orographic precipitation ratios and inversions.\\n\\n 3. The water balance model is very simple and does not account for\\n heterogeneity in vegetation types or their physiological response to\\n changing environmental conditions.\\n\\n 4. Limited validation in data-sparse regions (e.g., Antarctica).\\n","license":"proprietary","providers":[{"name":"University of California Merced","roles":["producer","licensor"],"url":"http://www.climatologylab.org/terraclimate.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1958-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/TERRACLIMATE"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/IDAHO_EPSCOR_TERRACLIMATE_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JAXA/ALOS/AVNIR-2/ORI","title":"ALOS/AVNIR-2 ORI","description":"This dataset is contains orthorectified imagery from\\nthe Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2)\\nsensor on-board the Advanced Land Observing Satellite (ALOS) \\"DAICHI\\".\\n\\nThe AVNIR-2 ORI product was created from AVNIR-2 1B1 data after stereo\\nmatching with reference to ALOS\'s Panchromatic Remote-sensing Instrument\\nfor Stereo Mapping (PRISM)-derived DSM AW3D30. The orthorectification\\nprocess used AW3D30 DSM data when available and SRTM (The Shuttle\\nRadar Topography Mission) DSM data otherwise.\\n","license":"proprietary","providers":[{"name":"JAXA Earth Observation Research Center","roles":["producer","licensor"],"url":"http://www.eorc.jaxa.jp/ALOS/en/alos-ori/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AVNIR-2_ORI"}],"extent":{"spatial":{"bbox":[[127.05,23.81,154.41,46.04]]},"temporal":{"interval":[["2006-04-26T00:00:00Z","2011-04-18T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AVNIR-2/ORI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/AVNIR2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AVNIR-2_ORI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JAXA/ALOS/AW3D30/V2_2","title":"ALOS DSM: Global 30m","description":"ALOS World 3D - 30m (AW3D30) is a global digital surface model (DSM) dataset with a horizontal resolution of approximately 30 meters (1 arcsec mesh). The dataset is based on the DSM dataset (5-meter mesh version) of the [World 3D Topographic Data](http://www.aw3d.jp/en/). More details are available in the dataset [documentation](http://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v11_format_e.pdf).\\n\\nIn the version 2.2, released in April 2019 is an improved version of the\\nnorthern region over 60 degrees north. Along with the complement of no-data or\\nlow-quality area, updating of coastline was also performed.\\n","license":"proprietary","providers":[{"name":"JAXA Earth Observation Research Center","roles":["producer","licensor"],"url":"http://www.eorc.jaxa.jp/ALOS/en/aw3d30/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V2_2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-12T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V2_2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/AW3D30_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V2_2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JAXA/ALOS/PALSAR/YEARLY/FNF","title":"Global PALSAR-2/PALSAR Forest/Non-Forest Map","description":"The global forest/non-forest map (FNF) is generated by\\nclassifying the SAR image (backscattering coefficient) in the\\nglobal 25m resolution PALSAR-2/PALSAR SAR mosaic so that strong and\\nlow backscatter pixels are assigned as \\"forest\\" and \\"non-forest\\",\\nrespectively. Here, \\"forest\\" is defined as the natural forest\\nwith the area larger than 0.5 ha and forest cover over 10%. This\\ndefinition is the same as the Food and Agriculture Organization\\n(FAO) definition. Since the radar backscatter from the forest\\ndepends on the region (climate zone), the classification of\\nForest/Non-Forest is conducted by using a region-dependent\\nthreshold of backscatter. The classification accuracy is\\nchecked by using in-situ photos and high-resolution optical\\nsatellite images. Detailed information is available in the provider\'s\\n[Dataset Description](http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/DatasetDescription_PALSAR2_Mosaic_FNF_revE.pdf).\\n\\nAttention:\\n\\n* Backscatter values may vary significantly from path to path\\n over high latitude forest areas. This is due to the change of\\n backscattering intensity caused by freezing trees in winter.\\n Please note that this may affect the classification of\\n forest/non-forest.\\n","license":"proprietary","providers":[{"name":"JAXA EORC","roles":["producer","licensor"],"url":"http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_FNF"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/FNF"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/PALSAR_FNF_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_FNF#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JAXA/ALOS/PALSAR/YEARLY/SAR","title":"Global PALSAR-2/PALSAR Yearly Mosaic","description":"The global 25m PALSAR/PALSAR-2 mosaic is a seamless global\\nSAR image created by mosaicking strips of SAR imagery\\nfrom PALSAR/PALSAR-2. For each year and location,\\nthe strip data were selected through visual inspection of the\\nbrowse mosaics available over the period, with those showing minimum\\nresponse to surface moisture preferentially used. 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This is due to the change of\\n backscattering intensity caused by freezing trees in winter.\\n\\nMore information is available in the provider\'s\\n[Dataset Description](http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/DatasetDescription_PALSAR2_Mosaic_FNF_revH.pdf).\\n","license":"proprietary","providers":[{"name":"JAXA EORC","roles":["producer","licensor"],"url":"http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm"},{"name":"Google Earth 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version [JAXA/GCOM-C/L3/OCEAN/SST/V2](JAXA_GCOM-C_L3_OCEAN_SST_V2) is also available\\nfor this dataset which uses this [algorithm](https://suzaku.eorc.jaxa.jp/GCOM_C/data/product_std.html)\\nfor processing.\\n","license":"proprietary","providers":[{"name":"Global Change Observation Mission (GCOM)","roles":["producer","licensor"],"url":"http://suzaku.eorc.jaxa.jp/GCOM/index.html"},{"name":"Google Earth 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The products are marked with a metadata property called \'status\'. When a product is initially made available, the property value is \'provisional\'. Once a provisional product has been updated with the final version, this value is updated to \'permanent\'.\\nGSMaP for the reanalysis data (from 2000 to 2014) is also provided in the [GPM RNL collection][JAXA_GPM_L3_GSMaP_v6_reanalysis].","license":"proprietary","providers":[{"name":"JAXA Earth Observation Research Center","roles":["producer","licensor"],"url":"http://sharaku.eorc.jaxa.jp/GSMaP/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational"}],"extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["2014-03-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v6/operational"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GSMAP_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1029/2006GL027531."},{"rel":"cite-as","href":"https://doi.org/10.2151/sola"},{"rel":"cite-as","href":"https://doi.org/10.1029/2007JD009002."}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JAXA/GPM_L3/GSMaP/v6/reanalysis","title":"GSMaP Reanalysis: Global Satellite Mapping of Precipitation","description":"Global Satellite Mapping of Precipitation (GSMaP) provides a global hourly rain rate with a 0.1 x 0.1 degree resolution. GSMaP is a product of the Global Precipitation Measurement (GPM) mission, which provides global precipitation observations at three hour intervals. Values are estimated using multi-band passive microwave and infrared radiometers from the GPM Core Observatory satellite and with the assistance of a constellation of other satellites. GPM\'s precipitation rate retrieval algorithm is based on a radiative transfer model. The gauge-adjusted rate is calculated based on the optimization of the 24h accumulation of GSMaP hourly rain rate to daily precipitation by NOAA/CPC gauge measurement. This dataset is processed by GSMaP algorithm version 6 (product version 3). See [GSMaP Technical Documentation](http://www.eorc.jaxa.jp/GPM/doc/algorithm/GSMaPforGPM_20140902_E.pdf) for more details.\\n\\nThe operational GSMaP dataset (from 2014 to present) is also [available](JAXA_GPM_L3_GSMaP_v6_operational].","license":"proprietary","providers":[{"name":"JAXA Earth Observation Research Center","roles":["producer","licensor"],"url":"http://sharaku.eorc.jaxa.jp/GSMaP/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_reanalysis"}],"extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["2000-03-01T00:00:00Z","2014-03-12T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v6/reanalysis"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GSMAP_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_reanalysis#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1029/2006GL027531."},{"rel":"cite-as","href":"https://doi.org/10.2151/sola"},{"rel":"cite-as","href":"https://doi.org/10.1029/2007JD009002."}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1","title":"GHSL: Global Human Settlement Layers, Built-Up Grid 1975-1990-2000-2015 (P2016)","description":"The GHSL relies on the design and implementation of new spatial data mining\\ntechnologies allowing to automatically process and extract analytics and\\nknowledge from large amount of heterogeneous data including: global,\\nfine-scale satellite image data streams, census data, and crowd sources or\\nvolunteered geographic information sources.\\n\\n\\nThese data contain a multitemporal information layer on built-up presence as\\nderived from Landsat image collections (GLS1975, GLS1990, GLS2000, and\\nad-hoc Landsat 8 collection 2013/2014).\\n\\nThe data have been produced by means of [Global Human Settlement Layer\\nmethodology](http://publications.jrc.ec.europa.eu/repository/handle/JRC97705)\\nin 2015.\\n\\nFor more information visit: [http://ghsl.jrc.ec.europa.eu/ghs_bu.php](http://ghsl.jrc.ec.europa.eu/ghs_bu.php)\\nand [http://ghsl.jrc.ec.europa.eu/documents/GHSL_data_access.pdf](http://ghsl.jrc.ec.europa.eu/documents/GHSL_data_access.pdf).\\n","license":"proprietary","providers":[{"name":"EC JRC","roles":["producer","licensor"],"url":"http://ghsl.jrc.ec.europa.eu/index.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_BUILT_LDSMT_GLOBE_V1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2014-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GHSL_BUILT_multi_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_BUILT_LDSMT_GLOBE_V1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JRC/GHSL/P2016/POP_GPW_GLOBE_V1","title":"GHSL: Global Human Settlement Layers, Population Grid 1975-1990-2000-2015 (P2016)","description":"The GHSL relies on the design and implementation of new spatial data mining\\ntechnologies allowing to automatically process and extract analytics and\\nknowledge from large amount of heterogeneous data including: global,\\nfine-scale satellite image data streams, census data, and crowd sources or\\nvolunteered geographic information sources.\\n\\n\\nThis dataset depicts the distribution and density of population, expressed\\nas the number of people per cell, for reference epochs: 1975, 1990, 2000,\\n2015.\\n\\nResidential population estimates were provided by CIESIN GPW v4. These were\\ndisaggregated from census or administrative units to grid cells, informed by\\nthe distribution and density of built-up as mapped in the GHSL global layer\\nper corresponding epoch. (See [“Development of New Open and Free\\nMulti-temporal Global Population Grids at 250 m\\nResolution.”](http://publications.jrc.ec.europa.eu/repository/handle/JRC100523))\\n\\nThis dataset was produced in the World Mollweide projection (EPSG:54009).\\n\\nFor more information visit: [http://ghsl.jrc.ec.europa.eu/ghs_pop.php](http://ghsl.jrc.ec.europa.eu/ghs_pop.php).\\n","license":"proprietary","providers":[{"name":"EC JRC","roles":["producer","licensor"],"url":"http://ghsl.jrc.ec.europa.eu/index.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2015-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/POP_GPW_GLOBE_V1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GHSL_POP_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JRC/GHSL/P2016/SMOD_POP_GLOBE_V1","title":"GHSL: Global Human Settlement Layers, Settlement Grid 1975-1990-2000-2014 (P2016)","description":"The GHSL relies on the design and implementation of new spatial data mining\\ntechnologies allowing to automatically process and extract analytics and\\nknowledge from large amount of heterogeneous data including: global,\\nfine-scale satellite image data streams, census data, and crowd sources or\\nvolunteered geographic information sources.\\n\\n\\nThe GHS-SMOD is the rural-urban Settlement classification MODel adopted by\\nthe GHSL. It is the representation of the degree of urbanization\\n([DEGURBA](http://ec.europa.eu/eurostat/web/degree-of-urbanisation/background))\\nconcept into the GHSL data scenario. Each grid in the GHS-SMOD has been\\ngenerated by integrating the GHSL built-up areas and GHSL population grids\\ndata for reference epochs: 1975, 1990, 2000, 2015.\\n\\nThe DEGURBA classification schema is a people-based definition of cities and\\nsettlements: it operates using as main input a 1 km² grid cell accounting\\nfor population at a given point in time. The DEGURBA discriminates the\\npopulation grid cells in three main classes: \'[urban centers](http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Urban_centre)\'\\n(cities), \'[urban clusters](http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Urban_cluster)\'\\n(towns and suburbs), and \'[rural grid cells](http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Rural_grid_cell)\'.\\n(base). These class abstractions translate to \'high density clusters (HDC)\',\\n\'low density clusters (LDC)\', and \'rural grid cells (RUR)\', respectively,\\nin the GHS-SMOD implementation.\\n\\nThe \'HDC\' differ from the DEGURBA \'[urban centers](http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Urban_centre)\'\\nin that they account for the over-fragmentation of cities in regions\\nwith large low-density residential development by integrating the\\nbuilt-up layer. In the GHS-SMOD representation, the \'HDC\' are the spatial\\ngeneralization of contiguous population grid cells (4-connectivity,\\ngap-filling) with a density of at least 1500 inhabitants per km² or a\\ndensity of built-up surface > 50%, and a minimum total resident population\\nof 50000. The \'LDC\' are continuous grid cells with a density of at least 300\\ninhabitants per km² and a minimum total population of 5000. The \'RUR\' are\\ngrid cells outside \'HDC\' and \'LDC\' with population > 0 and < 300. Everything\\nelse is classified as inhabited areas where population = 0.\\n\\nThis dataset was produced in the World Mollweide projection (EPSG:54009).\\n\\nFor more information visit: [http://ghsl.jrc.ec.europa.eu/ghs_smod.php](http://ghsl.jrc.ec.europa.eu/ghs_smod.php).\\n","license":"proprietary","providers":[{"name":"EC JRC","roles":["producer","licensor"],"url":"http://ghsl.jrc.ec.europa.eu/index.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_SMOD_POP_GLOBE_V1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2015-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/SMOD_POP_GLOBE_V1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GHSL_SMOD_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_SMOD_POP_GLOBE_V1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JRC/GSW1_2/GlobalSurfaceWater","title":"JRC Global Surface Water Mapping Layers, v1.2","description":"This dataset contains maps of the location and temporal\\ndistribution of surface water from 1984 to 2019 and provides\\nstatistics on the extent and change of those water surfaces.\\nFor more information see the associated journal article: [High-resolution\\nmapping of global surface water and its long-term changes](http://www.nature.com/nature/journal/v540/n7633/full/nature20584.html)\\n(Nature, 2016) and the online [Data Users Guide](https://storage.googleapis.com/global-surface-water/downloads_ancillary/DataUsersGuidev2.pdf).\\n\\nThese data were generated using 4,185,439 scenes from Landsat\\n5, 7, and 8 acquired between 16 March 1984 and 31 December 2019.\\nEach pixel was individually classified into water / non-water\\nusing an expert system and the results were collated into a monthly\\nhistory for the entire time period and two epochs (1984-1999,\\n2000-2019) for change detection.\\n\\n\\nThis mapping layers product consists of 1 image containing 7 bands.\\nIt maps different facets of the spatial and temporal distribution of\\nsurface water over the last 35 years. Areas where water has\\nnever been detected are masked.\\n","license":"proprietary","providers":[{"name":"EC JRC / Google","roles":["producer","licensor"],"url":"http://global-surface-water.appspot.com"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_GlobalSurfaceWater"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_2/GlobalSurfaceWater"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/JRC_GlobalSurfaceWater_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_GlobalSurfaceWater#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JRC/GSW1_2/Metadata","title":"JRC Global Surface Water Metadata, v1.2","description":"This dataset contains maps of the location and temporal\\ndistribution of surface water from 1984 to 2019 and provides\\nstatistics on the extent and change of those water surfaces.\\nFor more information see the associated journal article: [High-resolution\\nmapping of global surface water and its long-term changes](http://www.nature.com/nature/journal/v540/n7633/full/nature20584.html)\\n(Nature, 2016) and the online [Data Users Guide](https://storage.googleapis.com/global-surface-water/downloads_ancillary/DataUsersGuidev2.pdf).\\n\\nThese data were generated using 4,185,439 scenes from Landsat\\n5, 7, and 8 acquired between 16 March 1984 and 31 December 2019.\\nEach pixel was individually classified into water / non-water\\nusing an expert system and the results were collated into a monthly\\nhistory for the entire time period and two epochs (1984-1999,\\n2000-2019) for change detection.\\n\\n\\nThis product contains metadata about the observations that went into\\ncomputing The Global Surface Water dataset. Areas where water has never\\nbeen detected are masked.\\n","license":"proprietary","providers":[{"name":"EC JRC / Google","roles":["producer","licensor"],"url":"http://global-surface-water.appspot.com"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_Metadata"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_2/Metadata"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/JRC_Metadata_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_Metadata#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JRC/GSW1_2/MonthlyHistory","title":"JRC Monthly Water History, v1.2","description":"This dataset contains maps of the location and temporal\\ndistribution of surface water from 1984 to 2019 and provides\\nstatistics on the extent and change of those water surfaces.\\nFor more information see the associated journal article: [High-resolution\\nmapping of global surface water and its long-term changes](http://www.nature.com/nature/journal/v540/n7633/full/nature20584.html)\\n(Nature, 2016) and the online [Data Users Guide](https://storage.googleapis.com/global-surface-water/downloads_ancillary/DataUsersGuidev2.pdf).\\n\\nThese data were generated using 4,185,439 scenes from Landsat\\n5, 7, and 8 acquired between 16 March 1984 and 31 December 2019.\\nEach pixel was individually classified into water / non-water\\nusing an expert system and the results were collated into a monthly\\nhistory for the entire time period and two epochs (1984-1999,\\n2000-2019) for change detection.\\n\\n\\nThis Monthly History collection holds the entire history of water detection\\non a month-by-month basis. The collection contains 430 images, one for each\\nmonth between March 1984 and December 2019.\\n","license":"proprietary","providers":[{"name":"EC JRC / Google","roles":["producer","licensor"],"url":"http://global-surface-water.appspot.com"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_MonthlyHistory"}],"extent":{"spatial":{"bbox":[[-180,-59,180,78]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_2/MonthlyHistory"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/JRC_MonthlyHistory_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_MonthlyHistory#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JRC/GSW1_2/MonthlyRecurrence","title":"JRC Monthly Water Recurrence, v1.2","description":"This dataset contains maps of the location and temporal\\ndistribution of surface water from 1984 to 2019 and provides\\nstatistics on the extent and change of those water surfaces.\\nFor more information see the associated journal article: [High-resolution\\nmapping of global surface water and its long-term changes](http://www.nature.com/nature/journal/v540/n7633/full/nature20584.html)\\n(Nature, 2016) and the online [Data Users Guide](https://storage.googleapis.com/global-surface-water/downloads_ancillary/DataUsersGuidev2.pdf).\\n\\nThese data were generated using 4,185,439 scenes from Landsat\\n5, 7, and 8 acquired between 16 March 1984 and 31 December 2019.\\nEach pixel was individually classified into water / non-water\\nusing an expert system and the results were collated into a monthly\\nhistory for the entire time period and two epochs (1984-1999,\\n2000-2019) for change detection.\\n\\n\\nThe Monthly Recurrence collection contains 12 images: monthly measures of\\nthe seasonality of water based on the occurrence values detected in that\\nmonth over all years.\\n","license":"proprietary","providers":[{"name":"EC JRC / Google","roles":["producer","licensor"],"url":"http://global-surface-water.appspot.com"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_MonthlyRecurrence"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_2/MonthlyRecurrence"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/JRC_MonthlyRecurrence_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_MonthlyRecurrence#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"JRC/GSW1_2/YearlyHistory","title":"JRC Yearly Water Classification History, v1.2","description":"This dataset contains maps of the location and temporal\\ndistribution of surface water from 1984 to 2019 and provides\\nstatistics on the extent and change of those water surfaces.\\nFor more information see the associated journal article: [High-resolution\\nmapping of global surface water and its long-term changes](http://www.nature.com/nature/journal/v540/n7633/full/nature20584.html)\\n(Nature, 2016) and the online [Data Users Guide](https://storage.googleapis.com/global-surface-water/downloads_ancillary/DataUsersGuidev2.pdf).\\n\\nThese data were generated using 4,185,439 scenes from Landsat\\n5, 7, and 8 acquired between 16 March 1984 and 31 December 2019.\\nEach pixel was individually classified into water / non-water\\nusing an expert system and the results were collated into a monthly\\nhistory for the entire time period and two epochs (1984-1999,\\n2000-2019) for change detection.\\n\\n\\nThis Yearly Seasonality Classification collection contains a year-by-year\\nclassification of the seasonality of water based on the occurrence values\\ndetected throughout the year.\\n","license":"proprietary","providers":[{"name":"EC JRC / Google","roles":["producer","licensor"],"url":"http://global-surface-water.appspot.com"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_YearlyHistory"}],"extent":{"spatial":{"bbox":[[-180,-59,180,78]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_2/YearlyHistory"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/JRC_YearlyHistory_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_YearlyHistory#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"KNTU/LiDARLab/IranLandCover/V1","title":"Iran Land Cover Map v1 13-class (2017)","description":"The Iran-wide land cover map was generated by processing Sentinel imagery\\nwithin the Google Earth Engine Cloud platform. For this purpose, over 2,500\\nSentinel-1 and over 11,000 Sentinel-2 images were processed to produce a single\\nmosaic dataset for the year 2017. Then, an object-based Random Forest\\nclassification method was trained by a large number of reference samples for 13\\nclasses to generate the Iran-wide land cover map.\\n","license":"proprietary","providers":[{"name":"K. N. Toosi University of Technology LiDAR Lab","roles":["producer","licensor"],"url":"https://en.kntu.ac.ir/laser-scanners-laboratory/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/KNTU_LiDARLab_IranLandCover_V1"}],"extent":{"spatial":{"bbox":[[43.46,24.62,65.58,39.95]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/KNTU/LiDARLab/IranLandCover/V1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/KNTU_LiDARLab_IranLandCover_V1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/KNTU_LiDARLab_IranLandCover_V1#terms-of-use"},{"rel":"license","href":"https://creativecommons.org/licenses/by/4.0/"},{"rel":"source","href":"ftp://94.184.181.78:1399/Iran_LC/Iran_LC.rar"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Fire/FRG/v1_2_0","title":"LANDFIRE FRG (Fire Regime Groups) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT).\\nThese data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.\\n\\n\\nFire Regime Groups (FRG) are created by linking the Biophysical Settings (BPS) Group attribute in the BpS layer with the Refresh Model Tracker (RMT) data and assigning the FRG attribute.\\nThis geospatial product should display a reasonable approximation of FRG, as documented in the RMT. (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM.)\\nFRG can be used in landscape assessments.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth 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programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT).\\nThese data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.\\n\\n\\nThe Mean Fire Return Interval (MFRI) layer quantifies the average period between fires under the presumed historical fire regime.\\nMFRI is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE (LF) Biophysical Settings (BPS) layer and BPS Model documentation.\\nMFRI is derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\\nThis layer is created by linking the BpS Group attribute in the BpS layer with the Refresh Model Tracker (RMT) data and assigning the MFRI attribute.\\nThis geospatial product should display a reasonable approximation of MFRI, as documented in the RMT.\\nMFRI is used in landscape assessments.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth 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programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT).\\nThese data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.\\n\\n\\nThe Percent of Low-severity Fire (PLS) layer quantifies the amount of low-severity fires relative to mixed- and replacement-severity fires under the presumed historical fire regime.\\nLow severity is defined as less than 25 percent average top-kill within a typical fire perimeter for a given vegetation type.\\nPLS was derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\\nThis layer is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE (LF) Biophysical Settings (BPS) layer and BPS Model documentation.\\nThis layer is created by linking the BPS Group attribute in the BPS layer with the Refresh Model Tracker (RMT) data and assigning the PLS attribute.\\nThis geospatial product should display a reasonable approximation of PLS, as documented in the RMT.\\nPLS is used in landscape assessments.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth 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programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT).\\nThese data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.\\n\\n\\nThe Percent of Mixed-severity Fire (PMS) layer quantifies the amount of mixed-severity fires relative to low- and replacement-severity fires under the presumed historical fire regime.\\nMixed severity is defined as between 25 and 75 percent average top-kill within a typical fire perimeter for a given vegetation type.\\nPMS was derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\\nThis layer is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE (LF) Biophysical Settings (BPS) layer and BPS Model documentation.\\nThis layer is created by linking the BPS Group attribute in the BPS layer with the Refresh Model Tracker (RMT) data and assigning the PMS attribute.\\nThis geospatial product should display a reasonable approximation of PMS, as documented in the RMT.\\nPMS is used in landscape assessments.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_PMS_v1_2_0"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PMS/v1_2_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Fire_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_PMS_v1_2_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Fire/PRS/v1_2_0","title":"LANDFIRE PRS (Percent of Replacement-severity Fire) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT).\\nThese data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.\\n\\n\\nThe Percent of Replacement-severity Fire (PRS) layer quantifies the amount of replacement-severity fires relative to low- and mixed-severity fires under the presumed historical fire regime.\\nReplacement severity is defined as greater than 75 percent average top-kill within a typical fire perimeter for a given vegetation type.\\nPRS was derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\\nThis layer is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE(LF) Biophysical Settings (BPS) layer and BPS Model documentation.\\nThis layer is created by linking the BPS Group attribute in the BPS layer with the Refresh Model Tracker (RMT) data and assigning the PRS attribute.\\nThis geospatial product should display a reasonable approximation of PRS, as documented in the RMT.\\nPRS is used in landscape assessments.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth 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programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT).\\nThese data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.\\n\\n\\nThe Succession Classes (SClass) layer characterizes current vegetation conditions with respect to the vegetation species composition, cover, and height ranges of successional states that occur within each biophysical setting.\\nSClass can also represent uncharacteristic vegetation components, such as exotic species, that are not found within the compositional or structural variability of successional states defined for a biophysical setting.\\nSuccession classes do not directly quantify fuel characteristics of the current vegetation, but rather represent vegetative states with unique succession or disturbance-related dynamics, such as structural development or fire frequency.\\nTo produce SClass, the historical reference conditions of these successional states were derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF_1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\\nThe area contained in succession classes is compared to the simulated historical reference conditions to calculate measurements of vegetation departure, such as fire regime condition class.\\nSClass is used in landscape assessments.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_SClass_v1_4_0"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/SClass/v1_4_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Fire_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_SClass_v1_4_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Fire/VCC/v1_4_0","title":"LANDFIRE VCC (Vegetation Condition Class) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT).\\nThese data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.\\n\\n\\nVegetation Condition Class (VCC) represents a simple categorization of the associated Vegetation Departure (VDEP) layer and indicates the general level to which current vegetation is different from the simulated historical vegetation reference conditions.\\nVDEP and VCC are based upon methods originally described in Interagency Fire Regime Condition Class Guidebook, but are not identical to those methods.\\nFull descriptions of the methods used can be found in the VDEP product description.\\nUsers should review those methods before comparing VDEP or VCC values across LANDFIRE (LF) versions.\\nVCC is a derivative of the VDEP layer.\\nIt is important to read and understand the characteristics of the VDEP spatial product, particularly if VCC values are compared across versions as the VDEP methods varied which directly impacts across-version VDEP and VCC comparability.\\nIn LF 2012, the original three VCC classes were divided in half to create six VCC classes to provide additional precision.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_VCC_v1_4_0"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/VCC/v1_4_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Fire_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_VCC_v1_4_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Fire/VDep/v1_4_0","title":"LANDFIRE VDep (Vegetation Departure) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions are mapped using the Vegetation Dynamics Development Tool (VDDT).\\nThese data support fire and landscape management planning goals in the National Cohesive Wildland Fire Management Strategy, the Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.\\n\\n\\nVegetation Departure (VDep) indicates how different current vegetation on a landscape is from estimated historical conditions.\\nVDep is based on changes to species composition, structural stage, and canopy closure using methods originally described in the Interagency Fire Regime Condition Class Guidebook, but is not identical to those methods.\\nLANDFIRE (LF) VDep is based only on departure of current vegetation conditions from reference vegetation conditions, whereas the Guidebook approach includes departure of current fire regimes from those of the reference period.\\nVDep is a landscape metric and is scale dependent. Every pixel in a unique biophysical settings (BpS) in a summary unit has the same VDep value.\\nThese large landscape values may not represent smaller areas within a summary unit.\\nThe VDep metric ranges from 0 - 100, and is based on four factors. These inputs are held constant within a single version of LF, but can be different across LF versions, which directly impacts VDep comparability across versions.\\nVDep can be compared across versions but caution is advised.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_VDep_v1_4_0"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/VDep/v1_4_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Fire_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_VDep_v1_4_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Vegetation/BPS/v1_4_0","title":"LANDFIRE BPS (Biophysical Settings) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.\\n\\n\\nLANDFIRE\'s (LF) Biophysical Settings (BPS) represents the vegetation that may have been dominant on the landscape prior to Euro-American settlement and is based on both the current biophysical environment and an approximation of the historical disturbance regime.\\nMap units are based on NatureServe\'s Ecological Systems classification and represent the natural plant communities that may have been present during the reference period. Each BPS map unit is matched with a model of vegetation succession, and both serve as key inputs to the LANDSUM landscape succession model.\\nThe actual time period for this data set is a composite of both the historical context provided by the fire regime and vegetation dynamics models and the more recent field and geospatial inputs used to create it.\\nLF\'s current BPS is unchanged from LF National\'s BPS except for updates made to water, barren, and snow classes (additions or removal), so that non-vegetated cover types within the BPS product matches LF existing vegetation and fuel products.\\nWith the final release of LF Remap for CONUS in mid to late 2020, the LF Remap BPS product will include the following as attributes: Mean Fire Return Interval (MFRI), Percent of Low-severity Fire (PLS), Percent of Mixed-severity Fire (PMS), Percent of Replacement-severity Fire (PRS), and Fire Regime Groups (FRG), so that the linkage of these characteristics to BPS is maintained.\\nLF uses BPS to depict reference conditions of vegetation across landscapes.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_BPS_v1_4_0"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/BPS/v1_4_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Vegetation_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_BPS_v1_4_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Vegetation/ESP/v1_2_0/AK","title":"LANDFIRE ESP AK (Environmental Site Potential) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.\\n\\n\\nLANDFIRE\'s (LF) Environmental Site Potential (ESP) represents the vegetation that could be supported at a given site based on the biophysical environment.\\nMap units are based on NatureServe\'s Ecological Systems classification and represent the natural plant communities that may have been present during the reference period.\\nESP map units represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance.\\nThey reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP concept is similar to that used in classifications of potential vegetation, including habitat types and plant associations.\\nIn early versions of LF, ESP was used to inform the existing vegetation and fuel mapping processes.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_AK"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/AK"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Vegetation_v1_4_0_AK_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_AK#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Vegetation/ESP/v1_2_0/CONUS","title":"LANDFIRE ESP CONUS (Environmental Site Potential) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.\\n\\n\\nLANDFIRE\'s (LF) Environmental Site Potential (ESP) represents the vegetation that could be supported at a given site based on the biophysical environment.\\nMap units are based on NatureServe\'s Ecological Systems classification and represent the natural plant communities that may have been present during the reference period.\\nESP map units represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance.\\nThey reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP concept is similar to that used in classifications of potential vegetation, including habitat types and plant associations.\\nIn early versions of LF, ESP was used to inform the existing vegetation and fuel mapping processes.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_CONUS"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/CONUS"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Vegetation_v1_4_0_CONUS_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_CONUS#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Vegetation/ESP/v1_2_0/HI","title":"LANDFIRE ESP HI (Environmental Site Potential) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.\\n\\n\\nLANDFIRE\'s (LF) Environmental Site Potential (ESP) represents the vegetation that could be supported at a given site based on the biophysical environment.\\nMap units are based on NatureServe\'s Ecological Systems classification and represent the natural plant communities that may have been present during the reference period.\\nESP map units represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance.\\nThey reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP concept is similar to that used in classifications of potential vegetation, including habitat types and plant associations.\\nIn early versions of LF, ESP was used to inform the existing vegetation and fuel mapping processes.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_HI"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/HI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Vegetation_v1_4_0_HI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_HI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Vegetation/EVC/v1_4_0","title":"LANDFIRE EVC (Existing Vegetation Cover) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.\\n\\n\\nLANDFIRE\'s (LF) Existing Vegetation Cover (EVC) represents the vertically projected percent cover of the live canopy layer for a 30-m cell.\\nEVC is generated separately for tree, shrub, and herbaceous cover lifeforms using training data and other geospatial layers. Percentage tree, shrub, and herbaceous canopy cover training data are generated using plot-level ground-based visual assessments and lidar observations.\\n* Once the training data are developed, relationships are then established separately for each lifeform between the training data and combination of Landsat and ancillary data. Each of the derived data layers (tree, shrub, herbaceous) has a potential range from 0-100 percent which are merged into a single composite EVC layer.\\n* Disturbance data were used to develop LF Remap products for LFRDB plot filtering and to ensure 2015 and 2016 disturbances were included that were not visible in the source imagery.\\n* The EVC product is then reconciled through QA/QC measures to ensure life-form is synchronized with both Existing Vegetation Height and Existing Vegetation Type products.\\nLF uses EVC in several subsequent layers, including the development of the fuel layers.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVC_v1_4_0"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVC/v1_4_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Vegetation_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVC_v1_4_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Vegetation/EVH/v1_4_0","title":"LANDFIRE EVH (Existing Vegetation Height) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.\\n\\n\\nExisting Vegetation Height (EVH) represents the average height of the dominant vegetation for a 30-m cell.\\nCanopy height is generated separately for tree, shrub, and herbaceous lifeforms using training data and other geospatial layers. EVH is determined by the average height weighted by species cover and based on the Existing Vegetation Type (EVT) lifeform.\\n\\n* Decision tree models using field reference data, lidar, Landsat, and ancillary data are developed separately for each lifeform. Decision tree relationships are used to generate lifeform specific height class layers, which are merged into a single composite EVH layer.\\n\\n* Disturbance data were used to develop LF Remap products for LFRDB plot filtering and to ensure 2015 and 2016 disturbances were included that were not visible in the source imagery.\\n\\n* The EVC product is then reconciled through QA/QC measures to ensure life-form is synchronized with both Existing Vegetation Cover and EVT products.\\n\\nLF uses EVH in several subsequent layers, including the development of the fuel products.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVH_v1_4_0"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVH/v1_4_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Vegetation_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVH_v1_4_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDFIRE/Vegetation/EVT/v1_4_0","title":"LANDFIRE EVT (Existing Vegetation Type) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture\'s Forest Service, U.S. Department of the Interior\'s Geological Survey, and The Nature Conservancy.\\n\\n\\nLANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.\\n\\n\\nLANDFIRE\'s (LF) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification, developed by NatureServe for the western hemisphere, through 2016. A terrestrial ecological system is defined as a group of plant community types (associations) that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients.\\n\\n*The LF Ecological Systems Descriptions for CONUS provides descriptions for each Ecological System including species, distribution and classification information.\\n\\nEVT also includes ruderal or semi-natural vegetation types within the U.S. National Vegetation Classification.\\nThe LF Ruderal NVC Groups Descriptions for CONUS provides descriptions for each ruderal NVC Group including species, distribution, and classification information.\\n\\nEVT is mapped using decision tree models, field data, Landsat imagery, elevation, and biophysical gradient data.\\n\\n* Decision tree models are developed separately for each of the three lifeforms—tree, shrub, and herbaceous and are then used to generate lifeform specific EVT layers.\\n\\n* Disturbance products are included in LF Remap products to describe areas on the landscape that have experienced change within the previous 10-year period.\\n\\n* The EVT product is reconciled through QA/QC measures to ensure life-form is synchronized with both Existing Vegetation Cover and Existing Vegetation Height.\\n","license":"proprietary","providers":[{"name":"U.S. Department of Agriculture\'s (USDA) U.S. Forest Service (USFS), U.S. Department of the Interior\'s Geological Survey (USGS), and The Nature Conservancy.","roles":["producer","licensor"],"url":"https://landfire.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVT_v1_4_0"}],"extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVT/v1_4_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDFIRE_Vegetation_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVT_v1_4_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/GLS1975","title":"Landsat Global Land Survey 1975 Mosaic","description":"The Global Land Survey (GLS) 1975 is a\\nglobal collection of imagery from the Landsat Multispectral\\nScanner (MSS). Most scenes were acquired by Landsat 1-3\\nin 1972-1983. A few gaps in the Landsat 1-3 data\\nhave been filled with scenes acquired by Landsat 4-5 during the\\nyears 1982-1987. These data contain 4 spectral bands:\\nGreen, Red, an NIR band, and a SWIR band. In the typical\\nFalse-color presentation, the images appear red because\\nthe NIR band, displayed as red, highlights vegetation.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/global-land-survey-gls"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS1975"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1972-01-01T00:00:00Z","1983-02-10T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS1975"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_GLS1975_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS1975#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/GLS1975_MOSAIC","title":"Landsat Global Land Survey 1975 Mosaic","description":"The Global Land Survey (GLS) 1975 is a\\n global collection of imagery from the Landsat Multispectral Scanner\\n (MSS). Most scenes were acquired by Landsat 1–3 during the\\n years from 1972–1983. A few gaps in the Landsat 1–3 data\\n have been filled with scenes acquired by Landsat 4–5 during the\\n years 1982–1987.
These data contain 4 spectral bands:\\n Green, Red, an NIR band, and a SWIR band. In the typical false-color\\n presentation the images appear red because the NIR band, displayed as\\n red, highlights vegetation.
All scenes\\n in the collection are included in this composite image.
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS1975_MOSAIC"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","1976-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS1975_MOSAIC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GLS1975_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS1975_MOSAIC#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/GLS2005","title":"Landsat Global Land Survey 2005, Landsat 5+7 scenes","description":"The GLS2005 data set is a collection of 9500\\northorectified leaf-on medium-resolution satellite images collected between\\n2004 and 2007 and covering the Earth\'s land masses. GLS2005 uses mainly Landsat\\n5 and gap-filled Landsat 7 data with EO-1 ALI and Terra ASTER data filling in\\nany data holes.
This dataset contains images from just the L5 TM and L7 ETM+\\nsensors, and only the 6 bands that those two sensors have in common: 10, 20,\\n30, 40, 50, and 70.
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-07-29T00:00:00Z","2008-07-29T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GLS1975_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/GLS2005_L5","title":"Landsat Global Land Survey 2005, Landsat 5 scenes","description":"The GLS2005 data set is a collection of 9500\\northorectified leaf-on medium-resolution satellite images collected between\\n2004 and 2007 and covering the Earth\'s land masses. GLS2005 uses mainly Landsat\\n5 and gap-filled Landsat 7 data with EO-1 ALI and Terra ASTER data filling in\\nany data holes.
This collection contains just the subset of the GLS2005 images\\n from the L5 ETM sensor.
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L5"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-08-14T00:00:00Z","2008-05-29T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005_L5"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GLS1975_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L5#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/GLS2005_L7","title":"Landsat Global Land Survey 2005, Landsat 7 scenes","description":"The GLS2005 data set is a collection of 9500\\northorectified leaf-on medium-resolution satellite images collected between\\n2004 and 2007 and covering the Earth\'s land masses. GLS2005 uses mainly Landsat\\n5 and gap-filled Landsat 7 data with EO-1 ALI and Terra ASTER data filling in\\nany data holes.
This collection contains just the subset of the GLS2005 images\\n from the L7 ETM+ sensor.
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L7"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-07-29T00:00:00Z","2008-07-29T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005_L7"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GLS1975_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L7#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1","title":"USGS Landsat 8 Collection 1 Tier 1 Raw Scenes","description":"Landsat 8 Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see \\nLandsat 8 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_32DAY_BAI","title":"Landsat 8 Collection 1 Tier 1 32-Day BAI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_32DAY_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_32DAY_EVI","title":"Landsat 8 Collection 1 Tier 1 32-Day EVI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_32DAY_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_32DAY_NBRT","title":"Landsat 8 Collection 1 Tier 1 32-Day NBRT Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_32DAY_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_32DAY_NDSI","title":"Landsat 8 Collection 1 Tier 1 32-Day NDSI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_32DAY_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_32DAY_NDVI","title":"Landsat 8 Collection 1 Tier 1 32-Day NDVI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_32DAY_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_32DAY_NDWI","title":"Landsat 8 Collection 1 Tier 1 32-Day NDWI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_32DAY_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_32DAY_RAW","title":"Landsat 8 Collection 1 Tier 1 32-Day Raw Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_32DAY_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_32DAY_TOA","title":"Landsat 8 Collection 1 Tier 1 32-Day TOA Reflectance Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_32DAY_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_32DAY_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_8DAY_BAI","title":"Landsat 8 Collection 1 Tier 1 8-Day BAI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_8DAY_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_8DAY_EVI","title":"Landsat 8 Collection 1 Tier 1 8-Day EVI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_8DAY_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_8DAY_NBRT","title":"Landsat 8 Collection 1 Tier 1 8-Day NBRT Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_8DAY_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_8DAY_NDSI","title":"Landsat 8 Collection 1 Tier 1 8-Day NDSI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_8DAY_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_8DAY_NDVI","title":"Landsat 8 Collection 1 Tier 1 8-Day NDVI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_8DAY_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_8DAY_RAW","title":"Landsat 8 Collection 1 Tier 1 8-Day Raw Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_8DAY_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_ANNUAL_BAI","title":"Landsat 8 Collection 1 Tier 1 Annual BAI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_ANNUAL_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_ANNUAL_EVI","title":"Landsat 8 Collection 1 Tier 1 Annual EVI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
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These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_ANNUAL_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_ANNUAL_NDSI","title":"Landsat 8 Collection 1 Tier 1 Annual NDSI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_ANNUAL_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_ANNUAL_NDVI","title":"Landsat 8 Collection 1 Tier 1 Annual NDVI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_ANNUAL_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_ANNUAL_NDWI","title":"Landsat 8 Collection 1 Tier 1 Annual NDWI Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_ANNUAL_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_ANNUAL_RAW","title":"Landsat 8 Collection 1 Tier 1 Annual Raw Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_ANNUAL_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_ANNUAL_TOA","title":"Landsat 8 Collection 1 Tier 1 Annual TOA Reflectance Composite","description":"These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_ANNUAL_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_ANNUAL_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_RT","title":"USGS Landsat 8 Collection 1 Tier 1 and Real-Time data Raw Scenes","description":"Landsat 8 Collection 1 Tier 1 and Real-Time data DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
\\nThe T1_RT collection contains both Tier 1 and Real-Time (RT) assets.\\nNewly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed\\nupon downlink but use predicted ephemeris, initial bumper mode parameters,\\nor initial TIRS line of sight model parameters. The data is placed in the\\nReal-Time tier and made available for immediate download. Once the data\\nhave been reprocessed with definitive ephemeris, updated bumper mode\\nparameters and refined TIRS parameters, the products are transitioned to\\neither Tier 1 or Tier 2 and removed from the Real-Time tier. The transition\\ndelay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.
For more information, see \\nLandsat 8 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_RT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_RT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_RT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_RT_TOA","title":"USGS Landsat 8 Collection 1 Tier 1 and Real-Time data TOA Reflectance","description":"Landsat 8 Collection 1 Tier 1 and Real-Time data\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_RT_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_RT_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_RT_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_SR","title":"USGS Landsat 8 Surface Reflectance Tier 1","description":"This dataset is the atmospherically corrected\\nsurface reflectance from the Landsat 8 OLI/TIRS sensors.\\nThese images contain 5 visible and near-infrared (VNIR) bands and\\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\\nreflectance, and two thermal infrared (TIR) bands processed to orthorectified\\nbrightness temperature\\n\\nThese data have been atmospherically corrected using\\n[LaSRC](https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1368_L8_C1-LandSurfaceReflectanceCode-LASRC_ProductGuide-v3.pdf)\\nand includes a cloud, shadow, water and snow mask produced using\\n[CFMASK](https://www.usgs.gov/core-science-systems/nli/landsat/cfmask-algorithm),\\nas well as a per-pixel saturation mask.\\n\\nStrips of collected data are packaged into overlapping \\"scenes\\" covering approximately\\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\\n\\nSee also [the USGS page on SR QA bands](https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-quality-assessment).\\n\\nSR can only be produced for Landsat assets processed to the\\n[L1TP level](https://www.usgs.gov/land-resources/nli/landsat/landsat-levels-processing)\\n\\nData provider notes:\\n\\n Although Surface Reflectance can be processed only from the Operational\\n Land Imager (OLI) bands, SR requires combined OLI/Thermal Infrared\\n Sensor (TIRS) product (LC8) input in order to generate the accompanying\\n cloud mask. Therefore, OLI only (LO8), and TIRS only (LT8) data products\\n cannot be calculated to SR. \\n SR is not run for a scene with a solar zenith angle greater than 76°. \\n Users are cautioned to avoid using SR for data acquired\\n over high latitudes (> 65°). \\n The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to\\n Surface Reflectance. \\n Efficacy of SR correction will be likely reduced in areas\\n where atmospheric correction is affected by adverse conditions:\\n \\n Hyper-arid or snow-covered regions \\n Low sun angle conditions \\n Coastal regions where land area is small relative to adjacent water \\n Areas with extensive cloud contamination \\n \\n \\n \\n\\nThis product is generated by Google using a Docker image supplied by USGS.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T1_TOA","title":"USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance","description":"Landsat 8 Collection 1 Tier 1\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T1_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T2","title":"USGS Landsat 8 Collection 1 Tier 2 Raw Scenes","description":"Landsat 8 Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see \\nLandsat 8 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T2_SR","title":"USGS Landsat 8 Surface Reflectance Tier 2","description":"This dataset is the atmospherically corrected\\nsurface reflectance from the Landsat 8 OLI/TIRS sensors.\\nThese images contain 5 visible and near-infrared (VNIR) bands and\\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\\nreflectance, and two thermal infrared (TIR) bands processed to orthorectified\\nbrightness temperature\\n\\nThese data have been atmospherically corrected using\\n[LaSRC](https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1368_L8_C1-LandSurfaceReflectanceCode-LASRC_ProductGuide-v3.pdf)\\nand includes a cloud, shadow, water and snow mask produced using\\n[CFMASK](https://www.usgs.gov/core-science-systems/nli/landsat/cfmask-algorithm),\\nas well as a per-pixel saturation mask.\\n\\nStrips of collected data are packaged into overlapping \\"scenes\\" covering approximately\\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\\n\\nSee also [the USGS page on SR QA bands](https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-quality-assessment).\\n\\nSR can only be produced for Landsat assets processed to the\\n[L1TP level](https://www.usgs.gov/land-resources/nli/landsat/landsat-levels-processing)\\n\\nData provider notes:\\n\\n Although Surface Reflectance can be processed only from the Operational\\n Land Imager (OLI) bands, SR requires combined OLI/Thermal Infrared\\n Sensor (TIRS) product (LC8) input in order to generate the accompanying\\n cloud mask. Therefore, OLI only (LO8), and TIRS only (LT8) data products\\n cannot be calculated to SR. \\n SR is not run for a scene with a solar zenith angle greater than 76°. \\n Users are cautioned to avoid using SR for data acquired\\n over high latitudes (> 65°). \\n The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to\\n Surface Reflectance. \\n Efficacy of SR correction will be likely reduced in areas\\n where atmospheric correction is affected by adverse conditions:\\n \\n Hyper-arid or snow-covered regions \\n Low sun angle conditions \\n Coastal regions where land area is small relative to adjacent water \\n Areas with extensive cloud contamination \\n \\n \\n \\n\\nThis product is generated by Google using a Docker image supplied by USGS.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T2_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T2_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T2_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LC08/C01/T2_TOA","title":"USGS Landsat 8 Collection 1 Tier 2 TOA Reflectance","description":"Landsat 8 Collection 1 Tier 2\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T2_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C01/T2_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T2_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1","title":"USGS Landsat 7 Collection 1 Tier 1 Raw Scenes","description":"Landsat 7 Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see \\nLandsat 7 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_32DAY_BAI","title":"Landsat 7 Collection 1 Tier 1 32-Day BAI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_32DAY_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_32DAY_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_32DAY_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_32DAY_EVI","title":"Landsat 7 Collection 1 Tier 1 32-Day EVI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_32DAY_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_32DAY_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_32DAY_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_32DAY_NBRT","title":"Landsat 7 Collection 1 Tier 1 32-Day NBRT Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_32DAY_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_32DAY_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_32DAY_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_32DAY_NDSI","title":"Landsat 7 Collection 1 Tier 1 32-Day NDSI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_8DAY_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_8DAY_EVI","title":"Landsat 7 Collection 1 Tier 1 8-Day EVI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_8DAY_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_8DAY_NBRT","title":"Landsat 7 Collection 1 Tier 1 8-Day NBRT Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_8DAY_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_8DAY_NDSI","title":"Landsat 7 Collection 1 Tier 1 8-Day NDSI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_8DAY_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_8DAY_NDVI","title":"Landsat 7 Collection 1 Tier 1 8-Day NDVI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_8DAY_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_8DAY_NDWI","title":"Landsat 7 Collection 1 Tier 1 8-Day NDWI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_8DAY_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_8DAY_RAW","title":"Landsat 7 Collection 1 Tier 1 8-Day Raw Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_8DAY_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_8DAY_TOA","title":"Landsat 7 Collection 1 Tier 1 8-Day TOA Reflectance Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_8DAY_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_8DAY_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_BAI","title":"Landsat 7 Collection 1 Tier 1 Annual BAI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_EVI","title":"Landsat 7 Collection 1 Tier 1 Annual EVI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_GREENEST_TOA","title":"Landsat 7 Collection 1 Tier 1 Landsat 7 Collection 1 Tier 1 Annual Greenest-Pixel TOA Reflectance Composite","description":"Landsat 7 Collection 1 Tier 1\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the greenest pixel as the composite value, where\\nthe greenest pixel means the pixel with the highest value of\\nthe Normalized Difference Vegetation Index (NDVI).","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_GREENEST_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_GREENEST_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":""},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_GREENEST_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_NBRT","title":"Landsat 7 Collection 1 Tier 1 Annual NBRT Composite","description":"
These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_NDSI","title":"Landsat 7 Collection 1 Tier 1 Annual NDSI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_NDVI","title":"Landsat 7 Collection 1 Tier 1 Annual NDVI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_NDWI","title":"Landsat 7 Collection 1 Tier 1 Annual NDWI Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_RAW","title":"Landsat 7 Collection 1 Tier 1 Annual Raw Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_ANNUAL_TOA","title":"Landsat 7 Collection 1 Tier 1 Annual TOA Reflectance Composite","description":"These Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_ANNUAL_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_ANNUAL_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_RT","title":"USGS Landsat 7 Collection 1 Tier 1 and Real-Time data Raw Scenes","description":"Landsat 7 Collection 1 Tier 1 and Real-Time data DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
\\nThe T1_RT collection contains both Tier 1 and Real-Time (RT) assets.\\nNewly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed\\nupon downlink but use predicted ephemeris, initial bumper mode parameters,\\nor initial TIRS line of sight model parameters. The data is placed in the\\nReal-Time tier and made available for immediate download. Once the data\\nhave been reprocessed with definitive ephemeris, updated bumper mode\\nparameters and refined TIRS parameters, the products are transitioned to\\neither Tier 1 or Tier 2 and removed from the Real-Time tier. The transition\\ndelay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.
For more information, see \\nLandsat 7 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_RT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_RT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_RT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_RT_TOA","title":"USGS Landsat 7 Collection 1 Tier 1 and Real-Time data TOA Reflectance","description":"Landsat 7 Collection 1 Tier 1 and Real-Time data\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_RT_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_RT_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_RT_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_SR","title":"USGS Landsat 7 Surface Reflectance Tier 1","description":"This dataset is the atmospherically\\ncorrected surface reflectance from the Landsat 7 ETM+ sensor.\\nThese images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave\\ninfrared (SWIR) bands processed to orthorectified surface reflectance,\\nand one thermal infrared (TIR) band processed to orthorectified brightness\\ntemperature. The VNIR and SWIR bands have a resolution of 30m / pixel.\\nThe TIR band, while originally collected with a resolution of 120m / pixel\\n(60m / pixel for Landsat 7) has been resampled using cubic convolution to 30m.\\n\\nThese data have been atmospherically corrected using\\n[LEDAPS](https://www.usgs.gov/media/files/landsat-4-7-surface-reflectance-code-ledaps-product-guide),\\nand include a cloud, shadow, water and snow mask produced using\\n[CFMASK](https://www.usgs.gov/land-resources/nli/landsat/cfmask-algorithm),\\nas well as a per-pixel saturation mask.\\n\\nStrips of collected data are packaged into overlapping \\"scenes\\" covering approximately\\n170km x 183km using a [standardized reference grid](https://www.usgs.gov/faqs/what-worldwide-reference-system-wrs).\\n\\nSee also [the USGS page on SR QA bands](https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-quality-assessment).\\n\\nSR can only be produced for Landsat assets processed to the\\n[L1TP level](https://www.usgs.gov/land-resources/nli/landsat/landsat-levels-processing)\\n\\nData provider notes:\\n\\n SR is not run for a scene with a solar zenith angle greater than 76°. \\n Users are cautioned to avoid using SR for data acquired\\n over high latitudes (> 65°). \\n The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to\\n Surface Reflectance. \\n Efficacy of SR correction will be likely reduced in areas\\n where atmospheric correction is affected by adverse conditions:\\n \\n Hyper-arid or snow-covered regions \\n Low sun angle conditions \\n Coastal regions where land area is small relative to adjacent water \\n Areas with extensive cloud contamination \\n \\n \\n \\n\\nThis product is generated by Google using a Docker image supplied by USGS.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T1_TOA","title":"USGS Landsat 7 Collection 1 Tier 1 TOA Reflectance","description":"Landsat 7 Collection 1 Tier 1\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T1_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T2","title":"USGS Landsat 7 Collection 1 Tier 2 Raw Scenes","description":"Landsat 7 Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see \\nLandsat 7 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T2_SR","title":"USGS Landsat 7 Surface Reflectance Tier 2","description":"This dataset is the atmospherically\\ncorrected surface reflectance from the Landsat 7 ETM+ sensor.\\nThese images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave\\ninfrared (SWIR) bands processed to orthorectified surface reflectance,\\nand one thermal infrared (TIR) band processed to orthorectified brightness\\ntemperature. The VNIR and SWIR bands have a resolution of 30m / pixel.\\nThe TIR band, while originally collected with a resolution of 120m / pixel\\n(60m / pixel for Landsat 7) has been resampled using cubic convolution to 30m.\\n\\nThese data have been atmospherically corrected using\\n[LEDAPS](https://www.usgs.gov/media/files/landsat-4-7-surface-reflectance-code-ledaps-product-guide),\\nand include a cloud, shadow, water and snow mask produced using\\n[CFMASK](https://www.usgs.gov/land-resources/nli/landsat/cfmask-algorithm),\\nas well as a per-pixel saturation mask.\\n\\nStrips of collected data are packaged into overlapping \\"scenes\\" covering approximately\\n170km x 183km using a [standardized reference grid](https://www.usgs.gov/faqs/what-worldwide-reference-system-wrs).\\n\\nSee also [the USGS page on SR QA bands](https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-quality-assessment).\\n\\nSR can only be produced for Landsat assets processed to the\\n[L1TP level](https://www.usgs.gov/land-resources/nli/landsat/landsat-levels-processing)\\n\\nData provider notes:\\n\\n SR is not run for a scene with a solar zenith angle greater than 76°. \\n Users are cautioned to avoid using SR for data acquired\\n over high latitudes (> 65°). \\n The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to\\n Surface Reflectance. \\n Efficacy of SR correction will be likely reduced in areas\\n where atmospheric correction is affected by adverse conditions:\\n \\n Hyper-arid or snow-covered regions \\n Low sun angle conditions \\n Coastal regions where land area is small relative to adjacent water \\n Areas with extensive cloud contamination \\n \\n \\n \\n\\nThis product is generated by Google using a Docker image supplied by USGS.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T2_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T2_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T2_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE07/C01/T2_TOA","title":"USGS Landsat 7 Collection 1 Tier 2 TOA Reflectance","description":"Landsat 7 Collection 1 Tier 2\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T2_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C01/T2_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T2_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE7_TOA_1YEAR","title":"Landsat 7 annual TOA percentile composites","description":"These 1-year composites were created\\n from all Landsat 7 images in the specified composite period, excluding\\n images marked with a negative sun elevation in their metadata. The\\n composites were created using the\\n \\n ee.Algorithms.Landsat.simpleComposite() method with its default settings.\\n Reflectance ([0,1]) in bands B1, B2, B3, B4, B5, and B7 is scaled to 8 bits\\n ([0,255]) and temperature in band B6_VCID_2 is converted to units of\\n Kelvin-100.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_1YEAR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z","2014-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE7_TOA_1YEAR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_1YEAR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE7_TOA_3YEAR","title":"Landsat 7 3-year TOA percentile composites","description":"These 3-year composites were created\\n from all Landsat 7 images in the specified composite period, excluding\\n images marked with a negative sun elevation in their metadata. The\\n composites were created using the\\n \\n ee.Algorithms.Landsat.simpleComposite() method with its default settings.\\n Reflectance ([0,1]) in bands B1, B2, B3, B4, B5, and B7 is scaled to 8 bits\\n ([0,255]) and temperature in band B6_VCID_2 is converted to units of\\n Kelvin-100.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_3YEAR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2014-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE7_TOA_3YEAR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_3YEAR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LE7_TOA_5YEAR","title":"Landsat 7 5-year TOA percentile composites","description":"These 5-year composites were created\\n from all Landsat 7 images in the specified composite period, excluding\\n images marked with a negative sun elevation in their metadata. The\\n composites were created using the\\n \\n ee.Algorithms.Landsat.simpleComposite() method with its default settings.\\n Reflectance ([0,1]) in bands B1, B2, B3, B4, B5, and B7 is scaled to 8 bits\\n ([0,255]) and temperature in band B6_VCID_2 is converted to units of\\n Kelvin-100.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_5YEAR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z","2012-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE7_TOA_5YEAR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_5YEAR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM01/C01/T1","title":"USGS Landsat 1 MSS Collection 1 Tier 1 Raw Scenes","description":"Landsat 1 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM01_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1972-07-23T00:00:00Z","1978-01-07T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM01/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM01_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM01/C01/T2","title":"USGS Landsat 1 MSS Collection 1 Tier 2 Raw Scenes","description":"Landsat 1 MSS Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM01_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1972-07-23T00:00:00Z","1978-01-07T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM01/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM01_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM02/C01/T1","title":"USGS Landsat 2 MSS Collection 1 Tier 1 Raw Scenes","description":"Landsat 2 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM02_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-22T00:00:00Z","1982-02-26T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM02/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM02_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM02/C01/T2","title":"USGS Landsat 2 MSS Collection 1 Tier 2 Raw Scenes","description":"Landsat 2 MSS Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM02_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-22T00:00:00Z","1982-02-26T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM02/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM02_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM03/C01/T1","title":"USGS Landsat 3 MSS Collection 1 Tier 1 Raw Scenes","description":"Landsat 3 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM03_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-03-05T00:00:00Z","1983-03-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM03/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM03_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM03/C01/T2","title":"USGS Landsat 3 MSS Collection 1 Tier 2 Raw Scenes","description":"Landsat 3 MSS Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM03_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-03-05T00:00:00Z","1983-03-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM03/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM03_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM04/C01/T1","title":"USGS Landsat 4 MSS Collection 1 Tier 1 Raw Scenes","description":"Landsat 4 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM04_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-07-16T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM04/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM04_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM04/C01/T2","title":"USGS Landsat 4 MSS Collection 1 Tier 2 Raw Scenes","description":"Landsat 4 MSS Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM04_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-07-16T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM04/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM04_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM05/C01/T1","title":"USGS Landsat 5 MSS Collection 1 Tier 1 Raw Scenes","description":"Landsat 5 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM05_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-01T00:00:00Z","2013-01-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM05/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM05_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LM05/C01/T2","title":"USGS Landsat 5 MSS Collection 1 Tier 2 Raw Scenes","description":"Landsat 5 MSS Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see Landsat\\nMSS Product Information Page
\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM05_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-01T00:00:00Z","2013-01-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM05/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM05_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LO08/C01/T1","title":"USGS Landsat 8 Collection 1 Tier 1 OLI Raw Scenes","description":"Landsat 8 Collection 1 Tier 1 OLI DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see \\nLandsat 8 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LO08_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LO08/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LO08_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LO08/C01/T1_RT","title":"USGS Landsat 8 Collection 1 Tier 1 and Real-Time data OLI Raw Scenes","description":"Landsat 8 Collection 1 Tier 1 and Real-Time data OLI DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
\\nThe T1_RT collection contains both Tier 1 and Real-Time (RT) assets.\\nNewly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed\\nupon downlink but use predicted ephemeris, initial bumper mode parameters,\\nor initial TIRS line of sight model parameters. The data is placed in the\\nReal-Time tier and made available for immediate download. Once the data\\nhave been reprocessed with definitive ephemeris, updated bumper mode\\nparameters and refined TIRS parameters, the products are transitioned to\\neither Tier 1 or Tier 2 and removed from the Real-Time tier. The transition\\ndelay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.
For more information, see \\nLandsat 8 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LO08_C01_T1_RT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LO08/C01/T1_RT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LO08_C01_T1_RT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LO08/C01/T2","title":"USGS Landsat 8 Collection 1 Tier 2 OLI Raw Scenes","description":"Landsat 8 Collection 1 Tier 2 OLI DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see \\nLandsat 8 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LO08_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LO08/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LO08_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1","title":"USGS Landsat 4 TM Collection 1 Tier 1 Raw Scenes","description":"Landsat 4 TM Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see \\nUSGS Landsat 5 Page
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_32DAY_BAI","title":"Landsat 4 TM Collection 1 Tier 1 32-Day BAI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_32DAY_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_32DAY_EVI","title":"Landsat 4 TM Collection 1 Tier 1 32-Day EVI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_32DAY_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_32DAY_NBRT","title":"Landsat 4 TM Collection 1 Tier 1 32-Day NBRT Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_32DAY_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_32DAY_NDSI","title":"Landsat 4 TM Collection 1 Tier 1 32-Day NDSI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_32DAY_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_32DAY_NDVI","title":"Landsat 4 TM Collection 1 Tier 1 32-Day NDVI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_32DAY_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_32DAY_NDWI","title":"Landsat 4 TM Collection 1 Tier 1 32-Day NDWI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_32DAY_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_32DAY_RAW","title":"Landsat 4 TM Collection 1 Tier 1 32-Day Raw Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_32DAY_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_32DAY_TOA","title":"Landsat 4 TM Collection 1 Tier 1 32-Day TOA Reflectance Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_32DAY_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_32DAY_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_8DAY_BAI","title":"Landsat 4 TM Collection 1 Tier 1 8-Day BAI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_8DAY_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_8DAY_EVI","title":"Landsat 4 TM Collection 1 Tier 1 8-Day EVI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_8DAY_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_8DAY_NBRT","title":"Landsat 4 TM Collection 1 Tier 1 8-Day NBRT Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_8DAY_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_8DAY_NDSI","title":"Landsat 4 TM Collection 1 Tier 1 8-Day NDSI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_8DAY_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_8DAY_NDVI","title":"Landsat 4 TM Collection 1 Tier 1 8-Day NDVI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_8DAY_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_8DAY_NDWI","title":"Landsat 4 TM Collection 1 Tier 1 8-Day NDWI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_8DAY_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_8DAY_RAW","title":"Landsat 4 TM Collection 1 Tier 1 8-Day Raw Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_8DAY_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_8DAY_TOA","title":"Landsat 4 TM Collection 1 Tier 1 8-Day TOA Reflectance Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_8DAY_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_8DAY_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_BAI","title":"Landsat 4 TM Collection 1 Tier 1 Annual BAI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_EVI","title":"Landsat 4 TM Collection 1 Tier 1 Annual EVI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_GREENEST_TOA","title":"Landsat 4 TM Collection 1 Tier 1 Landsat 4 TM Collection 1 Tier 1 Annual Greenest-Pixel TOA Reflectance Composite","description":"Landsat 4 TM Collection 1 Tier 1\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the greenest pixel as the composite value, where\\nthe greenest pixel means the pixel with the highest value of\\nthe Normalized Difference Vegetation Index (NDVI).","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_GREENEST_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_GREENEST_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":""},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_GREENEST_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_NBRT","title":"Landsat 4 TM Collection 1 Tier 1 Annual NBRT Composite","description":"
These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_NDSI","title":"Landsat 4 TM Collection 1 Tier 1 Annual NDSI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_NDVI","title":"Landsat 4 TM Collection 1 Tier 1 Annual NDVI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_NDWI","title":"Landsat 4 TM Collection 1 Tier 1 Annual NDWI Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_RAW","title":"Landsat 4 TM Collection 1 Tier 1 Annual Raw Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_ANNUAL_TOA","title":"Landsat 4 TM Collection 1 Tier 1 Annual TOA Reflectance Composite","description":"These Landsat 4 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_ANNUAL_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_ANNUAL_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_SR","title":"USGS Landsat 4 Surface Reflectance Tier 1","description":"This dataset is the atmospherically\\ncorrected surface reflectance from the Landsat 4 ETM sensor.\\nThese images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave\\ninfrared (SWIR) bands processed to orthorectified surface reflectance,\\nand one thermal infrared (TIR) band processed to orthorectified brightness\\ntemperature. The VNIR and SWIR bands have a resolution of 30m / pixel.\\nThe TIR band, while originally collected with a resolution of 120m / pixel\\n(60m / pixel for Landsat 7) has been resampled using cubic convolution to 30m.\\n\\nThese data have been atmospherically corrected using\\n[LEDAPS](https://www.usgs.gov/media/files/landsat-4-7-surface-reflectance-code-ledaps-product-guide),\\nand include a cloud, shadow, water and snow mask produced using\\n[CFMASK](https://www.usgs.gov/land-resources/nli/landsat/cfmask-algorithm),\\nas well as a per-pixel saturation mask.\\n\\nStrips of collected data are packaged into overlapping \\"scenes\\" covering approximately\\n170km x 183km using a [standardized reference grid](https://www.usgs.gov/faqs/what-worldwide-reference-system-wrs).\\n\\nSee also [the USGS page on SR QA bands](https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-quality-assessment).\\n\\nSR can only be produced for Landsat assets processed to the\\n[L1TP level](https://www.usgs.gov/land-resources/nli/landsat/landsat-levels-processing)\\n\\nData provider notes:\\n\\n SR is not run for a scene with a solar zenith angle greater than 76°. \\n Users are cautioned to avoid using SR for data acquired\\n over high latitudes (> 65°). \\n The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to\\n Surface Reflectance. \\n Efficacy of SR correction will be likely reduced in areas\\n where atmospheric correction is affected by adverse conditions:\\n \\n Hyper-arid or snow-covered regions \\n Low sun angle conditions \\n Coastal regions where land area is small relative to adjacent water \\n Areas with extensive cloud contamination \\n \\n \\n \\n\\nThis product is generated by Google using a Docker image supplied by USGS.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T1_TOA","title":"USGS Landsat 4 TM Collection 1 Tier 1 TOA Reflectance","description":"Landsat 4 TM Collection 1 Tier 1\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T1_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T1_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T2","title":"USGS Landsat 4 TM Collection 1 Tier 2 Raw Scenes","description":"Landsat 4 TM Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see \\nUSGS Landsat 5 Page
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T2_SR","title":"USGS Landsat 4 Surface Reflectance Tier 2","description":"This dataset is the atmospherically\\ncorrected surface reflectance from the Landsat 4 ETM sensor.\\nThese images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave\\ninfrared (SWIR) bands processed to orthorectified surface reflectance,\\nand one thermal infrared (TIR) band processed to orthorectified brightness\\ntemperature. The VNIR and SWIR bands have a resolution of 30m / pixel.\\nThe TIR band, while originally collected with a resolution of 120m / pixel\\n(60m / pixel for Landsat 7) has been resampled using cubic convolution to 30m.\\n\\nThese data have been atmospherically corrected using\\n[LEDAPS](https://www.usgs.gov/media/files/landsat-4-7-surface-reflectance-code-ledaps-product-guide),\\nand include a cloud, shadow, water and snow mask produced using\\n[CFMASK](https://www.usgs.gov/land-resources/nli/landsat/cfmask-algorithm),\\nas well as a per-pixel saturation mask.\\n\\nStrips of collected data are packaged into overlapping \\"scenes\\" covering approximately\\n170km x 183km using a [standardized reference grid](https://www.usgs.gov/faqs/what-worldwide-reference-system-wrs).\\n\\nSee also [the USGS page on SR QA bands](https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-quality-assessment).\\n\\nSR can only be produced for Landsat assets processed to the\\n[L1TP level](https://www.usgs.gov/land-resources/nli/landsat/landsat-levels-processing)\\n\\nData provider notes:\\n\\n SR is not run for a scene with a solar zenith angle greater than 76°. \\n Users are cautioned to avoid using SR for data acquired\\n over high latitudes (> 65°). \\n The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to\\n Surface Reflectance. \\n Efficacy of SR correction will be likely reduced in areas\\n where atmospheric correction is affected by adverse conditions:\\n \\n Hyper-arid or snow-covered regions \\n Low sun angle conditions \\n Coastal regions where land area is small relative to adjacent water \\n Areas with extensive cloud contamination \\n \\n \\n \\n\\nThis product is generated by Google using a Docker image supplied by USGS.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T2_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T2_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T2_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT04/C01/T2_TOA","title":"USGS Landsat 4 TM Collection 1 Tier 2 TOA Reflectance","description":"Landsat 4 TM Collection 1 Tier 2\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T2_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T00:00:00Z","1993-12-14T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C01/T2_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C01_T2_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1","title":"USGS Landsat 5 TM Collection 1 Tier 1 Raw Scenes","description":"Landsat 5 TM Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat scenes with the highest available data quality\\nare placed into Tier 1 and are considered suitable for time-series processing\\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\\nthat have well-characterized radiometry and are inter-calibrated across the\\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\\nconsistent and within prescribed tolerances [<=12 m root mean square error\\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\\ninter-calibrated (regardless of sensor) across the full collection. See more information\\nin the USGS docs. \\n
For more information, see \\nUSGS Landsat 5 Page
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_32DAY_BAI","title":"Landsat 5 TM Collection 1 Tier 1 32-Day BAI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_32DAY_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_32DAY_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_32DAY_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_32DAY_EVI","title":"Landsat 5 TM Collection 1 Tier 1 32-Day EVI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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These composites are created from\\nall the scenes in\\neach 32-day period beginning from the first day of the year and\\ncontinuing to the 352nd day of the year. The last composite of the\\nyear, beginning on day 353, will overlap the first composite of the\\nfollowing year by 20 days. All the images from each 32-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
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The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_8DAY_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_8DAY_NDVI","title":"Landsat 5 TM Collection 1 Tier 1 8-Day NDVI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_8DAY_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_8DAY_NDWI","title":"Landsat 5 TM Collection 1 Tier 1 8-Day NDWI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_8DAY_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_8DAY_RAW","title":"Landsat 5 TM Collection 1 Tier 1 8-Day Raw Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_8DAY_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_8DAY_TOA","title":"Landsat 5 TM Collection 1 Tier 1 8-Day TOA Reflectance Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in each\\n8-day period beginning from the first day of the year and continuing\\nto the 360th day of the year. The last composite of the year,\\nbeginning on day 361, will overlap the first composite of the\\nfollowing year by 3 days. All the images from each 8-day period are\\nincluded in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_8DAY_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_8DAY_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_BAI","title":"Landsat 5 TM Collection 1 Tier 1 Annual BAI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Burn Area Index (BAI) is generated from the Red and\\nNear-IR bands, and measures the spectral distance of each pixel from a\\nreference spectral point (the measured reflectance of charcoal). This\\nindex is intended to emphasize the charcoal signal in post-fire\\nimages. See\\nChuvieco\\net al. (2002) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_EVI","title":"Landsat 5 TM Collection 1 Tier 1 Annual EVI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_GREENEST_TOA","title":"Landsat 5 TM Collection 1 Tier 1 Landsat 5 TM Collection 1 Tier 1 Annual Greenest-Pixel TOA Reflectance Composite","description":"Landsat 5 TM Collection 1 Tier 1\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the greenest pixel as the composite value, where\\nthe greenest pixel means the pixel with the highest value of\\nthe Normalized Difference Vegetation Index (NDVI).","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_GREENEST_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_GREENEST_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":""},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_GREENEST_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_NBRT","title":"Landsat 5 TM Collection 1 Tier 1 Annual NBRT Composite","description":"
These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Burn Ratio Thermal (NBRT) index is\\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\\nhas a range from -1.0 to 1.0. See\\nHolden\\net al. (2005) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_NBRT"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_NBRT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NBRT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_NBRT#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_NDSI","title":"Landsat 5 TM Collection 1 Tier 1 Annual NDSI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_NDVI","title":"Landsat 5 TM Collection 1 Tier 1 Annual NDVI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_NDWI","title":"Landsat 5 TM Collection 1 Tier 1 Annual NDWI Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details.These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_RAW","title":"Landsat 5 TM Collection 1 Tier 1 Annual Raw Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the DN values, representing scaled, calibrated at-sensor radiance.
These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_RAW"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_RAW"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_RAW#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_ANNUAL_TOA","title":"Landsat 5 TM Collection 1 Tier 1 Annual TOA Reflectance Composite","description":"These Landsat 5 TM Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the \\n computed top-of-atmosphere (TOA) reflectance. See\\n \\n Chander et al. (2009) for details on the TOA computation.
These composites are created from\\nall the scenes in\\neach annual period beginning from the first day of the year and\\ncontinuing to the last day of the year. All the images from each year\\nare included in the composite, with the most recent pixel as the composite value.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_ANNUAL_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_ANNUAL_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_SR","title":"USGS Landsat 5 Surface Reflectance Tier 1","description":"This dataset is the atmospherically\\ncorrected surface reflectance from the Landsat 5 ETM sensor.\\nThese images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave\\ninfrared (SWIR) bands processed to orthorectified surface reflectance,\\nand one thermal infrared (TIR) band processed to orthorectified brightness\\ntemperature. The VNIR and SWIR bands have a resolution of 30m / pixel.\\nThe TIR band, while originally collected with a resolution of 120m / pixel\\n(60m / pixel for Landsat 7) has been resampled using cubic convolution to 30m.\\n\\nThese data have been atmospherically corrected using\\n[LEDAPS](https://www.usgs.gov/media/files/landsat-4-7-surface-reflectance-code-ledaps-product-guide),\\nand include a cloud, shadow, water and snow mask produced using\\n[CFMASK](https://www.usgs.gov/land-resources/nli/landsat/cfmask-algorithm),\\nas well as a per-pixel saturation mask.\\n\\nStrips of collected data are packaged into overlapping \\"scenes\\" covering approximately\\n170km x 183km using a [standardized reference grid](https://www.usgs.gov/faqs/what-worldwide-reference-system-wrs).\\n\\nSee also [the USGS page on SR QA bands](https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-quality-assessment).\\n\\nSR can only be produced for Landsat assets processed to the\\n[L1TP level](https://www.usgs.gov/land-resources/nli/landsat/landsat-levels-processing)\\n\\nData provider notes:\\n\\n SR is not run for a scene with a solar zenith angle greater than 76°. \\n Users are cautioned to avoid using SR for data acquired\\n over high latitudes (> 65°). \\n The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to\\n Surface Reflectance. \\n Efficacy of SR correction will be likely reduced in areas\\n where atmospheric correction is affected by adverse conditions:\\n \\n Hyper-arid or snow-covered regions \\n Low sun angle conditions \\n Coastal regions where land area is small relative to adjacent water \\n Areas with extensive cloud contamination \\n \\n \\n \\n\\nThis product is generated by Google using a Docker image supplied by USGS.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T1_TOA","title":"USGS Landsat 5 TM Collection 1 Tier 1 TOA Reflectance","description":"Landsat 5 TM Collection 1 Tier 1\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T1_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T2","title":"USGS Landsat 5 TM Collection 1 Tier 2 Raw Scenes","description":"Landsat 5 TM Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see \\nUSGS Landsat 5 Page
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T2_SR","title":"USGS Landsat 5 Surface Reflectance Tier 2","description":"This dataset is the atmospherically\\ncorrected surface reflectance from the Landsat 5 ETM sensor.\\nThese images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave\\ninfrared (SWIR) bands processed to orthorectified surface reflectance,\\nand one thermal infrared (TIR) band processed to orthorectified brightness\\ntemperature. The VNIR and SWIR bands have a resolution of 30m / pixel.\\nThe TIR band, while originally collected with a resolution of 120m / pixel\\n(60m / pixel for Landsat 7) has been resampled using cubic convolution to 30m.\\n\\nThese data have been atmospherically corrected using\\n[LEDAPS](https://www.usgs.gov/media/files/landsat-4-7-surface-reflectance-code-ledaps-product-guide),\\nand include a cloud, shadow, water and snow mask produced using\\n[CFMASK](https://www.usgs.gov/land-resources/nli/landsat/cfmask-algorithm),\\nas well as a per-pixel saturation mask.\\n\\nStrips of collected data are packaged into overlapping \\"scenes\\" covering approximately\\n170km x 183km using a [standardized reference grid](https://www.usgs.gov/faqs/what-worldwide-reference-system-wrs).\\n\\nSee also [the USGS page on SR QA bands](https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-quality-assessment).\\n\\nSR can only be produced for Landsat assets processed to the\\n[L1TP level](https://www.usgs.gov/land-resources/nli/landsat/landsat-levels-processing)\\n\\nData provider notes:\\n\\n SR is not run for a scene with a solar zenith angle greater than 76°. \\n Users are cautioned to avoid using SR for data acquired\\n over high latitudes (> 65°). \\n The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to\\n Surface Reflectance. \\n Efficacy of SR correction will be likely reduced in areas\\n where atmospheric correction is affected by adverse conditions:\\n \\n Hyper-arid or snow-covered regions \\n Low sun angle conditions \\n Coastal regions where land area is small relative to adjacent water \\n Areas with extensive cloud contamination \\n \\n \\n \\n\\nThis product is generated by Google using a Docker image supplied by USGS.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T2_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T2_SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T2_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT05/C01/T2_TOA","title":"USGS Landsat 5 TM Collection 1 Tier 2 TOA Reflectance","description":"Landsat 5 TM Collection 1 Tier 2\\n calibrated top-of-atmosphere (TOA) reflectance.\\n Calibration coefficients are extracted from the image metadata. See\\n \\n Chander et al. (2009) for details on the TOA computation.","license":"proprietary","providers":[{"name":"USGS/Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T2_TOA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2012-05-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C01/T2_TOA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T2_TOA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/LT08/C01/T2","title":"USGS Landsat 8 Collection 1 Tier 2 TIRS Raw Scenes","description":"Landsat 8 Collection 1 Tier 2 TIRS DN values, representing scaled, calibrated at-sensor radiance.
Scenes not meeting Tier 1 criteria during\\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\\nRMSE and other properties to determine the suitability for use in individual\\napplications and studies. See more information\\nin the USGS docs. \\n
For more information, see \\nLandsat 8 Handbook
","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://landsat.usgs.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT08_C01_T2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT08/C01/T2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_RAW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT08_C01_T2#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"LANDSAT/MANGROVE_FORESTS","title":"Global Mangrove Forests Distribution, v1 (2000)","description":"The database was prepared using Landsat satellite data from the year 2000.\\nMore than 1,000 Landsat scenes obtained from the USGS Earth Resources\\nObservation and Science Center (EROS) were classified using hybrid\\nsupervised and unsupervised digital image classification techniques. This\\ndatabase is the first, most comprehensive mangrove assessment of the world\\n([Giri et al., 2011](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1466-8238.2010.00584.x)).\\nPartial funding of this research was provided by NASA.\\n\\nThe mangrove database is being used for identifying priority areas\\nfor mangrove conservation, studying the role of mangrove forests in\\nsaving lives and properties from natural disasters (e.g. tsunami),\\ncarbon accounting, and biodiversity conservation. The USGS EROS has\\nbeen using the data to study the impact of sea level rise on mangrove\\necosystems. The database serves as a baseline for mangrove\\nmonitoring.\\n\\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/lulc-global-mangrove-forests-distribution-2000/docs)\\n","license":"proprietary","providers":[{"name":"NASA SEDAC at the Center for International Earth Science Information Network","roles":["producer","licensor"],"url":"https://doi.org/10.7927/H4J67DW8"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_MANGROVE_FORESTS"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2001-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/MANGROVE_FORESTS"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LANDSAT_MANGROVE_FORESTS_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_MANGROVE_FORESTS#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7927/H4J67DW8"},{"rel":"cite-as","href":"https://doi.org/10.1111/j.1466"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MERIT/DEM/v1_0_3","title":"MERIT DEM: Multi-Error-Removed Improved-Terrain DEM","description":"MERIT DEM a high accuracy global DEM at 3 arc second resolution (~90 m at the equator) produced by eliminating major error components from existing DEMs (NASA SRTM3 DEM, JAXA AW3D DEM, Viewfinder Panoramas DEM).\\nMERIT DEM separates absolute bias, stripe noise, speckle noise and tree height bias using multiple satellite datasets and filtering techniques. After the error removal, land areas mapped with 2 m or better vertical accuracy were increased from 39% to 58%. Significant improvements were found in flat regions where height errors larger than topography variability, and landscapes such as river networks and hill-valley structures became clearly represented.","license":"proprietary","providers":[{"name":"Dai Yamazaki (University of Tokyo)","roles":["producer","licensor"],"url":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/index.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_DEM_v1_0_3"}],"extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["1987-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MERIT/DEM/v1_0_3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MERIT_DEM_v1_0_3_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_DEM_v1_0_3#terms-of-use"},{"rel":"license","href":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/index.html"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MERIT/Hydro_reduced/v1_0_1","title":"MERIT Hydro: supplementary visualization layers","description":"Supplementary visualization layers for [MERIT Hydro](MERIT/Hydro/v1_0_1)\\n","license":"proprietary","providers":[{"name":"Dai Yamazaki (University of Tokyo)","roles":["producer","licensor"],"url":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/index.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_reduced_v1_0_1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1987-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MERIT/Hydro_reduced/v1_0_1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MERIT_Hydro_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_reduced_v1_0_1#terms-of-use"},{"rel":"license","href":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/index.html"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MERIT/Hydro/v1_0_1","title":"MERIT Hydro: global hydrography datasets","description":"\'MERIT Hydro is a new global flow direction map at 3 arc-second resolution (~90\\nm at the equator) derived from the version 1.0.3 of the MERIT DEM elevation data\\nand water body datasets (G1WBM, GSWO and OpenStreetMap).\\n\\nMERIT Hydro contains the output of a new algorithm that\\nextracts river networks near-automatically by separating actual inland basins from\\nnoise caused by the errors in input elevation data. After a minimum\\namount of hand-editing, the constructed hydrography map shows good agreement with\\nexisting quality-controlled river network datasets in terms of flow accumulation\\narea and river basin shape (see Figure 9a in the paper). The location of river\\nstreamlines was realistically aligned with existing satellite-based global\\nriver channel data (see Figure 10a in the paper). Relative error\\nin the drainage area was smaller than 0.05 for 90% of GRDC gauges, confirming\\nthe accuracy of the delineated global river networks. Discrepancies in flow accumulation\\narea were found mostly in arid river basins containing depressions that are occasionally\\nconnected at high water levels and thus resulting in uncertain watershed boundaries.\\n\\nMERIT Hydro improves on existing global hydrography datasets in terms of spatial\\ncoverage (between 90N and 60S) and representation of small streams, mainly due\\nto increased availability of high-quality baseline geospatial datasets.\\n\\n[You can use this web app to visualize MERIT Hydro data.](https://meritdataset.users.earthengine.app/view/merit-hydro-visualization-and-interactive-map)\\n[The app\\\\\'s source code is available.](https://github.com/google/earthengine-community/blob/master/datasets/scripts/Hydro_Visualization.js)\\n\\nKnown Problems:\\n\\n* River width (Update from GWD-LR v1): Width algorithm was updated to consider\\n sub-pixel water fraction. Now, 30 m water map is used for width calculation at\\n 90 m resolution. Currently river width is calculated for each channel separately\\n in braided/anabranching sections. Merged river width should be calculated.\\n* Water body map: There are some inconsistencies between the DEM land sea mask and\\n wate body data (such as new islands along the coast). The quality of OpenStreeetMap\\n water body layer is not uniform in all areas.\\n* Channel bifurcations: Channel bifurcation is not well represented in the current\\n version. Each pixel is assumed to have only one downstream direction. Secondary\\n (or multiple) downstream direction should be considered, to represent complex\\n river networks in the delta regions, floodplains, and braided rivers.\\n* Underground rivers/tunnels: Major underground rivers/tunnels should be implemented\\n to improve large-scale water balance.\\n* River/lake separation: Rivers and lakes need to be separated better for some applications.\\n* Below-sea-level areas: The areas below sea level in coastal regions are not well\\n represented in adjusted elevation data.\\n* Flow direction over glaciers: Flow direction over glaciers is not well represented,\\n because the elevation of glacier centerline is higher than glacier edge.\\n* Supplementary data: It would be better to add location of GRDC gauging stations, water\\n falls, reservoirs, etc.\'\\n","license":"proprietary","providers":[{"name":"Dai Yamazaki (University of Tokyo)","roles":["producer","licensor"],"url":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/index.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_v1_0_1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1987-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MERIT/Hydro/v1_0_1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MERIT_Hydro_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_v1_0_1#terms-of-use"},{"rel":"license","href":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/index.html"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD12Q1","title":"MCD12Q1.006 MODIS Land Cover Type Yearly Global 500m","description":"The MCD12Q1 V6 product provides global land cover types at yearly intervals\\n(2001-2016) derived from six different classification schemes. It is derived\\nusing supervised classifications of MODIS Terra and Aqua reflectance data.\\nThe supervised classifications then undergo additional post-processing that\\nincorporate prior knowledge and ancillary information to further refine\\nspecific classes.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/86/MCD12_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD12Q1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD12Q1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD12Q1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MCD12Q1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD12Q1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD12Q2","title":"MCD12Q2.006 Land Cover Dynamics Yearly Global 500m","description":"The MCD12Q2 V6 Land Cover Dynamics product (informally called the MODIS Global Vegetation Phenology product) provides estimates of the timing of vegetation phenology at global scales. Additionally, it provides information related to the range and summation of the enhanced vegetation index (EVI) computed from MODIS surface reflectance data at each pixel. It identifies 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.\\nThe MCD12Q2 Version 6 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 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.","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://lpdaac.usgs.gov/products/mcd12q2v006/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD12Q2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_MCD12Q2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD12Q2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD15A3H","title":"MCD15A3H.006 MODIS Leaf Area Index/FPAR 4-Day Global 500m","description":"The MCD15A3H V6 level 4, Combined Fraction of Photosynthetically\\nActive Radiation (FPAR), and Leaf Area Index (LAI) product is a\\n4-day composite data set with 500 meter pixel size. The algorithm\\nchooses the \\"best\\" pixel available from all the acquisitions of\\nboth MODIS sensors located on NASA\'s Terra and Aqua satellites\\nfrom within the 4-day period.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD15A3H)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD15A3H.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD15A3H"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD15A3H"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MCD15A3H_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD15A3H#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD15A3H.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD19A2_GRANULES","title":"MCD19A2.006: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km","description":"The MCD19A2 V6 data product is a MODIS Terra and Aqua combined Multi-angle\\nImplementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded\\nLevel 2 product produced daily at 1 km resolution. For more information\\nsee the [MAIAC user guide](https://lpdaac.usgs.gov/documents/110/MCD19_User_Guide_V6.pdf).\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/110/MCD19_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/111/MCD19_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD19A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD19A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD19A2_GRANULES"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD19A2_GRANULES"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MCD19A2_GRANULES_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD19A2_GRANULES#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD19A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD43A1","title":"MCD43A1.006 MODIS BRDF-Albedo Model Parameters Daily 500m","description":"The MCD43A1 V6 Bidirectional Reflectance Distribution\\nFunction and Albedo (BRDF/Albedo) Model Parameters dataset is\\na 500 meter daily 16-day product. The Julian date represents\\nthe 9th day of the 16-day retrieval period, and consequently\\nthe observations are weighted to estimate the BRDF/Albedo for\\nthat day. The MCD43A1 algorithm, as is with all combined products,\\nchooses the best representative pixel from a pool that includes\\nall the acquisitions from both the Terra and Aqua sensors from\\nthe retrieval period.\\n\\nThe MCD43A1 provides the three model\\nweighting parameters (isotropic, volumetric, and geometric) for\\neach of the MODIS bands 1 through 7 and the visible (vis), near\\ninfrared (nir), and shortwave bands used to derive the Albedo\\nand BRDF products (MCD43A3 and MCD43A4). The Mandatory Quality\\nlayers for each of the 10 bands are supplied as well.\\n\\nSee\\n[dataset user guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006/mcd43a1_brdif_albedo_model_parameters_product)\\nfor more information.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD43A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD43A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MCD43A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD43A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD43A2","title":"MCD43A2.006 MODIS BRDF-Albedo Quality Daily 500m","description":"The MCD43A2 V6 Bidirectional Reflectance Distribution\\nFunction and Albedo (BRDF/Albedo) Quality dataset is a 500 meter\\ndaily 16-day product. It contains all the quality information\\nfor the corresponding 16-day MCD43A3 Albedo and the MCD43A4 Nadir-BRDF\\n(NBAR) products.\\n\\nThe MCD43A2 contains individual band quality\\nand observation information for the MODIS land bands\\n1-7, along with the overall BRDF/Albedo quality information.\\n\\nSee\\n[dataset user guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006/mcd43a2_albedo_product)\\nfor more information.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD43A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD43A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MCD43A2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD43A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD43A3","title":"MCD43A3.006 MODIS Albedo Daily 500m","description":"The MCD43A3 V6 Albedo Model dataset is a daily 16-day\\nproduct. It provides both directional hemispherical reflectance\\n(black sky albedo) and bihemispherical reflectance (white sky albedo)\\nfor each of the MODIS surface reflectance bands (band 1 through\\nband 7) as well as 3 broad spectrum bands (visible, near infrared,\\nand shortwave). Each 500m/pixel daily image is generated using\\n16 days of data, centered on the given day. A quality band is\\nalso provided for each of the 10 albedo bands.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD43A3)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD43A3.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A3"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MCD43A3_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A3#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD43A3.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD43A4","title":"MCD43A4.006 MODIS Nadir BRDF-Adjusted Reflectance Daily 500m","description":"The MCD43A4 V6 Nadir Bidirectional Reflectance Distribution\\nFunction Adjusted Reflectance (NBAR) product provides 500 meter\\nreflectance data of the MODIS \\"land\\" bands 1-7. These are adjusted\\nusing a bidirectional reflectance distribution function to model\\nthe values as if they were collected from a nadir view. The data\\nare produced daily based on a 16-day retrieval period, with the\\nimage\'s date occurring on the 9th day. This product combines data\\nfrom both the Terra and Aqua spacecrafts, choosing the best representative\\npixel from the 16-day period.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD43A4)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD43A4.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A4"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A4"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MCD43A4_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A4#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD43A4.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD43C3","title":"MCD43C3.00C BRDF/Albedo Daily L3 0.05 Deg CMG","description":"The MCD43C3 Version 6 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.\\nMCD43C3 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.\\nSee [dataset user guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006/mcd43c3) for more information.","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD43C3.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43C3"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43C3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_MCD43B3_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43C3#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MCD64A1","title":"MCD64A1.006 MODIS Burned Area Monthly Global 500m","description":"The Terra and Aqua combined MCD64A1 Version 6 Burned Area data product is\\na monthly, global gridded 500m product containing per-pixel burned-area\\nand quality information. The MCD64A1 burned-area mapping approach employs\\n500m MODIS Surface Reflectance imagery coupled with 1km MODIS active\\nfire observations. The algorithm uses a burn sensitive vegetation index (VI)\\nto create dynamic thresholds that are applied to the composite data.\\nThe VI is derived from MODIS shortwave infrared atmospherically corrected\\nsurface reflectance bands 5 and 7 with a measure of temporal texture.\\nThe algorithm identifies the date of burn for the 500m grid cells within\\neach individual MODIS tile. The date is encoded in a single data layer as\\nthe ordinal day of the calendar year on which the burn occurred, with\\nvalues assigned to unburned land pixels and additional special values\\nreserved for missing data and water grid cells.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/751/MCD64_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/115/MCD64_ATBD_V6.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD64A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MCD64A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD64A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-11-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD64A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MCD64A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD64A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD64A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD09A1","title":"MOD09A1.006 Terra Surface Reflectance 8-Day Global 500m","description":"The MOD09A1 V6 product provides an estimate of the surface\\nspectral reflectance of Terra MODIS bands 1-7 at 500m resolution\\nand corrected for atmospheric conditions such as gasses, aerosols,\\nand Rayleigh scattering. Along with the seven reflectance bands\\nis a quality layer and four observation bands. For each pixel,\\na value is selected from all the acquisitions within the 8-day\\ncomposite on the basis of high observation coverage, low view angle,\\nthe absence of clouds or cloud shadow, and aerosol loading.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD09A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD09A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD09A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD09GA","title":"MOD09GA.006 Terra Surface Reflectance Daily Global 1km and 500m","description":"The MODIS Surface Reflectance products provide an estimate\\nof the surface spectral reflectance as it would be measured at\\nground level in the absence of atmospheric scattering or absorption.\\nLow-level data are corrected for atmospheric gases and aerosols.\\nMOD09GA version 6 provides bands 1-7 in a daily gridded L2G product\\nin the sinusoidal projection, including 500m reflectance values\\nand 1km observation and geolocation statistics.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09GA)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD09GA.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09GA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD09GA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GA#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD09GA.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD09GQ","title":"MOD09GQ.006 Terra Surface Reflectance Daily Global 250m","description":"The MODIS Surface Reflectance products provide an estimate\\nof the surface spectral reflectance as it would be measured at\\nground level in the absence of atmospheric scattering or absorption.\\nLow-level data are corrected for atmospheric gases and aerosols.\\nMOD09GQ version 6 provides bands 1 and 2 at a 250m resolution in\\na daily gridded L2G product in the Sinusoidal projection, including\\na QC and five observation layers. This product is meant to be used\\nin conjunction with the MOD09GA where important quality and viewing\\ngeometry information is stored.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09GQ)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD09GQ.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GQ"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09GQ"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD09GQ_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GQ#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD09GQ.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD09Q1","title":"MOD09Q1.006 Terra Surface Reflectance 8-Day Global 250m","description":"The MOD09Q1 product provides an estimate of the surface\\nspectral reflectance of bands 1 and 2 at 250m resolution and corrected\\nfor atmospheric conditions such as gasses, aerosols, and Rayleigh\\nscattering. Along with the two reflectance bands, a quality layer\\nis also included. For each pixel, a value is selected from all\\nthe acquisitions within the 8-day composite on the basis of high\\nobservation coverage, low view angle, the absence of clouds or\\ncloud shadow, and aerosol loading.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09Q1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD09Q1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09Q1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09Q1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD09Q1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09Q1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD09Q1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD10A1","title":"MOD10A1.006 Terra Snow Cover Daily Global 500m","description":"The MOD10A1 V6 Snow Cover Daily Global 500m product\\ncontains snow cover, snow albedo, fractional snow cover, and quality\\nassessment (QA) data. Snow cover data are based on a snow mapping\\nalgorithm that employs a Normalized Difference Snow Index (NDSI)\\nand other criteria tests.\\n\\n[General documentation](https://doi.org/10.5067/MODIS/MOD10A1.006)\\n","license":"proprietary","providers":[{"name":"NASA NSIDC DAAC at CIRES","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD10A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD10A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD10A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MOD10A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD10A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD10A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD11A1","title":"MOD11A1.006 Terra Land Surface Temperature and Emissivity Daily Global 1km","description":"The MOD11A1 V6 product provides daily land surface temperature\\n(LST) and emissivity values in a 1200 x 1200 kilometer grid. The\\ntemperature value is derived from the MOD11_L2 swath product. Above\\n30 degrees latitude, some pixels may have multiple observations\\nwhere the criteria for clear-sky are met. When this occurs, the\\npixel value is the average of all qualifying observations. Provided\\nalong with both the day-time and night-time surface temperature\\nbands and their quality indicator layers are MODIS bands 31 and\\n32 and six observation layers.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD11A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD11A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD11A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD11A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD11A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD11A2","title":"MOD11A2.006 Terra Land Surface Temperature and Emissivity 8-Day Global 1km","description":"The MOD11A2 V6 product provides an average 8-day land\\nsurface temperature (LST) in a 1200 x 1200 kilometer grid. Each\\npixel value in MOD11A2 is a simple average of all the corresponding\\nMOD11A1 LST pixels collected within that 8 day period. The 8 day\\ncompositing period was chosen because twice that period is the\\nexact ground track repeat period of the Terra and Aqua platforms.\\nIn this product, along with both the day- and night-time surface\\ntemperature bands and their quality indicator (QC) layers, are\\nalso MODIS bands 31 and 32 and eight observation layers.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD11A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD11A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD11A2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD11A2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD11A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD13A1","title":"MOD13A1.006 Terra Vegetation Indices 16-Day Global 500m","description":"The MOD13A1 V6 product provides a Vegetation Index (VI)\\nvalue at a per pixel basis. There are two primary vegetation layers.\\nThe first is the Normalized Difference Vegetation Index (NDVI)\\nwhich is referred to as the continuity index to the existing National\\nOceanic and Atmospheric Administration-Advanced Very High Resolution\\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\\nvariations and maintains sensitivity over dense vegetation conditions.\\nThe EVI also uses the blue band to remove residual atmosphere contamination\\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\\nand EVI products are computed from atmospherically corrected bi-directional\\nsurface reflectances that have been masked for water, clouds, heavy\\naerosols, and cloud shadows.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD13A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD13A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD13A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD13A2","title":"MOD13A2.006 Terra Vegetation Indices 16-Day Global 1km","description":"The MOD13A2 V6 product provides two Vegetation Indices (VI): the Normalized\\nDifference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).\\nThe NDVI is referred to as the continuity index to the existing National\\nOceanic and Atmospheric Administration-Advanced Very High Resolution\\nRadiometer (NOAA-AVHRR) derived NDVI. The EVI has improved sensitivity over\\nhigh biomass regions.\\n\\nThe algorithm for this product chooses the best available\\npixel value from all the acquisitions from the 16-day period. The criteria\\nused are low clouds, low view angle, and the highest NDVI/EVI value.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD13A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13A2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD13A2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD13A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD13Q1","title":"MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m","description":"The MOD13Q1 V6 product provides a Vegetation Index (VI)\\nvalue at a per pixel basis. There are two primary vegetation layers.\\nThe first is the Normalized Difference Vegetation Index (NDVI)\\nwhich is referred to as the continuity index to the existing National\\nOceanic and Atmospheric Administration-Advanced Very High Resolution\\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\\nvariations and maintains sensitivity over dense vegetation conditions.\\nThe EVI also uses the blue band to remove residual atmosphere contamination\\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\\nand EVI products are computed from atmospherically corrected bi-directional\\nsurface reflectances that have been masked for water, clouds, heavy\\naerosols, and cloud shadows.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD13Q1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13Q1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MOD13Q1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD13Q1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD14A1","title":"MOD14A1.006: Terra Thermal Anomalies & Fire Daily Global 1km","description":"The MOD14A1 V6 dataset provides daily fire mask composites\\nat 1km resolution derived from the MODIS 4- and 11-micrometer radiances.\\nThe fire detection strategy is based on absolute detection of a\\nfire (when the fire strength is sufficient to detect), and on detection\\nrelative to its background (to account for variability of the surface\\ntemperature and reflection by sunlight). The product distinguishes\\nbetween fire, no fire and no observation. This information is used\\nfor monitoring the spatial and temporal distribution of fires in\\ndifferent ecosystems, detecting changes in fire distribution and\\nidentifying new fire frontiers, wild fires, and changes in the\\nfrequency of the fires or their relative strength. \\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD14A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD14A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD14A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD14A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD14A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD14A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD14A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD14A2","title":"MOD14A2.006: Terra Thermal Anomalies & Fire 8-Day Global 1km","description":"The MOD14A2 V6 dataset provides 8-day fire mask composites\\nat 1km resolution. It contains the maximum value of the individual\\npixel classes over the compositing period. Along with the fire\\nmask, an associated quality information layer is also provided.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD14A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD14A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD14A2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD14A2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD14A2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD14A2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD14A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD15A2H","title":"MOD15A2H.006: Terra Leaf Area Index/FPAR 8-Day Global 500m","description":"The MOD15A2H V6 MODIS combined Leaf Area Index (LAI) and\\nFraction of Photosynthetically Active Radiation (FPAR) product is an 8-day\\ncomposite dataset at 500m resolution. The algorithm chooses the \\"best\\" pixel\\navailable from all the acquisitions of the Terra sensor from within the 8-day period.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD15A2H)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD15A2H.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD15A2H"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD15A2H"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD15A2H_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD15A2H#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD15A2H.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD16A2","title":"MOD16A2.006: Terra Net Evapotranspiration 8-Day Global 500m","description":"The MOD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an\\n8-day composite product produced at 500 meter pixel resolution. The\\nalgorithm used for the MOD16 data product collection is based on the logic\\nof the Penman-Monteith equation, which includes inputs of daily\\nmeteorological reanalysis data along with MODIS remotely sensed data\\nproducts such as vegetation property dynamics, albedo, and land cover.\\n\\nThe pixel values for the two Evapotranspiration layers (ET & PET) are the\\nsum of all eight days within the composite period. The pixel values for\\nthe two Latent Heat layers (LE & PLE) are the average of all eight days\\nwithin the composite period. Note that the last 8-day period of each year\\nis a 5 or 6-day composite period, depending on the year.\\n\\nAccording to\\n[the \\"Layers\\" section of the dataset docs](https://lpdaac.usgs.gov/products/mod16a2v006),\\nthe class assignment in fill values 32761 through 32767 might be inaccurate.\\nThey are not included in the EE assets.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/494/MOD16_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/93/MOD16_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD16A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD16A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD16A2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD16A2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MOD16A2_V006_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD16A2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD16A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD17A2H","title":"MOD17A2H.006: Terra Gross Primary Productivity 8-Day Global 500M 500m","description":"The MOD17A2H V6 Gross Primary Productivity (GPP) product\\nis a cumulative 8-day composite with a 500m resolution. The product\\nis based on the radiation-use efficiency concept and can be potentially\\nused as inputs to data models to calculate terrestrial energy,\\ncarbon, water cycle processes, and biogeochemistry of vegetation.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/495/MOD17_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/95/MOD17_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD17A2H)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD17A2H.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A2H"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A2H"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD17A2H_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A2H#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD17A2H.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD17A3HGF","title":"MOD17A3HGF.006: Terra Net Primary Production Gap-Filled Yearly Global 500m","description":"The MOD17A3HGF V6 product provides information about\\nannual Net Primary Productivity (NPP) at 500m pixel resolution.\\n Annual NPP is derived from the sum of all 8-day Net Photosynthesis\\n(PSN) products (MOD17A2H) from the given year. The PSN value\\nis the difference of the Gross Primary Productivity (GPP) and the\\nMaintenance Respiration (MR) (GPP-MR).\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD17A3HGF.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A3HGF"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A3HGF"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD17A3HGF_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A3HGF#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD17A3HGF.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD44B","title":"MOD44B.006 Terra Vegetation Continuous Fields Yearly Global 250m","description":"The Terra MODIS Vegetation Continuous Fields (VCF)\\nproduct is a sub-pixel-level representation of surface vegetation\\ncover estimates globally. Designed to continuously represent\\nEarth\'s terrestrial surface as a proportion of basic vegetation\\ntraits, it provides a gradation of three surface cover components:\\npercent tree cover, percent non-tree cover, and percent bare.\\nVCF products provide a continuous, quantitative portrayal of\\nland surface cover with improved spatial detail, and hence, are\\nwidely used in environmental modeling and monitoring applications.\\n\\nGenerated yearly, the VCF product is produced using monthly\\ncomposites of Terra MODIS 250 and 500 meters Land Surface Reflectance\\ndata, including all seven bands, and Land Surface Temperature.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/112/MOD44B_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD44B)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD44B.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD44B"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD44B"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD44B_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD44B#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MOD44W","title":"MOD44W.006 Terra Land Water Mask Derived from MODIS and SRTM Yearly Global 250m","description":"The MOD44W V6 land/water mask 250m product is derived using a\\ndecision tree classifier trained with MODIS data and validated with the\\nMOD44W V5 product. A series of masks are applied to address known issues\\ncaused by terrain shadow, burn scars, cloudiness, or ice cover in oceans.\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MOD44W.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD44W"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD44W"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD44W_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD44W#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD44W.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MODOCGA","title":"MODOCGA.006 Terra Ocean Reflectance Daily Global 1km","description":"The MODOCGA V6 ocean reflectance product consists of\\n1 kilometer reflectance data from Terra MODIS bands 8-16. The product\\nis referred to as ocean reflectance, because bands 8-16 are used\\nprimarily to produce ocean products, but this is not an ocean product\\nas the tiles produced are land tiles.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MODOCGA)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MODOCGA.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MODOCGA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MODOCGA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MODOCGA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MODOCGA#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MODOCGA.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD09A1","title":"MYD09A1.006 Aqua Surface Reflectance 8-Day Global 500m","description":"The MYD09A1 V6 product provides an estimate of the surface\\nspectral reflectance of Aqua MODIS bands 1-7 at 500m resolution\\nand corrected for atmospheric conditions such as gasses, aerosols,\\nand Rayleigh scattering. Along with the seven reflectance bands\\nis a quality layer and four observation bands. For each pixel,\\na value is selected from all the acquisitions within the 8-day\\ncomposite on the basis of high observation coverage, low view angle,\\nthe absence of clouds or cloud shadow, and aerosol loading.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD09A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD09A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD09A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD09GA","title":"MYD09GA.006 Aqua Surface Reflectance Daily Global 1km and 500m","description":"The MODIS Surface Reflectance products provide an estimate\\nof the surface spectral reflectance as it would be measured at\\nground level in the absence of atmospheric scattering or absorption.\\nLow-level data are corrected for atmospheric gases and aerosols.\\nMYD09GA version 6 provides bands 1-7 in a daily gridded L2G product\\nin the sinusoidal projection, including 500m reflectance values\\nand 1km observation and geolocation statistics.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09GA)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD09GA.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09GA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09GA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD09GA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09GA#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD09GA.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD09GQ","title":"MYD09GQ.006 Aqua Surface Reflectance Daily Global 250m","description":"The MODIS Surface Reflectance products provide an estimate\\nof the surface spectral reflectance as it would be measured at\\nground level in the absence of atmospheric scattering or absorption.\\nLow-level data are corrected for atmospheric gases and aerosols.\\nMYD09GQ version 6 provides bands 1 and 2 at a 250m resolution in\\na daily gridded L2G product in the Sinusoidal projection, including\\na QC and five observation layers. This product is meant to be used\\nin conjunction with the MOD09GA where important quality and viewing\\ngeometry information is stored.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09GQ)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD09GQ.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09GQ"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09GQ"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD09GQ_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09GQ#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD09GQ.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD09Q1","title":"MYD09Q1.006 Aqua Surface Reflectance 8-Day Global 250m","description":"The MYD09Q1 product provides an estimate of the surface\\nspectral reflectance of bands 1 and 2 at 250m resolution and corrected\\nfor atmospheric conditions such as gasses, aerosols, and Rayleigh\\nscattering. Along with the two reflectance bands, a quality layer\\nis also included. For each pixel, a value is selected from all\\nthe acquisitions within the 8-day composite on the basis of high\\nobservation coverage, low view angle, the absence of clouds or\\ncloud shadow, and aerosol loading.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09Q1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD09Q1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09Q1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09Q1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD09Q1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09Q1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD09Q1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD10A1","title":"MYD10A1.006 Aqua Snow Cover Daily Global 500m","description":"The MYD10A1 V6 Snow Cover Daily Global 500m product\\ncontains snow cover, snow albedo, fractional snow cover, and quality\\nassessment (QA) data. Snow cover data are based on a snow mapping\\nalgorithm that employs a Normalized Difference Snow Index (NDSI)\\nand other criteria tests.\\n\\n[General documentation](https://doi.org/10.5067/MODIS/MYD10A1.006)\\n","license":"proprietary","providers":[{"name":"NASA NSIDC DAAC at CIRES","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD10A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD10A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD10A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MOD10A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD10A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD10A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD11A1","title":"MYD11A1.006 Aqua Land Surface Temperature and Emissivity Daily Global 1km","description":"The MYD11A1 V6 product provides daily land surface temperature\\n(LST) and emissivity values in a 1200 x 1200 kilometer grid. The\\ntemperature value is derived from the MOD11_L2 swath product. Above\\n30 degrees latitude, some pixels may have multiple observations\\nwhere the criteria for clear-sky are met. When this occurs, the\\npixel value is the average of all qualifying observations. Provided\\nalong with both the day-time and night-time surface temperature\\nbands and their quality indicator layers are MODIS bands 31 and\\n32 and six observation layers.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD11A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD11A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD11A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD11A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD11A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD11A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD11A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD11A2","title":"MYD11A2.006 Aqua Land Surface Temperature and Emissivity 8-Day Global 1km","description":"The MYD11A2 V6 product provides an average 8-day land\\nsurface temperature (LST) in a 1200 x 1200 kilometer grid. Each\\npixel value in MYD11A2 is a simple average of all the corresponding\\nMOD11A1 LST pixels collected within that 8 day period. The 8 day\\ncompositing period was chosen because twice that period is the\\nexact ground track repeat period of the Aqua and Aqua platforms.\\nIn this product, along with both the day- and night-time surface\\ntemperature bands and their quality indicator (QC) layers, are\\nalso MODIS bands 31 and 32 and eight observation layers.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD11A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD11A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD11A2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD11A2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD11A2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD11A2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD11A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD13A1","title":"MYD13A1.006 Aqua Vegetation Indices 16-Day Global 500m","description":"The MYD13A1 V6 product provides a Vegetation Index (VI)\\nvalue at a per pixel basis. There are two primary vegetation layers.\\nThe first is the Normalized Difference Vegetation Index (NDVI)\\nwhich is referred to as the continuity index to the existing National\\nOceanic and Atmospheric Administration-Advanced Very High Resolution\\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\\nvariations and maintains sensitivity over dense vegetation conditions.\\nThe EVI also uses the blue band to remove residual atmosphere contamination\\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\\nand EVI products are computed from atmospherically corrected bi-directional\\nsurface reflectances that have been masked for water, clouds, heavy\\naerosols, and cloud shadows.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD13A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD13A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD13A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD13A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD13A2","title":"MYD13A2.006 Aqua Vegetation Indices 16-Day Global 1km","description":"The MYD13A2 V6 product provides two Vegetation Indices (VI): the Normalized\\nDifference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).\\nThe NDVI is referred to as the continuity index to the existing National\\nOceanic and Atmospheric Administration-Advanced Very High Resolution\\nRadiometer (NOAA-AVHRR) derived NDVI. The EVI has improved sensitivity over\\nhigh biomass regions.\\n\\nThe algorithm for this product chooses the best available\\npixel value from all the acquisitions from the 16-day period. The criteria\\nused are low clouds, low view angle, and the highest NDVI/EVI value.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD13A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13A2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13A2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD13A2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13A2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD13A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD13Q1","title":"MYD13Q1.006 Aqua Vegetation Indices 16-Day Global 250m","description":"The MYD13Q1 V6 product provides a Vegetation Index (VI)\\nvalue at a per pixel basis. There are two primary vegetation layers.\\nThe first is the Normalized Difference Vegetation Index (NDVI)\\nwhich is referred to as the continuity index to the existing National\\nOceanic and Atmospheric Administration-Advanced Very High Resolution\\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\\nvariations and maintains sensitivity over dense vegetation conditions.\\nThe EVI also uses the blue band to remove residual atmosphere contamination\\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\\nand EVI products are computed from atmospherically corrected bi-directional\\nsurface reflectances that have been masked for water, clouds, heavy\\naerosols, and cloud shadows.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD13Q1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13Q1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13Q1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MOD13Q1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13Q1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD13Q1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD14A1","title":"MYD14A1.006: Aqua Thermal Anomalies & Fire Daily Global 1km","description":"The MYD14A1 V6 dataset provides daily fire mask composites\\nat 1km resolution derived from the MODIS 4- and 11-micrometer radiances.\\nThe fire detection strategy is based on absolute detection of a\\nfire (when the fire strength is sufficient to detect), and on detection\\nrelative to its background (to account for variability of the surface\\ntemperature and reflection by sunlight). The product distinguishes\\nbetween fire, no fire and no observation. This information is used\\nfor monitoring the spatial and temporal distribution of fires in\\ndifferent ecosystems, detecting changes in fire distribution and\\nidentifying new fire frontiers, wild fires, and changes in the\\nfrequency of the fires or their relative strength. \\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD14A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD14A1.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD14A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD14A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD14A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD14A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD14A1.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD14A2","title":"MYD14A2.006: Aqua Thermal Anomalies & Fire 8-Day Global 1km","description":"The MYD14A2 V6 dataset provides 8-day fire mask composites\\nat 1km resolution. It contains the maximum value of the individual\\npixel classes over the compositing period. Along with the fire\\nmask, an associated quality information layer is also provided.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD14A2)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD14A2.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD14A2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD14A2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD14A2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD14A2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD14A2.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD15A2H","title":"MYD15A2H.006: Aqua Leaf Area Index/FPAR 8-Day Global 500m","description":"The MYD15A2H V6 MODIS combined Leaf Area Index (LAI) and\\nFraction of Photosynthetically Active Radiation (FPAR) product is an 8-day\\ncomposite dataset at 500m resolution. The algorithm chooses the \\"best\\" pixel\\navailable from all the acquisitions of the Aqua sensor from within the 8-day period.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD15A2H)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD15A2H.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD15A2H"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD15A2H"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD15A2H_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD15A2H#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD15A2H.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD17A2H","title":"MYD17A2H.006: Aqua Gross Primary Productivity 8-Day Global 500M 500m","description":"The MYD17A2H V6 Gross Primary Productivity (GPP) product\\nis a cumulative 8-day composite with a 500m resolution. The product\\nis based on the radiation-use efficiency concept and can be potentially\\nused as inputs to data models to calculate terrestrial energy,\\ncarbon, water cycle processes, and biogeochemistry of vegetation.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/495/MOD17_User_Guide_V6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/95/MOD17_ATBD.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD17A2H)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD17A2H.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD17A2H"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A2H"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD17A2H_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD17A2H#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD17A2H.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYD17A3HGF","title":"MYD17A3HGF.006: Aqua Net Primary Production Gap-Filled Yearly Global 500m","description":"The MYD17A3HGF V6 product provides information about\\nannual Net Primary Productivity (NPP) at 500m pixel resolution.\\n Annual NPP is derived from the sum of all 8-day Net Photosynthesis\\n(PSN) products (MOD17A2H) from the given year. The PSN value\\nis the difference of the Gross Primary Productivity (GPP) and the\\nMaintenance Respiration (MR) (GPP-MR).\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MODIS/MYD17A3HGF.006"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD17A3HGF"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A3HGF"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MODIS_006_MOD17A3HGF_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD17A3HGF#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD17A3HGF.006"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/006/MYDOCGA","title":"MYDOCGA.006 Aqua Ocean Reflectance Daily Global 1km","description":"The MYDOCGA V6 ocean reflectance product consists of\\n1 kilometer reflectance data from Aqua MODIS bands 8-16. 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\\n\\nThe MODIS Normalized Difference Vegetation Index (NDVI) complements\\nNOAA\'s Advanced Very High Resolution Radiometer (AVHRR) NDVI products\\nprovide continuity for time series historical applications. MODIS also\\nincludes a new Enhanced Vegetation Index (EVI) that minimizes canopy\\nbackground variations and maintains sensitivity over dense vegetation\\nconditions. The EVI also uses the blue band to remove residual atmosphere\\ncontamination caused by smoke and sub-pixel thin cloud clouds. The MODIS\\nNDVI and EVI products are computed from atmospherically corrected\\nbi-directional surface reflectances that have been masked for water, clouds,\\nheavy aerosols, and cloud shadows.
\\n\\nGlobal MOD13A1 data are provided every 16 days at 500-meter spatial\\nresolution as a gridded level-3 product in the Sinusoidal projection.\\nVegetation indices are used for global monitoring of vegetation conditions\\nand are used in products displaying land cover and land cover changes. These\\ndata may be used as input for modeling global biogeochemical and hydrologic\\nprocesses and global and regional climate. These data also may be used for\\ncharacterizing land surface biophysical properties and processes, including\\nprimary production and land cover conversion.
\\n\\nVersion-5 MODIS Vegetation Indices products have attained Validation Stage\\n3.
Please visit LP\\nDAAC \'Citing Our Data\' page for information on citing LP DAAC\\ndatasets.
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","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_BAI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA_006_BAI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/BAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_BAI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/MYD09GA_006_EVI","title":"MODIS Aqua Daily EVI","description":" The Enhanced Vegetation Index (EVI) is generated from the\\nNear-IR, Red and Blue bands of each scene, and ranges in value from\\n-1.0 to 1.0. See\\n\\nHuete et al. (2002) for details. This product is generated from the MODIS/006/MYD09GA surface reflectance composites.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_EVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA_006_EVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_EVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/MYD09GA_006_NDSI","title":"MODIS Aqua Daily NDSI","description":" The Normalized Difference Snow Index is used to\\nidentify snow, based on its characteristically higher reflectance in\\nthe visible portion of the spectrum compared to the mid-IR. NDSI is\\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\\n1.0. See\\n\\nRiggs et al. (1994) for details. This product is generated from the MODIS/006/MYD09GA surface reflectance composites.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_NDSI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA_006_NDSI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDSI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_NDSI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/MYD09GA_006_NDVI","title":"MODIS Aqua Daily NDVI","description":" The Normalized Difference Vegetation Index is\\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\\n(NIR + Red), and ranges in value from -1.0 to 1.0. This product is generated from the MODIS/006/MYD09GA surface reflectance composites.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_NDVI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA_006_NDVI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_NDVI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/MYD09GA_006_NDWI","title":"MODIS Aqua Daily NDWI","description":" The Normalized Difference Water Index (NDWI) is sensitive\\nto changes in liquid water content of vegetation canopies. It is\\nderived from the Near-IR band and a second IR band, ≈1.24μm when\\navailable and the nearest available IR band otherwise. It ranges in\\nvalue from -1.0 to 1.0. See\\n\\nGao (1996) for details. This product is generated from the MODIS/006/MYD09GA surface reflectance composites.
","license":"proprietary","providers":[{"name":"Google","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_NDWI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA_006_NDWI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NDWI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_NDWI#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/MYD13A1","title":"MYD13A1.005 Vegetation Indices 16-Day L3 Global 500m","description":"Global MODIS vegetation indices are designed to\\nprovide consistent spatial and temporal comparisons of vegetation conditions.\\nBlue, red, and near-infrared reflectances, centered at 469-nanometers,\\n645-nanometers, and 858-nanometers, respectively, are used to determine the\\nMODIS daily vegetation indices.
\\n\\nThe MODIS Normalized Difference Vegetation Index (NDVI) complements\\nNOAA\'s Advanced Very High Resolution Radiometer (AVHRR) NDVI products\\nprovide continuity for time series historical applications. MODIS also\\nincludes a new Enhanced Vegetation Index (EVI) that minimizes canopy\\nbackground variations and maintains sensitivity over dense vegetation\\nconditions. The EVI also uses the blue band to remove residual atmosphere\\ncontamination caused by smoke and sub-pixel thin cloud clouds. The MODIS\\nNDVI and EVI products are computed from atmospherically corrected\\nbi-directional surface reflectances that have been masked for water, clouds,\\nheavy aerosols, and cloud shadows.
\\n\\nGlobal MYD13A1 data are provided every 16 days at 500-meter spatial\\nresolution as a gridded level-3 product in the Sinusoidal projection.\\nVegetation indices are used for global monitoring of vegetation conditions\\nand are used in products displaying land cover and land cover changes. These\\ndata may be used as input for modeling global biogeochemical and hydrologic\\nprocesses and global and regional climate. These data also may be used for\\ncharacterizing land surface biophysical properties and processes, including\\nprimary production and land cover conversion.
\\n\\nVersion-5 MODIS Vegetation Indices products have attained Validation Stage\\n3.
Vegetation Indices production is phased between Terra and Aqua\\nacquisitions, with Terra beginning on Day 001 and Aqua beginning on Day\\n008.
Please visit LP\\nDAAC \'Citing Our Data\' page for information on citing LP DAAC\\ndatasets.
","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/myd13a1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD13A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD13A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":""},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD13A1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"MODIS/NTSG/MOD16A2/105","title":"MOD16A2: MODIS Global Terrestrial Evapotranspiration 8-Day Global 1km","description":"The MOD16A2 V105 product provides information about\\n8-day global terrestrial evapotranspiration at 1km pixel resolution.\\nEvapotranspiration (ET) is the sum of evaporation and plant transpiration\\nfrom the Earth\'s surface to the atmosphere. With long-term ET\\ndata, the effects of changes in climate, land use, and ecosystems\\ndisturbances can be quantified.\\n\\nThe MOD16A2 product is produced by the Numerical Terradynamic Simulation\\nGroup [NTSG](http://www.ntsg.umt.edu/),\\nUniversity of Montana (UMT) in conjunction with NASA Earth Observing\\nSystem. For more details about the algorithm used see the [algorithm\\ntheoretical basis document](https://scholarworks.umt.edu/cgi/viewcontent.cgi?article=1267&context=ntsg_pubs).\\n","license":"proprietary","providers":[{"name":"Numerical Terradynamic Simulation Group, The University of Montana","roles":["producer","licensor"],"url":"http://www.ntsg.umt.edu/project/modis/mod16.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_NTSG_MOD16A2_105"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2014-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/NTSG/MOD16A2/105"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/MOD16A2_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_NTSG_MOD16A2_105#terms-of-use"},{"rel":"source","href":"http://files.ntsg.umt.edu/data/MOD16_TP/"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/ASTER_GED/AG100_003","title":"AG100: ASTER Global Emissivity Dataset 100-meter V003","description":"The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global\\nEmissivity Database (ASTER-GED) was developed by the National Aeronautics\\nand Space Administration\'s (NASA) Jet Propulsion Laboratory (JPL),\\nCalifornia Institute of Technology. This product includes the mean\\nemissivity and standard deviation for all 5 ASTER Thermal Infrared bands,\\nmean land surface temperature (LST) and standard deviation, a re-sampled\\nASTER GDEM, land-water mask, mean Normalized Difference Vegetation Index\\n(NDVI) and standard deviation, and observation count.\\n\\nASTER-GED land surface temperature and emissivity (LST&E) are generated\\nusing the ASTER Temperature Emissivity Separation (TES) algorithm in\\ncombination with a Water Vapor Scaling (WVS) atmospheric correction method\\nusing MODIS MOD07 atmospheric profiles and the MODTRAN 5.2 radiative\\ntransfer model.\\n\\nThis product was derived from clear-sky pixels for all available ASTER data\\n(2000-2008).\\n\\n[User\'s Guide] (https://lpdaac.usgs.gov/documents/120/ASTERGED_User_Guide_V3.pdf)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/COMMUNITY/ASTER_GED/AG100.003"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ASTER_GED_AG100_003"}],"extent":{"spatial":{"bbox":[[-180,-59,180,80]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2008-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ASTER_GED/AG100_003"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_ASTER_GED_AG100_003_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ASTER_GED_AG100_003#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/COMMUNITY/ASTER_GED/AG100.003"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/FLDAS/NOAH01/C/GL/M/V001","title":"FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data\\nAssimilation System\\n","description":"The FLDAS dataset (McNally et al. 2017), was designed to assist with food\\nsecurity assessments in data-sparse, developing country settings. It\\nincludes information on many climate-related variables including moisture\\ncontent, humidity, evapotranspiration, average soil temperature, total\\nprecipitation rate, etc.\\n\\nThere are multiple different FLDAS datasets; this one uses Noah version\\n3.6.1 surface model with CHIRPS-6 hourly rainfall that has been\\ndownscaled using the [NASA Land Surface Data Toolkit](https://lis.gsfc.nasa.gov/software/ldt).\\nwhich is part of the [Land Information System\\nframework](LIS; http://lis.gsfc.nasa.gov/). Temporal desegregation is required so that daily\\nrainfall inputs can be used in both energy and water balance calculations\\n\\nFor forcing data, this simulation uses a combination of the new version of Modern-Era\\nRetrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate\\nHazards Group InfraRed Precipitation with Station data (CHIRPS), a quasi-global rainfall\\ndataset designed for seasonal drought monitoring and trend analysis (Funk et al., 2015).\\n\\nDocumentation:\\n\\n* [Readme](https://hydro1.gesdisc.eosdis.nasa.gov/data/FLDAS/FLDAS_NOAH01_C_GL_M.001/doc/README_FLDAS.pdf)\\n\\n* [How-to](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\\n\\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\\n","license":"proprietary","providers":[{"name":"NASA GES DISC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/5NHC22T9375G"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001"}],"extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["1982-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/FLDAS/NOAH01/C/GL/M/V001"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_FLDAS_NOAH01_C_GL_M_V001_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001#terms-of-use"},{"rel":"source","href":"https://hydro1.gesdisc.eosdis.nasa.gov/data/FLDAS/FLDAS_NOAH01_C_GL_M.001/"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GIMMS/3GV0","title":"GIMMS NDVI from AVHRR Sensors (3rd Generation)","description":"GIMMS NDVI is generated from several NOAA\'s AVHRR sensors for a global\\n1/12-degree lat/lon grid. The latest version of the GIMMS NDVI dataset\\nis named NDVI3g (third generation GIMMS NDVI from AVHRR sensors).\\n","license":"proprietary","providers":[{"name":"NASA/NOAA","roles":["producer","licensor"],"url":"https://nex.nasa.gov/nex/projects/1349/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GIMMS_3GV0"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-07-01T00:00:00Z","2013-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GIMMS/3GV0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_GIMMS_3GV0_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GIMMS_3GV0#terms-of-use"},{"rel":"source","href":"https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v0/"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GLDAS/V021/NOAH/G025/T3H","title":"GLDAS-2.1: Global Land Data Assimilation System","description":"Global Land Data Assimilation System (GLDAS) ingests satellite and\\nground-based observational data products. Using advanced land surface\\nmodeling and data assimilation techniques, it generates optimal\\nfields of land surface states and fluxes.\\n\\n\\nGLDAS-2.1 is one of two components of the GLDAS Version 2 (GLDAS-2) dataset,\\nthe second being GLDAS-2.0. GLDAS-2.1 is analogous to GLDAS-1 product stream,\\nwith upgraded models forced by a combination of [GDAS, disaggregated GPCP,\\nand AGRMET radiation data sets](https://ldas.gsfc.nasa.gov/gldas/GLDASforcing.php).\\n\\nThe GLDAS-2.1 simulation started on January 1, 2000 using the\\nconditions from the GLDAS-2.0 simulation. This simulation was\\nforced with National Oceanic and Atmospheric Administration\\n(NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis\\nfields (Derber et al., 1991), the disaggregated Global Precipitation\\nClimatology Project (GPCP) precipitation fields (Adler et al., 2003),\\nand the Air Force Weather Agency\'s AGRicultural METeorological\\nmodeling system (AGRMET) radiation fields which became available\\nfor March 1, 2001 onwards.\\n\\nDocumentation:\\n\\n* [Redme](https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_NOAH025_3H.2.1/doc/README_GLDAS2.pdf)\\n\\n* [How-to](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\\n\\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\\n\\n* [GES DISC Data Rods Documentation](https://disc.gsfc.nasa.gov/information/tools?title=Hydrology%20Data%20Rods)\\n","license":"proprietary","providers":[{"name":"NASA GES DISC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/E7TYRXPJKWOQ"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V021/NOAH/G025/T3H"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_GLDAS_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GLDAS/V20/NOAH/G025/T3H","title":"Reprocessed GLDAS-2.0: Global Land Data Assimilation System","description":"Global Land Data Assimilation System (GLDAS) ingests satellite and\\nground-based observational data products. Using advanced land surface\\nmodeling and data assimilation techniques, it generates optimal\\nfields of land surface states and fluxes.\\n\\n\\nGLDAS-2.0 is one of two components of the GLDAS Version 2 (GLDAS-2)\\ndataset, the second being GLDAS-2.1. GLDAS-2.0 is reprocessed with\\nthe updated Princeton Global Meteorological Forcing Dataset\\n(Sheffield et al., 2006) and upgraded Land Information System\\nVersion 7 (LIS-7). It covers the period 1948-2010, and will be extended\\nto more recent years as corresponding forcing data become available.\\n\\nThe model simulation was initialized on January 1, 1948, using soil\\nmoisture and other state fields from the LSM climatology for\\nthat day of the year. The simulation used the common GLDAS datasets\\nfor land cover (MCD12Q1: Friedl et al., 2010), land water mask\\n(MOD44W: Carroll et al., 2009), soil texture (Reynolds, 1999), and\\nelevation (GTOPO30). The MODIS based land surface parameters are used in\\nthe current GLDAS-2.x products while the AVHRR base parameters were used\\nin GLDAS-1 and previous GLDAS-2 products (prior to October 2012).\\n\\nDocumentation:\\n\\n* [Redme](https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_NOAH025_3H.2.0/doc/README_GLDAS2.pdf)\\n\\n* [How-to](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\\n\\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\\n","license":"proprietary","providers":[{"name":"NASA GES DISC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/342OHQM9AK6Q"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V20_NOAH_G025_T3H"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1948-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V20/NOAH/G025/T3H"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_GLDAS_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V20_NOAH_G025_T3H#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GPM_L3/IMERG_MONTHLY_V06","title":"GPM: Monthly Global Precipitation Measurement (GPM) v6","description":"Global Precipitation Measurement (GPM) is an international\\nsatellite mission to provide next-generation observations of\\nrain and snow worldwide every three hours. The Integrated Multi-satellitE\\nRetrievals for GPM (IMERG) is the unified algorithm that provides\\nrainfall estimates combining data from all passive-microwave\\ninstruments in the GPM Constellation.\\n\\nThis algorithm is intended to intercalibrate, merge, and interpolate\\nall satellite microwave precipitation estimates, together with\\nmicrowave-calibrated infrared (IR) satellite estimates, precipitation\\ngauge analyses, and potentially other precipitation estimators at fine\\ntime and space scales for the TRMM and GPM eras over the entire globe.\\nThe system is run several times for each observation time, first\\ngiving a quick estimate and successively providing better estimates\\nas more data arrive. The final step uses monthly gauge data to\\ncreate research-level products. See [IMERG Technical Documentation](https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf)\\nfor more details on the algorithm.\\n\\nDocumentation:\\n* [Algorithm Theoretical Basis Document](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V06.pdf)\\n\\n* [IMERG Quality Index](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf)\\n\\n* [Caveats for IMERG extension into TRMM era](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_TRMMera-caveats.pdf)\\n\\n* [IMERG Technical Documentation](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_doc.06.pdf)\\n\\n* [Release notes; New Morphing algorithm](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/MorphingInV06IMERG.pdf)\\n\\n* [Remote-Sensing Reflectance](https://gpm1.gesdisc.eosdis.nasa.gov/data/doc/README.GPM.pdf)\\n\\n* [Anomalies](ftp://gpmweb2.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html)\\n\\n\\nThis collection contains data from\\n[GPM_3IMERGM_06](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGM_V06/summary)\\n","license":"proprietary","providers":[{"name":"NASA GES DISC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V06"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-06-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_MONTHLY_V06"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GPM_IMERG_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V06#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GPM_L3/IMERG_V06","title":"GPM: Global Precipitation Measurement (GPM) v6","description":"Global Precipitation Measurement (GPM) is an international\\nsatellite mission to provide next-generation observations of\\nrain and snow worldwide every three hours. The Integrated Multi-satellitE\\nRetrievals for GPM (IMERG) is the unified algorithm that provides\\nrainfall estimates combining data from all passive-microwave\\ninstruments in the GPM Constellation.\\n\\nThis algorithm is intended to intercalibrate, merge, and interpolate\\nall satellite microwave precipitation estimates, together with\\nmicrowave-calibrated infrared (IR) satellite estimates, precipitation\\ngauge analyses, and potentially other precipitation estimators at fine\\ntime and space scales for the TRMM and GPM eras over the entire globe.\\nThe system is run several times for each observation time, first\\ngiving a quick estimate and successively providing better estimates\\nas more data arrive. The final step uses monthly gauge data to\\ncreate research-level products. See [IMERG Technical Documentation](https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf)\\nfor more details on the algorithm.\\n\\nDocumentation:\\n* [Algorithm Theoretical Basis Document](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V06.pdf)\\n\\n* [IMERG Quality Index](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf)\\n\\n* [Caveats for IMERG extension into TRMM era](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_TRMMera-caveats.pdf)\\n\\n* [IMERG Technical Documentation](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_doc.06.pdf)\\n\\n* [Release notes; New Morphing algorithm](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/MorphingInV06IMERG.pdf)\\n\\n* [Remote-Sensing Reflectance](https://gpm1.gesdisc.eosdis.nasa.gov/data/doc/README.GPM.pdf)\\n\\n* [Anomalies](ftp://gpmweb2.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html)\\n\\n\\nThis collection contains provisional\\nproducts that are regularly replaced with updated versions when\\nthe data become available. The products are marked with a metadata\\nproperty called \'status\'. When a product is initially made available,\\nthe property value is \'provisional\'. Once a provisional product\\nhas been updated with the final version, this value is updated\\nto \'permanent\'.\\n\\nThis collection contains data from:\\n\\n* GPM_3IMERGHH_V06 [doi:10.5067/GPM/IMERG/3B-HH-L/06](https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06)\\n* GPM_3IMERGHH_06 [doi:10.5067/GPM/IMERG/3B-HH/06](https://doi.org/10.5067/GPM/IMERG/3B-HH/06)\\n","license":"proprietary","providers":[{"name":"NASA GES DISC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/GPM/IMERG/3B-HH/06"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V06"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-06-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_V06"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GPM_IMERG_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V06#terms-of-use"},{"rel":"source","href":"ftp://198.118.195.100/gpmallversions/V06"},{"rel":"source","href":"ftp://198.118.195.61/data/imerg/late/"},{"rel":"cite-as","href":"https://doi.org/10.5067/GPM/IMERG/3B-HH/06"},{"rel":"cite-as","href":"https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GRACE/MASS_GRIDS/LAND","title":"GRACE Monthly Mass Grids - Land","description":"GRACE Tellus Monthly Mass Grids provides monthly\\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\\nThe data contained in this dataset are units of \\"Equivalent Water Thickness\\"\\nwhich represent the deviations of mass in terms of vertical extent\\nof water in centimeters. See the provider\'s [Monthly Mass\\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\\nfor more details.\\n\\n\\nThe GRACE Tellus (GRCTellus) Monthly Mass Grids dataset is produced\\nby three centers: CSR (U. Texas / Center for Space Research),\\nGFZ (GeoForschungsZentrum Potsdam), and JPL\\n(NASA Jet Propulsion Laboratory). Each center is a part\\nof the GRACE Ground System and generates Level-2 data\\n(spherical harmonic fields) used in this dataset.\\nThe output includes spherical harmonic coefficients\\nof the gravity field and of the dealiasing fields used to compute them.\\nSince each center independently produces the coefficients, the results\\nmay be slightly different. It is recommended for most users to\\nuse the mean of all three datasets. See the provider\'s [choosing a\\nsolution](https://grace.jpl.nasa.gov/data/choosing-a-solution/) page\\nfor more details.\\n\\n**Note**\\n\\n* Due to the sampling and post-processing of GRACE observations,\\n surface mass variations at small spatial scales tend to be attenuated.\\n Therefore, users should multiply the GRCTellus Land data by the\\n scaling grid available at\\n [NASA/GRACE/MASS_GRIDS/LAND_AUX_2014](https://code.earthengine.google.com/?asset=NASA/GRACE/MASS_GRIDS/LAND_AUX_2014).\\n\\n* The GRCTellus Land grids processed from the Spherical Harmonic\\n Level-2 data are not suited to accurately quantify ice mass\\n changes over Greenland or Antarctica, or glaciers and ice caps.\\n For those areas it is recommended to use JPL\'s mascon\\n solution available as the following image collection:\\n [NASA/GRACE/MASS_GRIDS/MASCON](https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON)\\n","license":"proprietary","providers":[{"name":"NASA Jet Propulsion Laboratory","roles":["producer","licensor"],"url":"https://grace.jpl.nasa.gov/data/monthly-mass-grids/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_LAND"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-04-01T00:00:00Z","2017-02-03T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/LAND"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GRACE_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_LAND#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1029/2011WR011453"},{"rel":"cite-as","href":"https://doi.org/10.1029/2005GL025285"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GRACE/MASS_GRIDS/MASCON","title":"GRACE Monthly Mass Grids - Global Mascons","description":"GRACE Tellus Monthly Mass Grids provides monthly\\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\\nThe data contained in this dataset are units of \\"Equivalent Water Thickness\\"\\nwhich represent the deviations of mass in terms of vertical extent\\nof water in centimeters. See the provider\'s [Monthly Mass\\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\\nfor more details.\\n\\n\\nThe GRACE Tellus (GRCTellus) Global Mascon dataset\\nis based on Level-1 GRACE observations and processed\\nat NASA Jet Propulsion Laboratory (JPL).\\n\\nThis dataset uses a-priori constraints in space and time to\\nestimate global, monthly gravity fields in terms of equal-area\\n3°x3° spherical cap mass concentration (mascon) functions to minimize the\\neffect of measurement errors. No additional empirical destriping\\nfilter has been applied to the data. This results in better S/N\\nratios of the mascon fields compared to the conventional\\nspherical-harmonic solutions.\\n\\n**Note**\\n\\n* Mascons which lie on coastlines contain mixed land and ocean signals.\\n A version of this dataset with the Coastline Resolution Improvement\\n (CRI) filter applied to separate the land and ocean portions of mass\\n within each land/ocean mascon in a post-processing step. This\\n dataset is available at\\n [NASA/GRACE/MASS_GRIDS/MASCON_CRI](https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON_CRI).\\n\\n* The data are represented on a 1/2 degree lon-lat grid, but they\\n represent the 3x3 degree equal-area caps, which is the current\\n native resolution of JPL-RL05M.\\n","license":"proprietary","providers":[{"name":"NASA Jet Propulsion Laboratory","roles":["producer","licensor"],"url":"https://grace.jpl.nasa.gov/data/monthly-mass-grids/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-04-01T00:00:00Z","2017-02-03T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/MASCON"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GRACE_MASCON_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1002/2014JB011547"},{"rel":"cite-as","href":"https://doi.org/10.1002/2016WR019344"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GRACE/MASS_GRIDS/MASCON_CRI","title":"GRACE Monthly Mass Grids - Global Mascon (CRI Filtered)","description":"GRACE Tellus Monthly Mass Grids provides monthly\\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\\nThe data contained in this dataset are units of \\"Equivalent Water Thickness\\"\\nwhich represent the deviations of mass in terms of vertical extent\\nof water in centimeters. See the provider\'s [Monthly Mass\\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\\nfor more details.\\n\\n\\nThe GRACE Tellus (GRCTellus) Global Mascon dataset\\nis based on Level-1 GRACE observations and processed\\nat NASA Jet Propulsion Laboratory (JPL).\\n\\nThis dataset uses a-priori constraints in space and time to\\nestimate global, monthly gravity fields in terms of equal-area\\n3°x3° spherical cap mass concentration (mascon) functions to minimize the\\neffect of measurement errors. This results in better S/N\\nratios of the mascon fields compared to the conventional\\nspherical-harmonic solutions.\\n\\nThis version of the Global Mascon dataset has a Coastline Resolution\\nImprovement (CRI) filter applied to separate the land and ocean\\nportions of mass within each land/ocean mascon in a post-processing step.\\nFor users who wish to do their own separation of land and ocean mass\\nsignals, a version of the data without the CRI filter is available\\nat [NASA/GRACE/MASS_GRIDS/MASCON](https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON).\\n\\n**Note**\\n\\n* A set of global gain factors to aid in the interpretation of signals\\n at sub-mascon resolution is available at\\n [NASA/GRACE/MASS_GRIDS/MASCON_SCALE_CRI_2015](https://code.earthengine.google.com/?asset=NASA/GRACE/MASS_GRIDS/MASCON_SCALE_CRI_2015).\\n **Please note**: the gain factors can be used for hydrology-related\\n signals, but not for mountain glaciers or ice sheets. For more\\n information on scaling the mascon solutions, please see [Wiese et al.\\n (2016)](https://doi.org/10.1002/2016WR019344).\\n\\n* The data are represented on a 1/2 degree lon-lat grid, but they\\n represent the 3x3 degree equal-area caps, which is the current\\n native resolution of JPL-RL05M.\\n","license":"proprietary","providers":[{"name":"NASA Jet Propulsion Laboratory","roles":["producer","licensor"],"url":"https://grace.jpl.nasa.gov/data/monthly-mass-grids/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON_CRI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-03-31T00:00:00Z","2017-06-28T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/MASCON_CRI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GRACE_MASCON_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON_CRI#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1002/2014JB011547"},{"rel":"cite-as","href":"https://doi.org/10.1002/2016WR019344"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GRACE/MASS_GRIDS/OCEAN","title":"GRACE Monthly Mass Grids - Ocean","description":"GRACE Tellus Monthly Mass Grids provides monthly\\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\\nThe data contained in this dataset are units of \\"Equivalent Water Thickness\\"\\nwhich represent the deviations of mass in terms of vertical extent\\nof water in centimeters. See the provider\'s [Monthly Mass\\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\\nfor more details.\\n\\n\\nThe GRACE Tellus (GRCTellus) Monthly Mass Grids Ocean dataset is produced\\nby three centers: CSR (U. Texas / Center for Space Research),\\nGFZ (GeoForschungsZentrum Potsdam), and JPL\\n(NASA Jet Propulsion Laboratory). Each center is a part\\nof the GRACE Ground System and generates Level-2 data\\n(spherical harmonic fields) used in this dataset.\\nThe output includes spherical harmonic coefficients\\nof the gravity field and of the dealiasing fields used to compute them.\\nSince each center independently produces the coefficients, the results\\nmay be slightly different. It is recommended for most users to\\nuse the mean of all three datasets. See the provider\'s [choosing a\\nsolution](https://grace.jpl.nasa.gov/data/choosing-a-solution/) page\\nfor more details.\\n\\n**Note**\\n\\n* Land leakage correction: Ocean signals are typically weaker\\n than land signals, by factors of 2 and more, on seasonal and\\n interannual time scales. To minimize leakage from high land\\n signals onto ocean signals, a destriping filter has been applied\\n which may cause cause correlations over much larger distances.\\n\\n* The GRCTellus Ocean datasets are optimized to examine regional\\n ocean bottom pressure, but NOT intended to be spatially\\n averaged to determine global mean ocean mass.\\n\\n* A filtered version of the GRCTellus Ocean dataset is also available\\n which uses an alternative filtering method which may reduce leakage\\n errors, and have results which agree better with altimetric sea surface\\n height. [NASA/GRACE/MASS_GRIDS/OCEAN_EOFR](https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_OCEAN_EOFR).\\n","license":"proprietary","providers":[{"name":"NASA Jet Propulsion Laboratory","roles":["producer","licensor"],"url":"https://grace.jpl.nasa.gov/data/monthly-mass-grids/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_OCEAN"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-31T00:00:00Z","2017-01-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/OCEAN"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GRACE_OCEAN_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_OCEAN#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1175/2010JTECHO738.1"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/GRACE/MASS_GRIDS/OCEAN_EOFR","title":"GRACE Monthly Mass Grids - Ocean EOFR","description":"GRACE Tellus Monthly Mass Grids provides monthly\\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\\nThe data contained in this dataset are units of \\"Equivalent Water Thickness\\"\\nwhich represent the deviations of mass in terms of vertical extent\\nof water in centimeters. See the provider\'s [Monthly Mass\\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\\nfor more details.\\n\\n\\nThis dataset is a filtered version of the GRACE Tellus (GRCTellus)\\nOcean dataset. The \'EOFR\' bottom pressure (OBP) grids are obtained by\\nprojecting the data from the regular GRC Ocean grids product\\nonto the Empirical Orthogonal Functions (EOFs) of the\\nOcean Model for Circulation and Tides (OMCT). This effectively\\nfilters out signals in the GRACE data that are inconsistent\\nwith the physics and OBP variations in the OMCT ocean model.\\n\\nThe EOFR filtered reconstructed OBP fields agree\\nbetter with radar altimetric sea surface height,\\nreduce leakage around ice sheets and glaciers, and reduce\\nnoise in the mid latitudes where OBP variability is lower.\\n[(Chambers and Willis, 2010)](https://doi.org/10.1175/2010JTECHO738.1)\\n\\n**Note**\\n\\n* The GRCTellus Ocean datasets are optimized to examine regional\\n ocean bottom pressure, but NOT intended to be spatially\\n averaged to determine global mean ocean mass.\\n","license":"proprietary","providers":[{"name":"NASA Jet Propulsion Laboratory","roles":["producer","licensor"],"url":"https://grace.jpl.nasa.gov/data/monthly-mass-grids/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_OCEAN_EOFR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-12-31T00:00:00Z","2017-01-05T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/OCEAN_EOFR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GRACE_OCEAN_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_OCEAN_EOFR#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5194/os"},{"rel":"cite-as","href":"https://doi.org/10.1175/2010JTECHO738.1"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/JPL/global_forest_canopy_height_2005","title":"Global Forest Canopy Height, 2005","description":"This dataset represents global tree heights based on a fusion of\\nspaceborne-lidar data (2005) from the Geoscience Laser Altimeter\\nSystem (GLAS) and ancillary geospatial data. See [Simard et al. (2011)]\\n(https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011JG001708)\\nfor details.\\n","license":"proprietary","providers":[{"name":"NASA/JPL","roles":["producer","licensor"],"url":"https://earthobservatory.nasa.gov"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_JPL_global_forest_canopy_height_2005"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2005-05-20T00:00:00Z","2005-06-23T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/JPL/global_forest_canopy_height_2005"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_JPL_global_forest_canopy_height_2005_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_JPL_global_forest_canopy_height_2005#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/MEASURES/GFCC/TC/v3","title":"Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m","description":"The Landsat Vegetation Continuous Fields (VCF) tree cover layers contain\\nestimates of the percentage of horizontal ground in each 30-m pixel covered\\nby woody vegetation greater than 5 meters in height. The dataset is available\\nfor four epochs centered on the years 2000, 2005, 2010 and 2015. The dataset\\nis derived from the GFCC Surface Reflectance product (GFCC30SR), which is\\nbased on enhanced Global Land Survey (GLS) datasets. The GLS datasets are\\ncomposed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7\\nEnhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution.\\n\\nTree cover, the proportional, vertically projected area of vegetation\\n(including leaves, stems, branches, etc.) of woody plants above a given\\nheight, affects terrestrial energy and water exchanges, photosynthesis and\\ntranspiration, net primary production, and carbon and nutrient fluxes. Tree\\ncover also affects habitat quality and movements of wildlife, residential\\nproperty value for humans, and other ecosystem services. The continuous\\nclassification scheme of the VCF product enables better depiction of land\\ncover gradients than traditional discrete classification schemes.\\nImportantly for detection and monitoring of forest changes (e.g.,\\ndeforestation and degradation), tree cover provides a measurable attribute\\nupon which to define forest cover and its changes. Changes in tree cover\\nover time can be used to monitor and retrieve site-specific histories of\\nforest change.\\n\\nThe dataset has been produced for four year epochs: 2000, 2005, 2010, and\\n2015 with an image in the collection for each available WRS2 path/row.\\n\\nDocumentation:\\n\\n* [User\'s guide](https://lpdaac.usgs.gov/documents/145/GFCC_User_Guide_V1.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2015-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/MEASURES/GFCC/TC/v3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_MEASURES_GFCC_TC_v3_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/NASADEM_HGT/001","title":"NASADEM: NASA NASADEM Digital Elevation 30m","description":"NASADEM is a reprocessing of STRM data, with improved accuracy by incorporating auxiliary data\\nfrom ASTER GDEM, ICESat GLAS, and PRISM datasets.\\n\\nThe most significant processing improvements involve void reduction through improved\\nphase unwrapping and using ICESat GLAS data for control.\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/592/NASADEM_User_Guide_V1.pdf)\\n","license":"proprietary","providers":[{"name":"NASA / USGS / JPL-Caltech","roles":["producer","licensor"],"url":"https://cmr.earthdata.nasa.gov/search/concepts/C1546314043-LPDAAC_ECS.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NASADEM_HGT_001"}],"extent":{"spatial":{"bbox":[[-180,-56,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NASADEM_HGT/001"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASADEM_HGT_001_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NASADEM_HGT_001#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/NEX-DCP30","title":"NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections","description":"The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios\\nfor the conterminous United States that are derived from the General\\nCirculation Model (GCM) runs conducted under the Coupled Model\\nIntercomparison Project Phase 5 (CMIP5, see [Taylor et al. 2012](https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1))\\nand across the four greenhouse gas emissions scenarios known as\\nRepresentative Concentration Pathways (RCPs, see [Meinshausen et al. 2011](https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1))\\ndeveloped for the Fifth Assessment Report of the Intergovernmental Panel\\non Climate Change (IPCC AR5). The purpose of these datasets is to provide\\na set of high resolution, bias-corrected climate change projections that\\ncan be used to evaluate climate change impacts on processes that are\\nsensitive to finer-scale climate gradients and the effects of local\\ntopography on climate conditions.\\n\\nThe dataset contains monthly projections covering the periods from 1950\\nthrough 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).\\nIt includes downscaled projections from 33 models. Not every\\nscenario contains projections from every model.\\n\\nNEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames\\nResearch Center using the NASA Earth Exchange, and distributed by the NASA\\nCenter for Climate Simulation (NCCS).\\n","license":"proprietary","providers":[{"name":"NASA / CSU","roles":["producer","licensor"],"url":"https://cds.nccs.nasa.gov/nex/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30"}],"extent":{"spatial":{"bbox":[[-125.03,24.07,-66.47,53.74]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2099-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-DCP30"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_NEX-DCP30_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/NEX-DCP30_ENSEMBLE_STATS","title":"NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections","description":"The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios\\nfor the conterminous United States that are derived from the General\\nCirculation Model (GCM) runs conducted under the Coupled Model\\nIntercomparison Project Phase 5 (CMIP5, see [Taylor et al. 2012](https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1))\\nand across the four greenhouse gas emissions scenarios known as\\nRepresentative Concentration Pathways (RCPs, see [Meinshausen et al. 2011](https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1))\\ndeveloped for the Fifth Assessment Report of the Intergovernmental Panel\\non Climate Change (IPCC AR5). The purpose of these datasets is to provide\\na set of high resolution, bias-corrected climate change projections that\\ncan be used to evaluate climate change impacts on processes that are\\nsensitive to finer-scale climate gradients and the effects of local\\ntopography on climate conditions.\\n\\nThe dataset contains monthly projections covering the periods from 1950\\nthrough 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).\\nIt includes ensemble statistics calculated for each RCP from all model\\nruns available for the pr, tasmin, and tasmax bands.\\n\\nNEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames\\nResearch Center using the NASA Earth Exchange, and distributed by the NASA\\nCenter for Climate Simulation (NCCS).\\n","license":"proprietary","providers":[{"name":"NASA / CSU","roles":["producer","licensor"],"url":"https://cds.nccs.nasa.gov/nex/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30_ENSEMBLE_STATS"}],"extent":{"spatial":{"bbox":[[-125.03,24.07,-66.47,49.93]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2099-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-DCP30_ENSEMBLE_STATS"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_NEX-DCP30_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30_ENSEMBLE_STATS#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/NEX-GDDP","title":"NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections","description":"The NASA NEX-GDDP dataset is comprised of downscaled climate scenarios\\nfor the globe that are derived from the General Circulation Model (GCM)\\nruns conducted under the Coupled Model Intercomparison Project Phase 5\\n(CMIP5, see [Taylor et al. 2012](https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1))\\nand across two of the four greenhouse gas emissions scenarios known as\\nRepresentative Concentration Pathways (RCPs, see [Meinshausen et\\nal. 2011](https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1)).\\nThe CMIP5 GCM runs were developed in support of the Fifth Assessment\\nReport of the Intergovernmental Panel on Climate Change (IPCC AR5).\\n\\nThis dataset was prepared by the Climate Analytics Group and NASA Ames\\nResearch Center using the NASA Earth Exchange, and distributed by the NASA\\nCenter for Climate Simulation (NCCS).\\n","license":"proprietary","providers":[{"name":"NASA / CSU","roles":["producer","licensor"],"url":"https://cds.nccs.nasa.gov/nex-gddp/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-GDDP"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2099-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-GDDP"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_NEX-GDDP_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-GDDP#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/NLDAS/FORA0125_H002","title":"NLDAS-2: North American Land Data Assimilation System Forcing Fields","description":"\'Land Data Assimilation System (LDAS) combines multiple\\nsources of observations (such as precipitation gauge data, satellite\\ndata, and radar precipitation measurements) to produce estimates\\nof climatological properties at or near the Earth\'\'s surface.\\n\\nThis dataset is the primary (default) forcing file (File A) for\\nPhase 2 of the North American Land Data Assimilation System (NLDAS-2).\\nThe data are in 1/8th-degree grid spacing; the temporal resolution\\nis hourly.\\n\\nNLDAS is a collaboration project among several groups: NOAA/NCEP\'\'s\\nEnvironmental Modeling Center (EMC), NASA\'\'s Goddard Space Flight\\nCenter (GSFC), Princeton University, the University of Washington,\\nthe NOAA/NWS Office of Hydrological Development (OHD), and the\\nNOAA/NCEP Climate Prediction Center (CPC). NLDAS is a core project\\nwith support from NOAA\'\'s Climate Prediction Program for the Americas\\n(CPPA).\'\\n\\nDocumentation:\\n\\n* [Readme](https://hydro1.gesdisc.eosdis.nasa.gov/data/NLDAS/README.NLDAS2.pdf)\\n\\n* [How-To](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\\n\\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\\n\\n* [GES DISC Data Rods Documentation](https://disc.gsfc.nasa.gov/information/tools?title=Hydrology%20Data%20Rods)\\n","license":"proprietary","providers":[{"name":"NASA GES DISC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/6J5LHHOHZHN4"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002"}],"extent":{"spatial":{"bbox":[[-125.15,24.85,-66.85,53.28]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NLDAS/FORA0125_H002"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_NLDAS_FORA0125_H002_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002#terms-of-use"},{"rel":"source","href":"https://disc.sci.gsfc.nasa.gov/datasets?keywords=NLDAS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/OCEANDATA/MODIS-Aqua/L3SMI","title":"Ocean Color SMI: Standard Mapped Image MODIS Aqua Data","description":"This level 3 product includes ocean color and satellite ocean biology data\\nproduced or collected under [EOSDIS](https://earthdata.nasa.gov/about).\\n\\nThis dataset may be used for studying the biology and hydrology of\\ncoastal zones, changes in the diversity and geographical distribution of\\ncoastal marine habitats, biogeochemical fluxes and their influence in\\nEarth\'s oceans and climate over time, and finally the impact of\\nclimate and environmental variability and change on ocean ecosystems\\nand the biodiversity they support.\\n\\nScale factor and offset are already applied.\\n\\nDocumentation:\\n\\n* [Ocean Color Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\\n\\n* [Chlorophyll Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\\n\\n* [Algorithm Theoretical Basis Document (Chlorophyll)](https://oceancolor.gsfc.nasa.gov/atbd/chlor_a)\\n\\n* [Algorithm Theoretical Basis Document (Fluorescence Line Height)](https://oceancolor.gsfc.nasa.gov/atbd/nflh)\\n\\n* [Algorithm Theoretical Basis Document (Particulate Organic Carbon)](https://oceancolor.gsfc.nasa.gov/atbd/poc)\\n\\n* [Algorithm Theoretical Basis Document (Remote-Sensing Reflectance)](https://oceancolor.gsfc.nasa.gov/atbd/rrs)\\n\\n* [Processing History](https://oceancolor.gsfc.nasa.gov/reprocessing)\\n","license":"proprietary","providers":[{"name":"NASA OB.DAAC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://oceancolor.gsfc.nasa.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/MODIS-Aqua/L3SMI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_OCEANDATA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/AQUA/MODIS/L3M/FLH/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/AQUA/MODIS/L3M/POC/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/AQUA/MODIS/L3M/RRS/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/GHMDA-2PJ19"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/OCEANDATA/MODIS-Terra/L3SMI","title":"Ocean Color SMI: Standard Mapped Image MODIS Terra Data","description":"This level 3 product includes ocean color and satellite ocean biology data\\nproduced or collected under [EOSDIS](https://earthdata.nasa.gov/about).\\n\\nThis dataset may be used for studying the biology and hydrology of\\ncoastal zones, changes in the diversity and geographical distribution of\\ncoastal marine habitats, biogeochemical fluxes and their influence in\\nEarth\'s oceans and climate over time, and finally the impact of\\nclimate and environmental variability and change on ocean ecosystems\\nand the biodiversity they support.\\n\\nScale factor and offset are already applied.\\n\\nDocumentation:\\n\\n* [Ocean Color Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\\n\\n* [Chlorophyll Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\\n\\n* [Algorithm Theoretical Basis Document (Chlorophyll)](https://oceancolor.gsfc.nasa.gov/atbd/chlor_a)\\n\\n* [Algorithm Theoretical Basis Document (Fluorescence Line Height)](https://oceancolor.gsfc.nasa.gov/atbd/nflh)\\n\\n* [Algorithm Theoretical Basis Document (Particulate Organic Carbon)](https://oceancolor.gsfc.nasa.gov/atbd/poc)\\n\\n* [Algorithm Theoretical Basis Document (Remote-Sensing Reflectance)](https://oceancolor.gsfc.nasa.gov/atbd/rrs)\\n\\n* [Processing History](https://oceancolor.gsfc.nasa.gov/reprocessing)\\n\\n\\n* [MODIS-Terra OceanData](https://oceancolor.gsfc.nasa.gov/data/terra)\\n","license":"proprietary","providers":[{"name":"NASA OB.DAAC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://oceancolor.gsfc.nasa.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Terra_L3SMI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/MODIS-Terra/L3SMI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_OCEANDATA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Terra_L3SMI#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/TERRA/MODIS/L3M/CHL/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/TERRA/MODIS/L3M/FLH/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/TERRA/MODIS/L3M/POC/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/TERRA/MODIS/L3M/RRS/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/GHMDT-2PJ19"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/OCEANDATA/SeaWiFS/L3SMI","title":"Ocean Color SMI: Standard Mapped Image SeaWiFS Data","description":"This level 3 product includes ocean color and satellite ocean biology data\\nproduced or collected under [EOSDIS](https://earthdata.nasa.gov/about).\\n\\nThis dataset may be used for studying the biology and hydrology of\\ncoastal zones, changes in the diversity and geographical distribution of\\ncoastal marine habitats, biogeochemical fluxes and their influence in\\nEarth\'s oceans and climate over time, and finally the impact of\\nclimate and environmental variability and change on ocean ecosystems\\nand the biodiversity they support.\\n\\nScale factor and offset are already applied.\\n\\nDocumentation:\\n\\n* [Ocean Color Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\\n\\n* [Chlorophyll Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\\n\\n* [Algorithm Theoretical Basis Document (Chlorophyll)](https://oceancolor.gsfc.nasa.gov/atbd/chlor_a)\\n\\n* [Algorithm Theoretical Basis Document (Fluorescence Line Height)](https://oceancolor.gsfc.nasa.gov/atbd/nflh)\\n\\n* [Algorithm Theoretical Basis Document (Particulate Organic Carbon)](https://oceancolor.gsfc.nasa.gov/atbd/poc)\\n\\n* [Algorithm Theoretical Basis Document (Remote-Sensing Reflectance)](https://oceancolor.gsfc.nasa.gov/atbd/rrs)\\n\\n* [Processing History](https://oceancolor.gsfc.nasa.gov/reprocessing)\\n","license":"proprietary","providers":[{"name":"NASA OB.DAAC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://oceancolor.gsfc.nasa.gov/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_SeaWiFS_L3SMI"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1997-09-04T00:00:00Z","2010-12-11T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/SeaWiFS/L3SMI"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_OCEANDATA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_SeaWiFS_L3SMI#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/ORBVIEW-2/SEAWIFS/L3M/POC/2018"},{"rel":"cite-as","href":"https://doi.org/10.5067/ORBVIEW-2/SEAWIFS/L3M/RRS/2018"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/ORNL/DAYMET_V3","title":"Daymet V3: Daily Surface Weather and Climatological Summaries","description":"Daymet V3 provides gridded estimates of daily weather parameters for\\nUnited States, Mexico, Canada, Hawaii, and Puerto Rico. It is derived\\nfrom selected meteorological station data and various supporting data\\nsources.\\n\\nCompared to the previous version, Daymet V3 uses an entirely new suite\\nof inputs including:\\n\\n- NASA SRTM DEM version 2.1.\\n\\n- Land/Water Mask: MODIS 250 `MOD44W_v2.NASA_ORNL_`\\n\\n- Horizon files derived from the SRTM DEM.\\n\\n- Ground station weather inputs from several sources with QA/QC.\\n\\nDocumentation:\\n\\n* [ORNL DAAC Dataset Documentation](https://daac.ornl.gov/DAYMET/guides/Daymet_V3_CFMosaics.html)\\n\\n* [Dataset Documentation](https://daac.ornl.gov/daacdata/daymet/Daymet_V3_CFMosaics/comp/Daymet_V3_CFMosaics.pdf)\\n\\n* [The THREDDS location for this Collection](https://thredds.daac.ornl.gov/thredds/catalog/ornldaac/1328/catalog.html)\\n","license":"proprietary","providers":[{"name":"NASA ORNL DAAC at Oak Ridge National Laboratory","roles":["producer","licensor"],"url":"https://doi.org/10.3334/ORNLDAAC/1328"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V3"}],"extent":{"spatial":{"bbox":[[-150.8,1.6,-1.1,84]]},"temporal":{"interval":[["1980-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/DAYMET_V3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_ORNL_DAYMET_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V3#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.3334/ORNLDAAC/1328"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA/ORNL/biomass_carbon_density/v1","title":"Global Aboveground and Belowground Biomass Carbon Density Maps","description":"This dataset provides temporally consistent and harmonized global maps of aboveground and\\nbelowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The\\naboveground biomass map integrates land-cover specific, remotely sensed maps of woody,\\ngrassland, cropland, and tundra biomass. Input maps were amassed from the published literature\\nand, where necessary, updated to cover the focal extent or time period. The belowground biomass\\nmap similarly integrates matching maps derived from each aboveground biomass map and land-cover\\nspecific empirical models. Aboveground and belowground maps were then integrated separately\\nusing ancillary maps of percent tree cover and landcover and a rule-based decision tree. Maps\\nreporting the accumulated uncertainty of pixel-level estimates are also provided.\\n","license":"proprietary","providers":[{"name":"NASA ORNL DAAC at Oak Ridge National Laboratory","roles":["producer","licensor"],"url":"https://doi.org/10.3334/ORNLDAAC/1763"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_biomass_carbon_density_v1"}],"extent":{"spatial":{"bbox":[[-180,-61.1,180,84]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/biomass_carbon_density/v1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_ORNL_biomass_carbon_density_v1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_biomass_carbon_density_v1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.3334/ORNLDAAC/1763"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA_USDA/HSL/SMAP_soil_moisture","title":"NASA-USDA SMAP Global Soil Moisture Data","description":"The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil\\nmoisture datates provide soil moisture information across the globe at\\n0.25°x0.25° spatial resolution. These datasets include\\n[surface](ftp://gimms.gsfc.nasa.gov/SMOS/SMAP/Surface_Soil_Moisture_SMAP.pdf)\\nand [subsurface](ftp://gimms.gsfc.nasa.gov/SMOS/SMAP/Sub_SurfaceSoil_Moisture_SMAP.pdf)\\nsoil moisture (mm),\\n[soil moisture profile](ftp://gimms.gsfc.nasa.gov/SMOS/SMAP/SoilMoisture_Profile_SMAP.pdf) (%),\\nand surface and subsurface soil moisture anomalies. Soil moisture anomalies\\nare unitless and represent standardized\\nanomalies computed using a 31-days moving window. Values around 0\\nindicate typical moisture conditions, while very positive and very\\nnegative values indicate extreme wetting (soil moisture conditions are\\nabove average) and drying (soil moisture conditions are below average),\\nrespectively.\\n\\n\\nThis dataset is generated by integrating satellite-derived Soil Moisture\\nActive Passive (SMAP) Level 3 soil moisture observations into the modified\\ntwo-layer Palmer model using a 1-D Ensemble Kalman Filter (EnKF) data\\nassimilation approach. The assimilation of the SMAP soil moisture\\nobservations helped improve the model-based soil moisture predictions\\nparticularly over poorly instrumented areas of the world that lack good\\nquality precipitation data.\\n","license":"proprietary","providers":[{"name":"NASA GSFC","roles":["producer","licensor"],"url":"https://gimms.gsfc.nasa.gov/SMOS/SMAP/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture"}],"extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2015-04-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/SMAP_soil_moisture"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_USDA_HSL_soil_moisture_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1029/2012GL053470"},{"rel":"cite-as","href":"https://doi.org/10.1109/JSTARS.2016.2639338"},{"rel":"cite-as","href":"https://doi.org/10.3390/rs10081265"},{"rel":"cite-as","href":"https://doi.org/10.1109/JPROC.2010.2043918"},{"rel":"cite-as","href":"https://doi.org/10.5067/ZX7YX2Y2LHEB"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NASA_USDA/HSL/soil_moisture","title":"NASA-USDA Global Soil Moisture Data","description":"The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil\\nmoisture datates provide soil moisture information across the globe at\\n0.25°x0.25° spatial resolution. These datasets include\\n[surface](ftp://gimms.gsfc.nasa.gov/SMOS/SMAP/Surface_Soil_Moisture_SMAP.pdf)\\nand [subsurface](ftp://gimms.gsfc.nasa.gov/SMOS/SMAP/Sub_SurfaceSoil_Moisture_SMAP.pdf)\\nsoil moisture (mm),\\n[soil moisture profile](ftp://gimms.gsfc.nasa.gov/SMOS/SMAP/SoilMoisture_Profile_SMAP.pdf) (%),\\nand surface and subsurface soil moisture anomalies. Soil moisture anomalies\\nare unitless and represent standardized\\nanomalies computed using a 31-days moving window. Values around 0\\nindicate typical moisture conditions, while very positive and very\\nnegative values indicate extreme wetting (soil moisture conditions are\\nabove average) and drying (soil moisture conditions are below average),\\nrespectively.\\n\\n\\nThis dataset is generated by integrating\\nsatellite-derived Soil Moisture Ocean Salinity (SMOS) Level 2 soil moisture\\nobservations into the modified two-layer Palmer model using a 1-D Ensemble\\nKalman Filter (EnKF) data assimilation approach. The assimilation of the\\nSMOS soil moisture observations helped improve the model-based soil\\nmoisture predictions particularly over poorly instrumented areas\\n(e.g., Southern African, Middle East) of the world that lack good quality\\nprecipitation data.\\n","license":"proprietary","providers":[{"name":"NASA GSFC","roles":["producer","licensor"],"url":"https://gimms.gsfc.nasa.gov/SMOS/jbolten/FAS/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture"}],"extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2010-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/soil_moisture"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NASA_USDA_HSL_soil_moisture_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1029/2012GL053470"},{"rel":"cite-as","href":"https://doi.org/10.1109/JSTARS.2016.2639338"},{"rel":"cite-as","href":"https://doi.org/10.3390/rs10081265"},{"rel":"cite-as","href":"https://doi.org/10.1109/TGRS.2008.917807"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NCEP_RE/sea_level_pressure","title":"NCEP/NCAR Reanalysis Data, Sea-Level Pressure","description":"The NCEP/NCAR Reanalysis Project is a joint project between the National\\nCenters for Environmental Prediction (NCEP, formerly \\"NMC\\") and the\\nNational Center for Atmospheric Research (NCAR). The goal of this joint\\neffort is to produce new atmospheric analyses using historical data as\\nwell as to produce analyses of the current atmospheric state (Climate Data\\nAssimilation System, CDAS). The NCEP/NCAR Reanalysis 1 project is using a\\nstate-of-the-art analysis/forecast system to perform data assimilation using\\npast data from 1948 to the present. The data have 6-hour temporal\\nresolution (0000, 0600, 1200, and 1800 UTC) and 2.5 degree spatial\\nresolution.\\n","license":"proprietary","providers":[{"name":"NCEP / NCAR","roles":["producer","licensor"],"url":"http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1948-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/sea_level_pressure"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NCEP_RE_sea_level_pressure_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NCEP_RE/surface_temp","title":"NCEP/NCAR Reanalysis Data, Surface Temperature","description":"The NCEP/NCAR Reanalysis Project is a joint project between the National\\nCenters for Environmental Prediction (NCEP, formerly \\"NMC\\") and the\\nNational Center for Atmospheric Research (NCAR). The goal of this joint\\neffort is to produce new atmospheric analyses using historical data as\\nwell as to produce analyses of the current atmospheric state (Climate Data\\nAssimilation System, CDAS). The NCEP/NCAR Reanalysis 1 project is using a\\nstate-of-the-art analysis/forecast system to perform data assimilation using\\npast data from 1948 to the present. The data have 6-hour temporal\\nresolution (0000, 0600, 1200, and 1800 UTC) and 2.5 degree spatial\\nresolution.\\n","license":"proprietary","providers":[{"name":"NCEP / NCAR","roles":["producer","licensor"],"url":"http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1948-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/surface_temp"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NCEP_RE_surface_temp_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NCEP_RE/surface_wv","title":"NCEP/NCAR Reanalysis Data, Water vapor","description":"The NCEP/NCAR Reanalysis Project is a joint project between the National\\nCenters for Environmental Prediction (NCEP, formerly \\"NMC\\") and the\\nNational Center for Atmospheric Research (NCAR). The goal of this joint\\neffort is to produce new atmospheric analyses using historical data as\\nwell as to produce analyses of the current atmospheric state (Climate Data\\nAssimilation System, CDAS). The NCEP/NCAR Reanalysis 1 project is using a\\nstate-of-the-art analysis/forecast system to perform data assimilation using\\npast data from 1948 to the present. The data have 6-hour temporal\\nresolution (0000, 0600, 1200, and 1800 UTC) and 2.5 degree spatial\\nresolution.\\n","license":"proprietary","providers":[{"name":"NCEP / NCAR","roles":["producer","licensor"],"url":"http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1948-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/surface_wv"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NCEP_RE_surface_wv_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/ATMOS_NEAR_SURFACE/V2","title":"NOAA CDR: Ocean Near-Surface Atmospheric Properties, Version 2","description":"The Ocean Near-Surface Atmospheric Properties dataset is part of the NOAA\\nOcean Surface Bundle (OSB) and provides a high quality Climate Data Record\\n(CDR) of air temperature, wind speed, and specific humidity over ice-free\\nocean surfaces.\\n\\nThese atmospheric properties are calculated based on brightness temperature\\ndata from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor\\nMicrowave/Imager Sounder (SSMIS) on the Defense Meteorological Satellite\\nProgram (DMSP) spacecraft, using a neural network.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/atmospheric/ocean-near-surface-atmospheric-properties"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_ATMOS_NEAR_SURFACE_V2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1988-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/ATMOS_NEAR_SURFACE/V2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/ATMOS_NEAR_SURFACE_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_ATMOS_NEAR_SURFACE_V2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V55T3HH0"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/AVHRR/AOT/V3","title":"NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v03","description":"The NOAA Climate Data Record (CDR) of Aerosol Optical Thickness (AOT) is a\\ncollection of global daily 0.1 degree derived data from the PATMOS-x AVHRR\\nlevel-2b channel 1 (0.63 micron) orbital clear-sky radiance. The aerosol\\nproduct is generated from AVHRR imagery in cloud-free conditions during\\ndaytime over oceans.\\n\\nDue to the relatively large uncertainties associated with surface\\nreflectance over water glint area and land surface as well as limited\\nAVHRR retrieval channels, this dataset only includes retrieval over\\nnon-glint water surface (specifically at the anti-solar side of the orbit\\nwith viewing angle more than 40 degree away from the specular ray). For\\nmore details, see the [Algorithm Description](https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/Aerosol_Optical_Thickness/AlgorithmDescription.pdf).\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/atmospheric/avhrr-aerosol-optical-thickness"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V3"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/AOT/V3"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/AVHRR_AOT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V3#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5BZ642P"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/AVHRR/LAI_FAPAR/V5","title":"NOAA CDR AVHRR LAI FAPAR: Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation, Version 5","description":"The NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and\\nFraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset\\ncontains derived values that characterize the canopy and photosynthetic\\nactivity of plants. This dataset is derived from the NOAA AVHRR Surface\\nReflectance product and is gridded at a resolution of 0.05° on a daily\\nbasis. The values are computed globally over land surfaces, but not over\\nbare or very sparsely vegetated areas, permanent ice or snow, permanent\\nwetland, urban areas, or water bodies.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/terrestrial/leaf-area-index-and-fapar"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_LAI_FAPAR_V5"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/LAI_FAPAR/V5"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/AVHRR_LAI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_LAI_FAPAR_V5#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5M043BX"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/AVHRR/NDVI/V5","title":"NOAA CDR AVHRR NDVI: Normalized Difference Vegetation Index, Version 5","description":"The NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation\\nIndex (NDVI) contains gridded daily NDVI derived from the NOAA AVHRR Surface\\nReflectance product. It provides a measurement of surface vegetation\\ncoverage activity, gridded at a resolution of 0.05° and computed globally\\nover land surfaces.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/terrestrial/normalized-difference-vegetation-index"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/NDVI/V5"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/AVHRR_NDVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5PZ56R6"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/AVHRR/SR/V5","title":"NOAA CDR AVHRR: Surface Reflectance, Version 5","description":"The NOAA Climate Data Record (CDR) of AVHRR Surface Reflectance contains\\ngridded daily surface reflectance and brightness temperatures derived from\\nthe Advanced Very High Resolution Radiometer (AVHRR) sensors onboard seven\\nNOAA polar orbiting satellites. The data are gridded at a\\nresolution of 0.05° and computed globally over land surfaces.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/terrestrial/avhrr-surface-reflectance"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_SR_V5"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/SR/V5"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/AVHRR_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_SR_V5#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V53776Z4"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/GRIDSAT-B1/V2","title":"NOAA CDR GRIDSAT-B1: Geostationary IR Channel Brightness Temperature","description":"This dataset provides a high quality Climate Data\\nRecord (CDR) of global infrared measurements from geostationary satellites.\\n\\nThe geostationary satellite data (GridSat-B1) provides data from 3\\nchannels: the CDR-quality infrared window (IRWIN) channel (near 11μm),\\nthe visible channel (near 0.6μm) and the infrared water vapor (IRWVP)\\nchannel (near 6.7μm). The GridSat-B1 data is projected onto a global\\n0.07 degree latitude equal-angle grid with date coverage spanning from\\n1980-present. This data is sourced from the 3-hourly International Satellite\\nCloud Climatology Project (ISCCP) B1 data. The satellites included in this\\ndataset with their longitudinal coverage over time can be seen [here](https://www.ncdc.noaa.gov/gridsat/images/isccp_coverage_VZA60_nolegend.png).\\nIn regions of overlap the CDR methodology merges satellites by selecting\\nthe nadir-most observations for each grid point.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/fundamental/geostationary-ir-channel-brightness-temperature-gridsat-b1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_GRIDSAT-B1_V2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1980-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/GRIDSAT-B1/V2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_CDR_GRIDSAT_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_GRIDSAT-B1_V2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V59P2ZKR"},{"rel":"cite-as","href":"https://doi.org/10.1175/2011BAMS3039.1"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/HEAT_FLUXES/V2","title":"NOAA CDR: Ocean Heat Fluxes, Version 2","description":"The Ocean Heat Fluxes dataset is part of the NOAA Ocean Surface Bundle (OSB)\\nand provides a high quality Climate Data Record (CDR) of the air/ocean heat\\nfluxes over ice-free oceans.\\n\\nThis dataset is calculated from the OSB CDR parameters of near-surface\\natmospheric and sea surface temperature using a neural-network\\nemulator of the TOGA-COARE Bulk Air-Sea Flux Algorithm.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/atmospheric/ocean-heat-fluxes"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_HEAT_FLUXES_V2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1988-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/HEAT_FLUXES/V2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/HEAT_FLUXES_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_HEAT_FLUXES_V2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V59K4885"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/OISST/V2_1","title":"NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature","description":"The NOAA 1/4 degree daily Optimum Interpolation Sea Surface Temperature\\n(OISST) provides complete ocean temperature fields constructed by combining\\nbias-adjusted observations from different platforms (satellite, ships,\\nbuoys) on a regular global grid, with gaps filled in by interpolation.\\nSatellite data from the Advanced Very High Resolution Radiometer (AVHRR)\\nprovides the main input which permits the high temporal-spatial coverage\\nbeginning in late 1981 to the present.\\n\\nThe OISST dataset has a single day\'s data processed twice. First a near\\nreal-time preliminary version is released with a lag of 1 day, and a final\\nversion with a lag of 14 days. The final version uses extra days for\\nsmoothing, and zonal bias correction in addition to replacing the\\npreliminary version.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/oceanic/sea-surface-temperature-optimum-interpolation"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-09-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/OISST/V2_1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OISST_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5SQ8XB5"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/PATMOSX/V53","title":"NOAA CDR PATMOSX: Cloud Properties, Reflectance, and Brightness Temperatures, Version 5.3","description":"This dataset provides high quality Climate Data Record (CDR) of multiple\\ncloud properties along with Advanced Very High Resolution Radiometer\\n(AVHRR) brightness temperatures and reflectances. These data have been\\nfitted to a 0.1 x 0.1 equal angle-grid with both ascending and descending\\nassets generated daily from two to ten NOAA and MetOp satellite passes per\\nday.\\n\\nThis dataset includes 48 bands, 11 of which are deemed CDR quality\\n(marked with \\"CDR variable\\" in the band list).\\nThe cloud products are derived using the ABI (Advanced Baseline Imager)\\nCloud Height Algorithm (ACHA), and the Daytime Cloud Optical Properties\\n(DCOMP) algorithm. For more detail on the processing see the algorithm\\ntheoretical basis documents for the\\n[ACHA products](https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AVHRR%20Cloud%20Properties%20-%20PATMOS-x/AlgorithmDescription/CDRP-ATBD-0522%20Rev%201%20AVHRR%20Cloud%20Properties%20-%20PATMOS-x%20(ACHA)%20C-ATBD%20(01B-01b)%20(DSR-653)_mjf.pdf),\\n[DCOMP products](https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AVHRR%20Cloud%20Properties%20-%20PATMOS-x/AlgorithmDescription/CDRP-ATBD-0523%20Rev%201%20AVHRR%20Cloud%20Properties%20-%20PATMOS-x%20(DCOMP)%20C-ATBD%20(01B-01b)%20(DSR-654).pdf),\\n[reflectance and brightness temperature products](https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AVHRR_Ch1-2-3a_Reflectance_PATMOS-x/AlgorithmDescription.pdf).\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/atmospheric/avhrr-cloud-properties-patmos-x"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_PATMOSX_V53"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/PATMOSX/V53"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/PATMOSX_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_PATMOSX_V53#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5348HCK"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/SST_PATHFINDER/V53","title":"NOAA AVHRR Pathfinder Version 5.3 Collated Global 4km Sea Surface Temperature","description":"The AVHRR Pathfinder Version 5.3 Sea Surface Temperature dataset (PFV53)\\nis a collection of global, twice-daily 4km sea surface temperature data\\nproduced in a partnership by the NOAA National Oceanographic Data Center\\nand the University of Miami\'s Rosenstiel School of Marine and Atmospheric\\nScience. PFV53 was computed from data from the AVHRR instruments\\non board NOAA\'s polar orbiting satellite series using an entirely\\nmodernized system based on SeaDAS. PFV53 data are nearly 100%\\ncompliant with the GHRSST Data Specification Version 2.0 for L3C\\nproducts and only deviate from that standard in that \'sses_bias\',\\n\'sses_standard_deviation\', and \'sst_dtime\' variables are empty and hence\\nnot included into EE assets. PFV53 data were collected through the\\noperational periods of the NOAA-7 through NOAA-19 Polar Operational\\nEnvironmental Satellites (POES), and are available from 1981 to 2014.\\nAdditional information is available at the [NOAA Pathfinder site]\\n(https://www.nodc.noaa.gov/satellitedata/pathfinder4km53/).\\n\\nAdditional band details can be found in the [Tech Specs]\\n(https://data.nodc.noaa.gov/pathfinder/Version5.2/GDS_TechSpecs_v2.0.pdf) page.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/oceanic/sea-surface-temperature-pathfinder"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_PATHFINDER_V53"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-08-24T00:00:00Z","2014-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/SST_PATHFINDER/V53"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/AVHRR_SST_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_PATHFINDER_V53#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V52J68XX"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CDR/SST_WHOI/V2","title":"NOAA CDR WHOI: Sea Surface Temperature, Version 2","description":"The Sea Surface Temperature - WHOI dataset is part of the NOAA Ocean Surface\\nBundle (OSB) and provides a high quality Climate Data Record (CDR) of sea\\nsurface temperature over ice-free oceans.\\n\\nThe SST values are found through modeling the diurnal variability in\\ncombination with AVHRR observations of sea surface temperature.\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/cdr/oceanic/sea-surface-temperature-whoi"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_WHOI_V2"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1988-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/SST_WHOI/V2"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/SST_WHOI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_WHOI_V2#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5FB510W"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CFSR","title":"CFSR: Climate Forecast System Reanalysis","description":"The National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis\\n(CFSR) was designed and executed as a global, high-resolution, coupled atmosphere-ocean-land\\nsurface-sea ice system to provide the best estimate of the state of these coupled domains over\\nthe 32-year period of record from January 1979 to March 2011. It has been extended as an\\noperational real-time product.\\n\\nForecasts are initialized four times per day (0000, 0600, 1200, and 1800\\nUTC). We ingest only a subset of bands from files matching\\ncdas1.t??z.pgrbh**03|00**.grib2 i.e. files of only 0-hour and 3-hour forecasts as the others are\\nomitted. The forecast length is indicated by the \'forecast_hour\' metadata field.\\n\\nSome images contain only a subset of bands. Using this dataset with both \\"00\\" and \\"03\\" forecast\\ntypes will require you to cast the bands across the collection.\\n","license":"proprietary","providers":[{"name":"NOAA NWS National Centers for Environmental Prediction (NCEP)","roles":["producer","licensor"],"url":"https://doi.org/10.5065/D6513W89"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_CFSR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5065/D6513W89"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/CFSV2/FOR6H","title":"CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products","description":"The National Centers for Environmental Prediction (NCEP) Climate Forecast\\nSystem (CFS) is a fully coupled model representing the interaction between\\nthe Earth\'s atmosphere, oceans, land, and sea ice. CFS was developed at the\\nEnvironmental Modeling Center (EMC) at NCEP. The operational CFS was\\nupgraded to version 2 (CFSv2) on March 30, 2011.\\n\\nForecasts are initialized four times per day (0000, 0600, 1200, and 1800\\nUTC). This is the same model that was used to create the NCEP Climate\\nForecast System Reanalysis (CFSR), and the purpose of the CFSv2 dataset is\\nto extend CFSR. We ingest only a subset of bands from files matching\\ncdas1.t??z.sfluxgrbf**06**.grib2.\\n\\nFor more information about CFS, please see the [CFS NOAA site](http://cfs.ncep.noaa.gov/).\\n","license":"proprietary","providers":[{"name":"NOAA NWS National Centers for Environmental Prediction (NCEP)","roles":["producer","licensor"],"url":"http://rda.ucar.edu/datasets/ds094.0/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSV2/FOR6H"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_CFSV2_FOR6H_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5065/D61C1TXF"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4","title":"DMSP OLS: Global Radiance-Calibrated Nighttime Lights Version 4, Defense Meteorological Program Operational Linescan System","description":"The Defense Meteorological Program (DMSP) Operational Line-Scan System\\n(OLS) has a unique capability to detect visible and near-infrared (VNIR)\\nemission sources at night.\\n\\n\\nThis collection contains global nighttime lights images with no sensor\\nsaturation. The sensor is typically operated at a high-gain setting to\\nenable the detection of moonlit clouds. However, with six bit quantization\\nand limited dynamic range, the recorded data are saturated in the bright\\ncores of urban centers. A limited set of observations at low lunar\\nillumination were obtained where the gain of the detector was set\\nsignificantly lower than its typical operational setting (sometimes by a\\nfactor of 100). Sparse data acquired at low-gain settings were combined\\nwith the operational data acquired at high-gain settings to produce the set\\nof global nighttime lights images with no sensor saturation. Data from\\ndifferent satellites were merged and blended into the final product in order\\nto gain maximum coverage. For more information, see this\\n[readme](https://eogdata.mines.edu/dmsp/radcal_readme.txt)\\nfile from the provider.\\n","license":"proprietary","providers":[{"name":"Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines","roles":["producer","licensor"],"url":"https://eogdata.mines.edu/dmsp/download_radcal.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4"}],"extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["1996-03-16T00:00:00Z","2011-07-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NIGHTTIME_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/DMSP-OLS/NIGHTTIME_LIGHTS","title":"DMSP OLS: Nighttime Lights Time Series Version 4, Defense Meteorological Program Operational Linescan System","description":"The Defense Meteorological Program (DMSP) Operational Line-Scan System\\n(OLS) has a unique capability to detect visible and near-infrared (VNIR)\\nemission sources at night.\\n\\n\\nVersion 4 of the DMSP-OLS Nighttime Lights Time Series consists of\\ncloud-free composites made using all the available archived DMSP-OLS smooth\\nresolution data for calendar years. In cases where two satellites were\\ncollecting data, two composites were produced.\\n","license":"proprietary","providers":[{"name":"Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines","roles":["producer","licensor"],"url":"https://eogdata.mines.edu/dmsp/downloadV4composites.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS"}],"extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2014-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/DMSP-OLS/NIGHTTIME_LIGHTS"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NIGHTTIME_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GFS0P25","title":"GFS: Global Forecast System 384-Hour Predicted Atmosphere Data","description":"The Global Forecast System (GFS) is a weather forecast model produced by\\nthe National Centers for Environmental Prediction (NCEP). The GFS dataset\\nconsists of selected model outputs (described below) as gridded forecast\\nvariables. The 384-hour forecasts, with 3-hour forecast interval,\\nare made at 6-hour temporal resolution (i.e. updated four times daily).\\nUse the \'creation_time\' and \'forecast_time\' properties to select data of\\ninterest.\\n\\nThe GFS is a coupled model, composed of an atmosphere model, an ocean model,\\na land/soil model, and a sea ice model which work together to provide an\\naccurate picture of weather conditions. See [history of recent modifications\\nto the global forecast/analysis system](http://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html),\\nthe [model performance statistical web page](http://www.emc.ncep.noaa.gov/gmb/STATS_vsdb/),\\nand the [documentation homepage](https://nomads.ncep.noaa.gov/txt_descriptions/GFS_doc.shtml)\\nfor more information.\\n","license":"proprietary","providers":[{"name":"NOAA/NCEP/EMC","roles":["producer","licensor"],"url":"https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-07-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GFS0P25"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GFS0P25_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1016/j.jqsrt.2004.05.058"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/16/FDCC","title":"GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Fire (HSC) product contains four images: one in the form\\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\\nand fire radiative power.\\n\\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\\nthe lowest tolerance for false alarms should focus on the \\"processed\\" and\\n\\"saturated\\" categories (mask codes 10, 11, 30, and 31), but within these categories there\\ncan still be false alarms.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#FDC)\\n\\nNOAA provides the following scripts for suggested categories,\\ncolor maps, and visualizations:\\n\\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-fire-hot-spot-characterization-fdc"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC"}],"extent":{"spatial":{"bbox":[[-152.11,14,-49.18,56.77]]},"temporal":{"interval":[["2017-05-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/FDCC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_16_FDCC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-FDCC/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/16/FDCF","title":"GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Fire (HSC) product contains four images: one in the form\\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\\nand fire radiative power.\\n\\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\\nthe lowest tolerance for false alarms should focus on the \\"processed\\" and\\n\\"saturated\\" categories (mask codes 10, 11, 30, and 31), but within these categories there\\ncan still be false alarms.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#FDC)\\n\\nNOAA provides the following scripts for suggested categories,\\ncolor maps, and visualizations:\\n\\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-fire-hot-spot-characterization-fdc"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-05-24T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/FDCF"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_16_FDCF_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-FDCF/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/16/MCMIPC","title":"GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Cloud and Moisture Imagery products are all at 2km\\nresolution. Bands 1-6 are reflective. The dimensionless \\"reflectance factor\\" quantity is\\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#CMIP)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-cloud-and-moisture-imagery-products-cmi1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC"}],"extent":{"spatial":{"bbox":[[-152.11,14,-49.18,56.77]]},"temporal":{"interval":[["2017-07-10T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_16_MCMIPC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-MCMIPC/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/16/MCMIPF","title":"GOES-16 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Cloud and Moisture Imagery products are all at 2km\\nresolution. Bands 1-6 are reflective. The dimensionless \\"reflectance factor\\" quantity is\\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#CMIP)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-cloud-and-moisture-imagery-products-cmi1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-07-10T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPF"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_16_MCMIPF_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-MCMIPF/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/16/MCMIPM","title":"GOES-16 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Cloud and Moisture Imagery products are all at 2km\\nresolution. Bands 1-6 are reflective. The dimensionless \\"reflectance factor\\" quantity is\\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\\n\\nThe locations of domains 1 and 2 change over time.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#CMIP)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-fire-hot-spot-characterization-fdc"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-07-10T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_16_MCMIPM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-MCMIPM/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/17/FDCC","title":"GOES-17 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Fire (HSC) product contains four images: one in the form\\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\\nand fire radiative power.\\n\\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\\nthe lowest tolerance for false alarms should focus on the \\"processed\\" and\\n\\"saturated\\" categories (mask codes 10, 11, 30, and 31), but within these categories there\\ncan still be false alarms.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#FDC)\\n\\nNOAA provides the following scripts for suggested categories,\\ncolor maps, and visualizations:\\n\\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-fire-hot-spot-characterization-fdc"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCC"}],"extent":{"spatial":{"bbox":[[-180,14.57,180,53.51]]},"temporal":{"interval":[["2018-08-27T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/FDCC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_17_FDCC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCC#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-FDCC/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/17/FDCF","title":"GOES-17 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Fire (HSC) product contains four images: one in the form\\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\\nand fire radiative power.\\n\\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\\nthe lowest tolerance for false alarms should focus on the \\"processed\\" and\\n\\"saturated\\" categories (mask codes 10, 11, 30, and 31), but within these categories there\\ncan still be false alarms.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#FDC)\\n\\nNOAA provides the following scripts for suggested categories,\\ncolor maps, and visualizations:\\n\\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-fire-hot-spot-characterization-fdc"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCF"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-08-27T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/FDCF"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_17_FDCF_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCF#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-FDCF/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/17/MCMIPC","title":"GOES-17 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Cloud and Moisture Imagery products are all at 2km\\nresolution. Bands 1-6 are reflective. The dimensionless \\"reflectance factor\\" quantity is\\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#CMIP)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-cloud-and-moisture-imagery-products-cmi1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPC"}],"extent":{"spatial":{"bbox":[[-180,14.57,180,53.51]]},"temporal":{"interval":[["2018-12-04T16:30:38Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_17_MCMIPC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPC#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-MCMIPC/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/17/MCMIPF","title":"GOES-17 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Cloud and Moisture Imagery products are all at 2km\\nresolution. Bands 1-6 are reflective. The dimensionless \\"reflectance factor\\" quantity is\\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#CMIP)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-cloud-and-moisture-imagery-products-cmi1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPF"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-04T16:30:38Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPF"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_17_MCMIPF_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPF#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-MCMIPF/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/GOES/17/MCMIPM","title":"GOES-17 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\\n\\n\\nThe Cloud and Moisture Imagery products are all at 2km\\nresolution. Bands 1-6 are reflective. The dimensionless \\"reflectance factor\\" quantity is\\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\\n\\nThe locations of domains 1 and 2 change over time.\\n\\n[README](https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites#CMIP)\\n","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"https://data.noaa.gov/dataset/dataset/noaa-goes-r-series-advanced-baseline-imager-abi-level-2-cloud-and-moisture-imagery-products-cmi1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPM"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-04T16:30:38Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_GOES_17_MCMIPM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPM#terms-of-use"},{"rel":"source","href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-MCMIPM/"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/NGDC/ETOPO1","title":"ETOPO1: Global 1 Arc-Minute Elevation","description":"ETOPO1 is a 1 arc-minute global relief model of Earth\'s surface that integrates land topography and ocean bathymetry. It was built from numerous global and regional data sets. It contains two elevation bands: ice_surface and bedrock.","license":"proprietary","providers":[{"name":"NOAA","roles":["producer","licensor"],"url":"http://www.ngdc.noaa.gov/mgg/global/global.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_NGDC_ETOPO1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2008-08-01T00:00:00Z","2008-08-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/NGDC/ETOPO1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_NGDC_ETOPO1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_NGDC_ETOPO1#terms-of-use"},{"rel":"source","href":"https://www.ngdc.noaa.gov/mgg/global/relief/ETOPO1/data/"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5C8276M"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/NWS/RTMA","title":"RTMA: Real-Time Mesoscale Analysis","description":"The Real-Time Mesoscale Analysis (RTMA) is a high-spatial and temporal\\nresolution analysis for near-surface weather conditions. This dataset\\nincludes hourly analyses at 2.5 km for CONUS.\\n","license":"proprietary","providers":[{"name":"NOAA/NWS","roles":["producer","licensor"],"url":"http://www.nco.ncep.noaa.gov/pmb/products/rtma/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA"}],"extent":{"spatial":{"bbox":[[-130.17,20.15,-60.81,52.91]]},"temporal":{"interval":[["2015-06-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/NWS/RTMA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_NWS_RTMA_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/PERSIANN-CDR","title":"PERSIANN-CDR: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record","description":"PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2.","license":"proprietary","providers":[{"name":"NOAA NCDC","roles":["producer","licensor"],"url":"http://doi.org/10.7289/V51V5BWQ"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_PERSIANN-CDR"}],"extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1983-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/PERSIANN-CDR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NOAA_PERSIANN-CDR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_PERSIANN-CDR#terms-of-use"},{"rel":"source","href":"https://www.ncei.noaa.gov/data/precipitation-persiann/access/"},{"rel":"cite-as","href":"https://doi.org/10.7289/V51V5BWQ"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/VIIRS/001/VNP09GA","title":"VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface\\nreflectance (VNP09GA) product provides an estimate of land\\nsurface reflectance from the Suomi National Polar-Orbiting Partnership\\n(S-NPP) VIIRS sensor. Data are provided for three imagery bands\\n(I1, I2, I3) at nominal 500 meter resolution (~463 meter) and nine\\nmoderate-resolution bands (M1, M2, M3, M4, M5, M7, M8, M10, M11) at nominal\\n1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets\\nare derived through resampling the native 375 meter and 750 meter VIIRS\\nresolutions, respectively, in the L2 input product. These bands are\\ncorrected for atmospheric conditions to provide an estimate\\nof the surface spectral reflectance as it would be measured at ground level.\\n\\nThe data is temporally aggregated over each of the 16 possible passes per\\nday. When multiple observations are present for each day, only the first\\nof the highest-quality observations is included.\\n\\nFor additional information, visit the VIIRS [Land Product Quality\\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/) and see\\nthe [User Guide](https://lpdaac.usgs.gov/documents/124/VNP09_User_Guide_V1.6.pdf).\\n\\nDocumentation:\\n\\n* [User\'s Guide] (https://lpdaac.usgs.gov/documents/124/VNP09_User_Guide_V1.6.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)] (https://lpdaac.usgs.gov/documents/122/VNP09_ATBD.pdf)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/VIIRS/VNP09GA.001"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09GA"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP09GA"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/VIIRS_SR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09GA#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/VIIRS/VNP09GA.001"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/VIIRS/001/VNP13A1","title":"VNP13A1: VIIRS Vegetation Indices 16-Day 500m","description":"The Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible\\nInfrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1)\\ndata product provides vegetation indices by a process of\\nselecting the best available pixel over a 16-day acquisition period\\nat 500 meter resolution. The VNP13 data products are designed\\nafter the Moderate Resolution Imaging Spectroradiometer (MODIS)\\nTerra and Aqua Vegetation Indices product suite to promote the\\ncontinuity of the Earth Observation System (EOS) mission.\\n\\nThe VNP13 algorithm process produces three vegetation indices: (1)\\nNormalized Difference Vegetation Index (NDVI), (2) the Enhanced\\nVegetation Index (EVI), and (3) Enhanced Vegetation Index-2 (EVI2).\\n(1) NDVI is one of the longest continual remotely sensed time series\\nobservations, using both the red and near-infrared (NIR) bands.\\n(2) EVI is a slightly different vegetation index that is more sensitive\\nto canopy cover, while NDVI is more sensitive to chlorophyll. (3) EVI2\\nis a reformation of the standard 3-band EVI, using the red band and\\nNIR band. This reformation addresses arising issues when comparing\\nVIIRS EVI to other EVI models that do not include a blue band. EVI2\\nwill eventually become the standard EVI.\\n\\nAlong with the three Vegetation Indices layers, this product also\\nincludes layers for near-infrared (NIR) reflectance; three shortwave\\ninfrared (SWIR) reflectance—red, blue, and green reflectance; composite\\nday of year; pixel reliability; view and sun angles, and a quality layer.\\n\\nFor additional information, visit the VIIRS [Land Product Quality\\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/) and see\\nthe [User Guide](https://lpdaac.usgs.gov/documents/184/VNP13_User_Guide_ATBD_V2.1.2.pdf).\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/184/VNP13_User_Guide_ATBD_V2.1.2.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/184/VNP13_User_Guide_ATBD_V2.1.2.pdf)\\n\\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/VIIRS/1/VNP13A1)\\n","license":"proprietary","providers":[{"name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/VIIRS/VNP13A1.001"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP13A1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP13A1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/VNP13A1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP13A1#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/VIIRS/VNP13A1.001"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG","title":"VIIRS Nighttime Day/Night Band Composites Version 1","description":"Monthly average radiance composite images using nighttime data from the\\nVisible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB).\\n\\nAs these data are composited monthly, there are many areas of the globe\\nwhere it is impossible to get good quality data coverage for that month.\\nThis can be due to cloud cover, especially in the tropical regions, or due\\nto solar illumination, as happens toward the poles in their respective\\nsummer months. Therefore it is recommended that users of these data utilize\\nthe \'cf_cvg\' band and not assume a value of zero in the average radiance\\nimage means that no lights were observed.\\n\\nCloud cover is determined using the VIIRS Cloud\\nMask product (VCM). In addition, data near the edges of the swath are not\\nincluded in the composites (aggregation zones 29-32). Version 1 has NOT\\nbeen filtered to screen out lights from aurora, fires, boats, and other\\ntemporal lights. This separation is under development and will be included\\nin a later version of this time series. Also in development is a method to\\nseparate lights from background (non-light) values.\\n\\n\\nPrior to averaging, the DNB data is filtered to exclude data impacted by\\nstray light, lightning, lunar illumination, and cloud-cover.\\n","license":"proprietary","providers":[{"name":"Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines","roles":["producer","licensor"],"url":"https://eogdata.mines.edu/download_dnb_composites.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG"}],"extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["2012-04-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/VIIRS_DNB_VCMSLCFG_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG","title":"VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1","description":"Monthly average radiance composite images using nighttime data from the\\nVisible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB).\\n\\nAs these data are composited monthly, there are many areas of the globe\\nwhere it is impossible to get good quality data coverage for that month.\\nThis can be due to cloud cover, especially in the tropical regions, or due\\nto solar illumination, as happens toward the poles in their respective\\nsummer months. Therefore it is recommended that users of these data utilize\\nthe \'cf_cvg\' band and not assume a value of zero in the average radiance\\nimage means that no lights were observed.\\n\\nCloud cover is determined using the VIIRS Cloud\\nMask product (VCM). In addition, data near the edges of the swath are not\\nincluded in the composites (aggregation zones 29-32). Version 1 has NOT\\nbeen filtered to screen out lights from aurora, fires, boats, and other\\ntemporal lights. This separation is under development and will be included\\nin a later version of this time series. Also in development is a method to\\nseparate lights from background (non-light) values.\\n\\n\\nThis product is an alternative configuration of the VIIRS DNB using a\\nprocedure to correct for stray light. The correction procedure extends\\nvisible areas closer to the poles and improves dynamic range. It should be\\nnoted some artifacts are introduced due to the procedure used in twilight\\nregions; see [reference paper](http://spie.org/Publications/Proceedings/Paper/10.1117/12.2023107)\\nfor more details. This product excludes data\\nimpacted by cloud cover.\\n","license":"proprietary","providers":[{"name":"Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines","roles":["producer","licensor"],"url":"https://eogdata.mines.edu/download_dnb_composites.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG"}],"extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/VIIRS_DNB_VCMSLCFG_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"NRCan/CDEM","title":"Canadian Digital Elevation Model","description":"The Canadian Digital Elevation Model (CDEM) is part of Natural Resources\\nCanada\'s (NRCan) altimetry system and stems from the existing Canadian\\nDigital Elevation Data (CDED). In these data, elevations can be either\\nground or reflective surface elevations.\\n\\nThe CDEM is comprised of multiple DEMs with varying resolutions. These\\nvary according to latitude and have a base resolution of 0.75 arc-seconds.\\nFor more information see the [Product Specifications](http://ftp.geogratis.gc.ca/pub/nrcan_rncan/elevation/cdem_mnec/doc/CDEM_product_specs.pdf)\\n","license":"proprietary","providers":[{"name":"NRCan","roles":["producer","licensor"],"url":"https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/NRCan_CDEM"}],"extent":{"spatial":{"bbox":[[-142,41,-52,84]]},"temporal":{"interval":[["1945-01-01T00:00:00Z","2011-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/NRCan/CDEM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NRCan_CDEM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/NRCan_CDEM#terms-of-use"},{"rel":"source","href":"http://ftp.geogratis.gc.ca/pub/nrcan_rncan/elevation/cdem_mnec/"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OREGONSTATE/PRISM/AN81d","title":"PRISM Daily Spatial Climate Dataset AN81d","description":"The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. \\n\\nGrids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country. For more information, see the [Descriptions of PRISM Spatial Climate Datasets](http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).\\n\\n**Note**\\n\\n* **Warning**: This dataset should not be\\n used to calculate century-long climate trends due to non-climatic\\n variations from to station equipment and location changes, openings\\n and closings, varying observation times, and the use of relatively\\n short-term networks. Please see the [dataset documentation\\n ](http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf)\\n for more details.\\n* The assets have start time of noon UTC, not midnight UTC.\\n* It takes time for observation networks to conduct quality control and\\n release station data. Therefore,\\n PRISM datasets are re-modeled several times until six months\\n have elapsed, when they are considered permanent. A [release\\n schedule is available](http://www.prism.oregonstate.edu/calendar/).\\n* For use of the 30 arc-second (~800 m) version of this dataset\\n please contact the provider at prism-questions@nacse.org\\n","license":"proprietary","providers":[{"name":"PRISM / OREGONSTATE","roles":["producer","licensor"],"url":"http://www.prism.oregonstate.edu/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d"}],"extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/AN81d"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/PRISM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1371/journal.pone.0141140"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OREGONSTATE/PRISM/AN81m","title":"PRISM Monthly Spatial Climate Dataset AN81m","description":"The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. \\n\\nGrids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country. For more information, see the [Descriptions of PRISM Spatial Climate Datasets](http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).\\n\\n**Note**\\n\\n* **Warning**, this dataset should not be\\n used to calculate century-long climate trends due to non-climatic\\n variations from to station equipment and location changes, openings\\n and closings, varying observation times, and the use of relatively\\n short-term networks. Please see the [dataset\\n documentation](http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf)\\n for more details.\\n* It takes time for observation networks to conduct quality control\\n and release station data. Therefore, PRISM datasets are re-modeled\\n several times until six months have elapsed, when they are\\n considered permanent. A [release schedule is\\n available](http://www.prism.oregonstate.edu/calendar/).\\n* For use of the 30 arc-second (~800 m) version of this dataset\\n please contact the provider at prism-questions@nacse.org.\\n","license":"proprietary","providers":[{"name":"PRISM / OREGONSTATE","roles":["producer","licensor"],"url":"http://www.prism.oregonstate.edu/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m"}],"extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1895-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/AN81m"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/PRISM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1371/journal.pone.0141140"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OREGONSTATE/PRISM/Norm81m","title":"PRISM Long-Term Average Climate Dataset Norm81m","description":"The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. \\n\\nGrids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country. For more information, see the [Descriptions of PRISM Spatial Climate Datasets](http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).","license":"proprietary","providers":[{"name":"PRISM / OREGONSTATE","roles":["producer","licensor"],"url":"http://www.prism.oregonstate.edu/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm81m"}],"extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/Norm81m"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/PRISM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm81m#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1371/journal.pone.0141140"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OSU/GIMP/2000_ICE_OCEAN_MASK","title":"Greenland Ice & Ocean Mask - Greenland Mapping Project (GIMP)","description":"This dataset provides complete land ice and ocean\\nclassification masks at 15 m for the Greenland ice sheet.\\nIce cover was mapped using\\na combination of orthorectified panchromatic (band 8)\\nimagery from the Landsat 7 Enhanced Thematic Mapper\\nPlus (ETM+), distributed by the USGS, and RADARSAT-1\\nSynthetic Amplitude Radar (SAR) amplitude images produced\\nand distributed by I. Joughin at the Applied Physics\\nLaboratory, University of Washington.\\n\\nThe Landsat imagery was acquired for the months of July\\nthrough September in 1999, 2000 and 2001 (mostly 2000)\\nand the RADARSAT imagery was acquired in fall of 2000.\\n","license":"proprietary","providers":[{"name":"NASA NSIDC DAAC at CIRES","roles":["producer","licensor"],"url":"https://doi.org/10.5067/B8X58MQBFUPA"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_2000_ICE_OCEAN_MASK"}],"extent":{"spatial":{"bbox":[[-89.3211593425295,58.79601275381146,7.555941634834938,83.95386175580668]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/2000_ICE_OCEAN_MASK"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GIMP_ICE_OCEAN_MASK_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_2000_ICE_OCEAN_MASK#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/B8X58MQBFUPA"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OSU/GIMP/2000_IMAGERY_MOSAIC","title":"2000 Greenland Mosaic - Greenland Ice Mapping Project (GIMP)","description":"This dataset provides a complete 15 m resolution image mosaic\\nof the Greenland ice sheet derived from Landsat 7 ETM+ and\\nRADARSAT-1 SAR imagery from the years 1999 to 2002. The methods\\ninclude a combination of image cloud masking, pan sharpening,\\nimage sampling and resizing, and image coregistration.\\nPlease see Howat, 2014 for more information regarding\\nprocessing methods.\\n\\n**Note**\\n\\n* Users of GIMP DEM or GIMP 2000 Image Mosaic images may find\\nit helpful to mask out areas outside of the Greenland coastline\\nusing the corresponding 15 m ocean mask image\\nGreenland Ice Mapping Project (GIMP) Land Ice and Ocean\\nClassification Mask,\\n* The SAR data are distributed at 20 m resolution. Data were\\nup-sampled through bilinear interpolation to 15 m to match\\nthe resolution of Landsat band-8.\\n\\n[General documentation](https://doi.org/10.5067/4RNTRRE4JCYD)\\n","license":"proprietary","providers":[{"name":"NASA NSIDC DAAC at CIRES","roles":["producer","licensor"],"url":"https://doi.org/10.5067/4RNTRRE4JCYD"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_2000_IMAGERY_MOSAIC"}],"extent":{"spatial":{"bbox":[[-89.3211593425295,58.79601275381146,7.555941634834938,83.95386175580668]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/2000_IMAGERY_MOSAIC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GIMP_IMAGE_MOSAIC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_2000_IMAGERY_MOSAIC#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/4RNTRRE4JCYD"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OSU/GIMP/DEM","title":"Greenland DEM - Greenland Mapping Project (GIMP)","description":"This Digital Elevation Model (DEM) is constructed from a combination\\nof ASTER and SPOT-5 DEM\'s for the ice sheet periphery and margin\\n(i.e. below the equilbrium line elevation) south of\\napproximately 82.5°N, and AVHRR photoclinometry in the ice sheet\\ninterior and far north (Scambos and Haran, 2002).\\n\\nSPOT-5 DEM\'s were produced and distributed as part of the Spot5\\nstereoscopic survey of Polar Ice: Reference Images & Topographies\\n(SPIRIT) project (Korona et al., 2009). Ocean surfaces were masked\\nusing the GIMP Land Classification mask and replased with the\\nCNES CLS11 mean sea surface height (Schaeffer et al., 2012).\\n\\n**Note**\\n\\n* All land elevation data is horizontally and vertically registered\\nto average ICESat elevations for the 2003-2009 time period, and\\ntherefore the DEM has a nominal date of 2007, although care must\\nbe taken when using the DEM in areas of rapid change, such as\\nmajor outlet glaciers south of 70°N.\\n* The DEM has a resolution of 30 m, although the \\"true\\" resolution of the\\nDEM will vary from 40 m in areas of SPOT-5 coverage\\n(see Korona et al. 2009) to 500 m in areas of photoclinometry.\\n* The ice-sheet-wide root-mean-squared validation error\\nrelative to ICESat is +/-10 m, rangining from close to +/- 1 m\\nover most ice surfaces to +/- 30 m in areas of high relief.\\n\\n[General documentation](https://doi.org/10.5067/NV34YUIXLP9W)\\n","license":"proprietary","providers":[{"name":"NASA NSIDC DAAC at CIRES","roles":["producer","licensor"],"url":"https://doi.org/10.5067/NV34YUIXLP9W"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_DEM"}],"extent":{"spatial":{"bbox":[[-89.32179714240877,58.795926930232035,7.556480769227746,83.95392768557056]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/DEM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GIMP_DEM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_DEM#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/NV34YUIXLP9W"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OSU/GIMP/ICE_VELOCITY_OPT","title":"MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from Optical Images Version 2","description":"This dataset, part of the NASA Making Earth System Data\\nRecords for Use in Research Environments (MEaSUREs) program,\\nconsists of mean monthly velocity maps for selected glacier outlet areas.\\nThe maps are generated by tracking visible features between optical\\nimage pairs acquired by the Landsat 4 and 5 Thematic Mapper (TM),\\nthe Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Landsat 8\\nOperational Land Imager (OLI), and the Advanced Spaceborne Thermal\\nEmission and Reflection Radiometer (ASTER).\\n\\n**Note**\\n\\nMonthly means are calculated from images, which may have acquisition\\ndates from the preceding or succeeding month. For the naming convention,\\nthe month is determined from where the midpoint Julian dates fall.\\nFor example, September monthly means may have been generated from\\nimages that were acquired in August or in October but the midpoint\\nJulian date between the images falls within September. The exact\\ndates used are included as per-image metadata fields.\\n\\n[General documentation](https://doi.org/10.5067/VM5DZ20MYF5C)\\n","license":"proprietary","providers":[{"name":"NASA NSIDC DAAC at CIRES","roles":["producer","licensor"],"url":"https://doi.org/10.5067/VM5DZ20MYF5C"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_ICE_VELOCITY_OPT"}],"extent":{"spatial":{"bbox":[[-89.3211593425295,58.79601275381146,7.555941634834938,83.95386175580668]]},"temporal":{"interval":[["1985-03-01T00:00:00Z","2016-09-30T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/ICE_VELOCITY_OPT"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/GIMP_ICE_VELOCITY_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_ICE_VELOCITY_OPT#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.5067/VM5DZ20MYF5C"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/CLM/CLM_LST_MOD11A2-DAYNIGHT_M/v01","title":"OpenLandMap Long-term Land Surface Temperature monthly day-night difference","description":"Long-term MODIS LST day-time and night-time\\ndifferences at 1 km based on the 2000-2017 time series\\n\\n\\nDerived using the [data.table package and quantile function in R](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD11A2)\\n. For more info about the MODIS LST product see [this page](https://lpdaac.usgs.gov/products/mod11a2v006/).\\nAntartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1420114"},{"name":"Google Earth 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4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1420114"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_M/v01","title":"OpenLandMap Long-term Land Surface Temperature daytime monthly median","description":"Land Surface Temperature daytime monthly mean value 2000-2017.\\n\\nDerived using the [data.table package and quantile function in R](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD11A2).\\nFor more info about the MODIS LST product see [this page](https://lpdaac.usgs.gov/products/mod11a2v006/).\\nAntartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1420114"},{"name":"Google Earth 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4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1420114"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_SD/v01","title":"OpenLandMap Long-term Land Surface Temperature daytime monthly standard deviation","description":"Long-term MODIS LST day-time and night-time temperatures standard deviation\\nat 1 km based on the 2000-2017 time series.\\n\\nDerived using the [data.table package and quantile function in R](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD11A2).\\nFor more info about the MODIS LST product see [this page](https://lpdaac.usgs.gov/products/mod11a2v006/).\\nAntartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1420114"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_SD_v01"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_SD/v01"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_SD_v01_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_SD_v01#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1420114"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/CLM/CLM_PRECIPITATION_SM2RAIN_M/v01","title":"OpenLandMap Precipitation monthly","description":"Monthly precipitation in mm at 1 km resolution based on [SM2RAIN-ASCAT\\n2007-2018](https://doi.org/10.5281/zenodo.2615278),\\nIMERG, CHELSA Climate, and WorldClim.\\n\\n\\n[Downscaled to 1 km resolution using gdalwarp](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/clim1km)\\n(cubic splines) and an average between [WorldClim](http://www.worldclim.com/version2),\\n[CHELSA Climate](https://chelsa-climate.org/),\\nand [IMERG monthly product](ftp://jsimpson.pps.eosdis.nasa.gov/NRTPUB/imerg/gis/) (see, e.g, \\\\\\"3B-MO-L.GIS.IMERG.20180601.V05B.tif\\\\\\").\\n3x higher weight is given to the SM2RAIN-ASCAT data since it assumed to be more accurate. Processing steps are available\\n[**here**](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/clim1km)\\n. Antartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1435912"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_CLM_CLM_PRECIPITATION_SM2RAIN_M_v01"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_PRECIPITATION_SM2RAIN_M/v01"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_CLM_CLM_PRECIPITATION_SM2RAIN_M_v01_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_CLM_CLM_PRECIPITATION_SM2RAIN_M_v01#terms-of-use"},{"rel":"license","href":"[Open Database License (ODbL) v1.0](https://opendatacommons.org/licenses/odbl/1-0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1435912"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01","title":"OpenLandMap Potential distribution of biomes","description":"Potential Natural Vegetation biomes global predictions of classes\\n(based on predictions using the BIOMES 6000 dataset\'s \'current biomes\' category.)\\n\\nPotential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate\\nthat would exist at a given location non-impacted by human activities. PNV is useful for raising\\npublic awareness about land degradation and for estimating land potential.\\nThis dataset contains results of predictions of\\n- (1) global distribution of biomes based on the BIOME 6000 data set (8057 modern pollen-based site reconstructions),\\n- (2) distribution of forest tree species in Europe based on detailed occurrence records (1,546,435 ground observations), and\\n- (3) global monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values (30,301 randomly-sampled points).\\n\\n\\nTo report an issue or artifact in data, please use [this link](https://github.com/envirometrix/PNVmaps/issues).\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.7910/DVN/QQHCIK"},{"name":"Google Earth 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v1.0](https://opendatacommons.org/licenses/odbl/1-0/)"},{"rel":"cite-as","href":"https://doi.org/10.7910/DVN/QQHCIK"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/PNV/PNV_FAPAR_PROBA-V_D/v01","title":"OpenLandMap Potential FAPAR monthly","description":"Potential Natural Vegetation FAPAR predicted monthly median (based on PROB-V FAPAR 2014-2017).\\n[Description](https://gitlab.com/openlandmap/global-layers/#potential-natural-vegetation).\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.7910/DVN/QQHCIK"},{"name":"Google Earth 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v1.0](https://opendatacommons.org/licenses/odbl/1-0/)"},{"rel":"cite-as","href":"https://doi.org/10.7910/DVN/QQHCIK"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_BULKDENS-FINEEARTH_USDA-4A1H_M/v02","title":"OpenLandMap Soil bulk density","description":"Soil bulk density (fine earth) 10 x kg / m3 at 6 standard\\ndepths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution.\\n\\n\\nProcessing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/LandGISmaps/tree/master/soil).\\nAntartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1475970"},{"name":"Google Earth 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4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1475970"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_CLAY-WFRACTION_USDA-3A1A1A_M/v02","title":"OpenLandMap Clay content","description":"Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60,\\n100 and 200 cm) at 250 m resolution\\n\\nBased on machine learning predictions from global compilation of soil profiles and samples. Processing\\nsteps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil)\\n. Antartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1476854"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_CLAY-WFRACTION_USDA-3A1A1A_M/v02"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1476854"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P/v01","title":"OpenLandMap predicted Hapludalfs probability","description":"Predicted USDA soil great groups at 250 m (probabilities).\\n\\n\\n\\nDistribution of the USDA soil great groups based on machine learning predictions\\nfrom global compilation of soil profiles. To learn more about soil great groups\\nplease refer to the [Illustrated Guide to Soil Taxonomy - NRCS - USDA](https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download/?cid=stelprdb1247203.pdf)\\n. Processing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil)\\n. Antartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1476844"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P_v01"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P/v01"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P_v01_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P_v01#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1476844"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX_C/v01","title":"OpenLandMap USDA soil taxonomy great groups","description":"Predicted USDA soil great group probablities at 250m\\n\\nDistribution of the USDA soil great groups based on machine learning predictions\\nfrom global compilation of soil profiles. To learn more about soil great groups\\nplease refer to the [Illustrated Guide to Soil Taxonomy - NRCS - USDA](https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download/?cid=stelprdb1247203.pdf)\\n. Processing steps are described in detail **[here]https://gitlab.com/openlandmap/global-layers/tree/master/soil)**\\n. Antartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1476844"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX_C/v01"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1476844"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_ORGANIC-CARBON_USDA-6A1C_M/v02","title":"OpenLandMap Soil organic carbon content","description":"Soil organic carbon content in x 5 g / kg at 6 standard depths\\n(0, 10, 30, 60, 100 and 200 cm) at 250 m resolution\\n\\n\\nPredicted from a global compilation of soil points. Processing steps are described\\nin detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil)\\n. Antartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1475457"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_ORGANIC-CARBON_USDA-6A1C_M/v02"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1475457"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_PH-H2O_USDA-4C1A2A_M/v02","title":"OpenLandMap Soil pH in H2O","description":"Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution\\n\\nProcessing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil)\\n. Antartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1475459"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_PH-H2O_USDA-4C1A2A_M/v02"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1475459"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_SAND-WFRACTION_USDA-3A1A1A_M/v02","title":"OpenLandMap Sand content","description":"Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution\\n\\n\\nBased on machine learning predictions from global compilation of soil profiles and samples.\\nProcessing steps are described in detail **[here](https://gitlab.com/openlandmap/global-layers/tree/master/soil)**\\n. Antartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1476851"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_SAND-WFRACTION_USDA-3A1A1A_M/v02"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1476851"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_TEXTURE-CLASS_USDA-TT_M/v02","title":"OpenLandMap Soil texture class (USDA system)","description":"Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m\\n\\n\\nDerived from predicted soil texture fractions using the soiltexture package in R.\\nProcessing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil)\\n. Antartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.1475451"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_TEXTURE-CLASS_USDA-TT_M_v02"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_TEXTURE-CLASS_USDA-TT_M/v02"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_SOL_SOL_TEXTURE-CLASS_USDA-TT_M_v02_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_TEXTURE-CLASS_USDA-TT_M_v02#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.1475451"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"OpenLandMap/SOL/SOL_WATERCONTENT-33KPA_USDA-4B1C_M/v01","title":"OpenLandMap Soil water content at 33kPa (field capacity)","description":"Soil water content (volumetric %) for 33kPa and 1500kPa suctions\\npredicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution\\n\\nTraining points are based on a global compilation of soil profiles:\\n\\n* [USDA NCSS](https://ncsslabdatamart.sc.egov.usda.gov/)\\n* [AfSPDB](https://www.isric.org/projects/africa-soil-profiles-database-afsp)\\n* [ISRIC WISE](https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c)\\n* [EGRPR](http://egrpr.esoil.ru/)\\n* [SPADE](https://esdac.jrc.ec.europa.eu/content/soil-profile-analytical-database-2)\\n* [CanNPDB](https://open.canada.ca/data/en/dataset/6457fad6-b6f5-47a3-9bd1-ad14aea4b9e0)\\n* [UNSODA](https://data.nal.usda.gov/dataset/unsoda-20-unsaturated-soil-hydraulic-database-database-and-program-indirect-methods-estimating-unsaturated-hydraulic-properties)\\n* [SWIG](https://doi.pangaea.de/10.1594/PANGAEA.885492)\\n* [HYBRAS](http://www.cprm.gov.br/en/Hydrology/Research-and-Innovation/HYBRAS-4208.html)\\n* [HydroS](https://doi.org/10.4228/ZALF.2003.273)\\n\\nData import steps are available [here](https://gitlab.com/openlandmap/compiled-ess-point-data-sets).\\nSpatial prediction steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil/soil_water).\\nNote: these are actually measured and mapped soil content values; no Pedo-Transfer-Functions\\nhave been used (except to fill in the missing NCSS bulk densities). Available\\nwater capacity in mm (derived as a difference between field capacity and wilting\\npoint multiplied by layer thickness) per layer is available [here](https://doi.org/10.5281/zenodo.2629148).\\nAntartica is not included.\\n","license":"proprietary","providers":[{"name":"EnvirometriX Ltd","roles":["producer","licensor"],"url":"https://doi.org/10.5281/zenodo.2629589"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_WATERCONTENT-33KPA_USDA-4B1C_M/v01"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01#terms-of-use"},{"rel":"license","href":"[Creative Commons Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/)"},{"rel":"cite-as","href":"https://doi.org/10.5281/zenodo.2629589"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/EVI_5km_Monthly","title":"Oxford MAP EVI: Malaria Atlas Project Gap-Filled Enhanced Vegetation Index","description":"The underlying dataset for this Enhanced Vegetation Index (EVI)\\nproduct is MODIS BRDF-corrected imagery (MCD43B4), which was gap-filled\\nusing the approach outlined in Weiss et al. (2014) to eliminate missing\\ndata caused by factors such as cloud cover. Gap-free outputs were then\\naggregated temporally and spatially to produce the monthly ≈5km product.\\n","license":"proprietary","providers":[{"name":"Oxford Malaria Atlas Project","roles":["producer","licensor"],"url":"https://www.bdi.ox.ac.uk/research/malaria-atlas-project"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_EVI_5km_Monthly"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-02-01T00:00:00Z","2015-06-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/EVI_5km_Monthly"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_EVI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_EVI_5km_Monthly#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual","title":"Oxford MAP: Malaria Atlas Project Fractional International Geosphere-Biosphere Programme Landcover","description":"The underlying dataset for this landcover product is the IGBP layer found\\nwithin the MODIS annual landcover product (MCD12Q1). This data was\\nconverted from its categorical format, which has a ≈500 meter resolution,\\nto a fractional product indicating the integer percentage (0-100) of the\\noutput pixel covered by each of the 17 landcover classes (1 per band).\\n","license":"proprietary","providers":[{"name":"Oxford Malaria Atlas Project","roles":["producer","licensor"],"url":"https://www.bdi.ox.ac.uk/research/malaria-atlas-project"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_IGBP_Fractional_Landcover_5km_Annual"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2013-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_Fractional_Landcover_Annual_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_IGBP_Fractional_Landcover_5km_Annual#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/LST_Day_5km_Monthly","title":"Oxford MAP LST: Malaria Atlas Project Gap-Filled Daytime Land Surface Temperature","description":"The underlying dataset for this daytime product is MODIS land surface\\ntemperature data (MOD11A2), which was gap-filled using the approach\\noutlined in Weiss et al. (2014) to eliminate missing data caused by factors\\nsuch as cloud cover. Gap-free outputs were then aggregated temporally and\\nspatially to produce the monthly ≈5km product.\\n","license":"proprietary","providers":[{"name":"Oxford Malaria Atlas Project","roles":["producer","licensor"],"url":"https://www.bdi.ox.ac.uk/research/malaria-atlas-project"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_LST_Day_5km_Monthly"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-03-01T00:00:00Z","2015-06-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/LST_Day_5km_Monthly"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_LST_Day_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_LST_Day_5km_Monthly#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/LST_Night_5km_Monthly","title":"Oxford MAP LST: Malaria Atlas Project Gap-Filled Nighttime Land Surface Temperature","description":"The underlying dataset for this nighttime product is MODIS land surface\\ntemperature data (MOD11A2), which was gap-filled using the approach outlined\\nin Weiss et al. (2014) to eliminate missing data caused by factors such as\\ncloud cover. Gap-free outputs were then aggregated temporally and spatially\\nto produce the monthly ≈5km product.\\n","license":"proprietary","providers":[{"name":"Oxford Malaria Atlas Project","roles":["producer","licensor"],"url":"https://www.bdi.ox.ac.uk/research/malaria-atlas-project"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_LST_Night_5km_Monthly"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-03-01T00:00:00Z","2015-06-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/LST_Night_5km_Monthly"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_LST_Night_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_LST_Night_5km_Monthly#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/TCB_5km_Monthly","title":"Oxford MAP TCB: Malaria Atlas Project Gap-Filled Tasseled Cap Brightness","description":"This gap-filled Tasseled Cap Brightness (TCB) dataset was created by\\napplying the tasseled-cap equations defined in Lobser and Cohen (2007) to\\nMODIS BRDF-corrected imagery (MCD43B4). The resulting data were gap-filled\\nusing the approach outlined in Weiss et al. (2014) to eliminate missing\\ndata caused by factors such as cloud cover, and then the data were\\naggregated temporally and spatially to produce the monthly ≈5km product.\\n","license":"proprietary","providers":[{"name":"Oxford Malaria Atlas Project","roles":["producer","licensor"],"url":"https://www.bdi.ox.ac.uk/research/malaria-atlas-project"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_TCB_5km_Monthly"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-02-01T00:00:00Z","2015-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/TCB_5km_Monthly"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_TCB_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_TCB_5km_Monthly#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/TCW_5km_Monthly","title":"Oxford MAP TCW: Malaria Atlas Project Gap-Filled Tasseled Cap Wetness","description":"This gap-filled Tasseled Cap Wetness (TCW) dataset was created by applying\\nthe tasseled-cap equations defined in Lobser and Cohen (2007) to\\nMODIS BRDF-corrected imagery (MCD43B4). The resulting data were\\ngap-filled using the approach outlined in Weiss et al. (2014) to eliminate\\nmissing data caused by factors such as cloud cover, and then the data were\\naggregated temporally and spatially to produce the monthly ≈5km product.\\n","license":"proprietary","providers":[{"name":"Oxford Malaria Atlas Project","roles":["producer","licensor"],"url":"https://www.bdi.ox.ac.uk/research/malaria-atlas-project"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_TCW_5km_Monthly"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-02-01T00:00:00Z","2015-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/TCW_5km_Monthly"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_TCW_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_TCW_5km_Monthly#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/accessibility_to_cities_2015_v1_0","title":"Accessibility to Cities 2015","description":"This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015.Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometer or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants.
This map was produced through a collaboration between the University of Oxford Malaria Atlas Project (MAP), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders.
These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a “friction surface”, a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest city (by travel time). Cities were determined using the high-density-cover product created by the Global Human Settlement Project.
Each pixel in the resultant accessibility map thus represents the modeled shortest time from that location to a city.","license":"proprietary","providers":[{"name":"Malaria Atlas Project, University of Oxford","roles":["producer","licensor"],"url":"http://www.map.ox.ac.uk/accessibility_to_cities/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_accessibility_to_cities_2015_v1_0"}],"extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/accessibility_to_cities_2015_v1_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_accessibility_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_accessibility_to_cities_2015_v1_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/accessibility_to_healthcare_2019","title":"Accessibility to Healthcare 2019","description":"This global accessibility map enumerates land-based travel time (in\\nminutes) to the nearest hospital or clinic for all areas between 85\\ndegrees north and 60 degrees south for a nominal year 2019. It also\\nincludes \\"walking-only\\" travel time, using non-motorized means of\\ntransportation only.\\n\\nMajor data collection efforts underway by OpenStreetMap, Google Maps, and\\nacademic researchers have been harnessed to compile the most complete\\ncollection of healthcare facility locations to date. This map was\\nproduced through a collaboration between MAP (University of Oxford),\\nTelethon Kids Institute (Perth, Australia), Google, and the University\\nof Twente, Netherlands.\\n\\nThis project builds on previous work published by Weiss et al 2018\\n([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)).\\nWeiss et al (2018) utilised datasets for roads\\n(comprising the first ever global-scale use of Open Street Map and Google\\nroads datasets), railways, rivers, lakes, oceans, topographic conditions\\n(slope and elevation), landcover types, and national borders. These\\ndatasets were each allocated a speed or speeds of travel in terms of time\\nto cross each pixel of that type. The datasets were then combined to\\nproduce a \\"friction surface\\": a map where every pixel is allocated a\\nnominal overall speed of travel based on the types occurring within that\\npixel. For the current project, an updated friction surface was created to\\nincorporate recent improvements within OSM roads data.\\n\\nLeast-cost-path algorithms (run in Google Earth Engine and, for\\nhigh-latitude areas, in R) were used in conjunction with this friction\\nsurface to calculate the time of travel from all locations to the nearest\\n(in time) healthcare facility. The healthcare facilities dataset utilized\\nlocation data from two of the largest global databases: (1) OSM data that\\nwas collated and made available for download at\\n[www.healthsites.io](http://www.healthsites.io/); and (2) data\\nextracted from Google Maps. The global datasets were augmented with\\ncontinental-scale facility locations that were recently published for\\nAfrica and Australia. To facilitate comparisons between data sources, only\\nfacilities defined as hospitals and clinics were used. Multiple points\\nfound within the same pixel were merged to match the resolution of the\\nanalysis as defined by the selected gridded representation of the Earth’s\\nsurface. Each pixel in the resultant accessibility map thus represents the\\nmodelled shortest time (in minutes) from that location to a hospital or\\nclinic.\\n","license":"proprietary","providers":[{"name":"Malaria Atlas Project","roles":["producer","licensor"],"url":"https://malariaatlas.org/research-project/accessibility-to-cities/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_accessibility_to_healthcare_2019"}],"extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/accessibility_to_healthcare_2019"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_accessibility_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_accessibility_to_healthcare_2019#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/friction_surface_2015_v1_0","title":"Global Friction Surface 2015","description":"This global friction surface enumerates land-based travel speed for all land pixels between 85 degrees north and 60 degrees south for a nominal year 2015.
This map was produced through a collaboration between the University of Oxford Malaria Atlas Project (MAP), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders.
These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce this “friction surface”, a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel, with the fastest travel mode intersecting the pixel being used to determine the speed of travel in that pixel (with some exceptions such as national boundaries, which have the effect of imposing a travel time penalty).
This map represents the travel speed from this allocation process, expressed in units of minutes required to travel one meter. It forms the underlying dataset behind the global accessibility map described in the referenced paper.","license":"proprietary","providers":[{"name":"Malaria Atlas Project, University of Oxford","roles":["producer","licensor"],"url":"http://www.map.ox.ac.uk/accessibility_to_cities/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_friction_surface_2015_v1_0"}],"extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/friction_surface_2015_v1_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_friction_surface_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_friction_surface_2015_v1_0#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Oxford/MAP/friction_surface_2019","title":"Global Friction Surface 2019","description":"This global friction surface enumerates land-based travel speed for all land pixels between 85 degrees north and 60 degrees south for a nominal year 2019. It also includes \\"walking-only\\" travel speed, using non-motorized means of transportation only.\\nThis map was produced through a collaboration between MAP (University of Oxford), Telethon Kids Institute (Perth, Australia), Google, and the University of Twente, Netherlands.\\nThis project builds on previous work published by Weiss et al 2018 ([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)). Weiss et al (2018) utilised datasets for roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a \\"friction surface\\"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. For the current project, an updated friction surface was created to incorporate recent improvements within OSM roads data.\\nDifferences between this friction surface and the 2015 version (Weiss et al. 2018) are not necessarily indicative of changes in infrastructure (e.g., new roads being built). Such discrepancies are far more likely to be associated with improved data quality, in particular updates made to OSM road coverage. As a result, comparisons between the friction surfaces and resulting travel time maps should be done cautiously and generally not interpreted as representing changes in access over time.
This map represents the travel speed from this allocation process, expressed in units of minutes required to travel one metre. It forms the underlying dataset behind the global healthcare accessibility map described in the referenced paper.\\n","license":"proprietary","providers":[{"name":"Malaria Atlas Project","roles":["producer","licensor"],"url":"https://malariaatlas.org/research-project/accessibility-to-cities/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_friction_surface_2019"}],"extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/friction_surface_2019"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Oxford_MAP_friction_surface_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_friction_surface_2019#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"SKYSAT/GEN-A/PUBLIC/ORTHO/MULTISPECTRAL","title":"Planet SkySat Public Ortho Imagery, Multispectral","description":"This data from Planet labs Inc. SkySat satellites was collected for\\nthe experimental \\"Skybox for Good Beta\\" program in 2015, as well as for\\nvarious crisis response events and a few other projects. The data is\\navailable in both a 5-band Multispectral/Pan collection, and a Pansharpened\\nRGB collection.\\n\\nEach image\'s asset ID contains the acquisition date and time, for\\nexample, image s01_20150304T080608Z was acquired on March 4, 2015 at\\n08:06 Zulu (UTC). For more information, please see the [Planet Imagery\\nProduct Specifications]\\n(https://www.planet.com/products/satellite-imagery/files/Planet_Combined_Imagery_Product_Specs_December2017.pdf)\\nand visit the [Planet Imagery and Archive]\\n(https://www.planet.com/products/planet-imagery/) site.\\n\\n\\nThis Multispectral/Pan collection contains images with five 16-bit bands\\nshifted up from the original 12-bit data. The B, G, R, and Near-IR bands\\nhave a resolution of approximately 2m per pixel, while the Pan band is\\napproximately 0.8m resolution (closer to 1m for off-nadir images).\\n","license":"proprietary","providers":[{"name":"Planet Labs Inc.","roles":["producer","licensor"],"url":"https://www.planet.com/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL"}],"extent":{"spatial":{"bbox":[[-180,-70,180,53]]},"temporal":{"interval":[["2014-07-03T00:00:00Z","2016-12-24T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/SKYSAT/GEN-A/PUBLIC/ORTHO/MULTISPECTRAL"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/SKYSAT_MULTISPECTRAL_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"SKYSAT/GEN-A/PUBLIC/ORTHO/RGB","title":"Planet SkySat Public Ortho Imagery, RGB","description":"This data from Planet labs Inc. SkySat satellites was collected for\\nthe experimental \\"Skybox for Good Beta\\" program in 2015, as well as for\\nvarious crisis response events and a few other projects. The data is\\navailable in both a 5-band Multispectral/Pan collection, and a Pansharpened\\nRGB collection.\\n\\nEach image\'s asset ID contains the acquisition date and time, for\\nexample, image s01_20150304T080608Z was acquired on March 4, 2015 at\\n08:06 Zulu (UTC). For more information, please see the [Planet Imagery\\nProduct Specifications]\\n(https://www.planet.com/products/satellite-imagery/files/Planet_Combined_Imagery_Product_Specs_December2017.pdf)\\nand visit the [Planet Imagery and Archive]\\n(https://www.planet.com/products/planet-imagery/) site.\\n\\n\\nThis RGB collection contains images with three pansharpened, 8-bit bands.\\nThe resolution is approximately 0.8m per pixel (closer\\nto 1m for off-nadir images).\\n","license":"proprietary","providers":[{"name":"Planet Labs Inc.","roles":["producer","licensor"],"url":"https://www.planet.com/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB"}],"extent":{"spatial":{"bbox":[[-180,-70,180,53]]},"temporal":{"interval":[["2014-07-03T00:00:00Z","2016-12-24T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/SKYSAT/GEN-A/PUBLIC/ORTHO/RGB"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/SKYSAT_RGB_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"TOMS/MERGED","title":"TOMS and OMI Merged Ozone Data","description":"The Total Ozone Mapping Spectrometer (TOMS) data represent the primary\\nlong-term, continuous record of satellite-based observations available for\\nuse in monitoring global and regional trends in total ozone over the past\\n25 years. The data are produced by the Laboratory for Atmospheres at NASA\'s\\nGoddard Space Flight Center. Version 8 TOMS data products include level 3\\ngridded data (1.0 x 1.25 deg). The Ozone Monitoring Instrument (OMI), aboard\\nthe Aura satellite (July 2004 - current), has a higher resolution\\n(1.0 x 1.0 deg).\\n\\nThese data represent a merged ozone product from TOMS/EarthProbe,\\nTOMS/Nimbus-7, TOMS/Meteor-3, OMI/Aura and USGS-interpolated data for dates\\nwith no data.\\n\\n[Additional TOMS and OMI information](https://ozoneaq.gsfc.nasa.gov/missions/)\\n","license":"proprietary","providers":[{"name":"NASA / GES DISC","roles":["producer","licensor"],"url":"https://disc.gsfc.nasa.gov/information?page=1&keywords=acdisc"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-11-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/TOMS/MERGED"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/OMI_TOMS_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"TRMM/3B42","title":"TRMM 3B42: 3-Hourly Precipitation Estimates","description":"The Tropical Rainfall Measuring Mission (TRMM) is a\\njoint mission between NASA and the Japan Aerospace Exploration\\nAgency (JAXA) designed to monitor and study tropical rainfall.\\nThe 34B2 product contains a gridded, TRMM-adjusted, merged infrared\\nprecipitation (mm/hr) and RMS precipitation-error estimate, with\\na 3-hour temporal resolution and a 0.25 degree spatial resolution.\\n\\nSee the [algorithm description](https://trmm.gsfc.nasa.gov/3b42.html)\\nand the [file specification](https://storm.pps.eosdis.nasa.gov/storm/data/docs/filespec.TRMM.V7.3B42.pdf)\\nfor details.\\n\\nDocumentation:\\n\\n* [PI Documentation](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/3B42_3B43_doc_V7.pdf)\\n\\n* [File Specification for TRMM Products](https://pps.gsfc.nasa.gov/Documents/filespec.TRMM.V7.pdf)\\n\\n* [Comparison between TRMM versions 6 and 7](https://pps.gsfc.nasa.gov/Documents/formatChangesV7.pdf)\\n\\n* [Readme](https://disc2.gesdisc.eosdis.nasa.gov/data/TRMM_L3/TRMM_3B42/doc/README.TRMM_V7.pdf)\\n\\n* [Details of the TMPA algorithm used in this product](https://pmm.nasa.gov/sites/default/files/imce/3B42_3B43_TMPA_restart.pdf)\\n\\n* [TRMM Data Gaps](ftp://gpmweb2.pps.eosdis.nasa.gov/tsdis/AB/docs/anomalous.html)\\n\\n* [Transition from TMPA to IMERG](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/TMPA-to-IMERG_transition.pdf)\\n","license":"proprietary","providers":[{"name":"NASA GES DISC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/TRMM/TMPA/3H/7"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B42"}],"extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1998-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/TRMM/3B42"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/TRMM_3B42_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B42#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"TRMM/3B43V7","title":"TRMM 3B43: Monthly Precipitation Estimates","description":"**This collection is no longer being updated. See\\n[IMERG monthly](https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLYV06)**\\n\\nThis dataset algorithmically merges microwave data from multiple satellites,\\nincluding SSMI, SSMIS, MHS, AMSU-B and AMSR-E, each inter-calibrated to the\\nTRMM Combined Instrument.\\n\\nAlgorithm 3B43 is executed once per calendar month to produce the single,\\nbest-estimate precipitation rate and RMS precipitation-error estimate field\\n(3B43) by combining the 3-hourly merged high-quality/IR estimates (3B42)\\nwith the monthly accumulated Global Precipitation Climatology Centre (GPCC)\\nrain gauge analysis.\\n\\nAll of the global precipitation datasets have some calibrating data source,\\nwhich is necessary to control bias differences between contributing\\nsatellites. The multi-satellite data are averaged to the monthly scale and\\ncombined with the Global Precipitation Climatology Centre\'s (GPCC) monthly\\nsurface precipitation gauge analysis. In each case the multi-satellite data\\nare adjusted to the large-area mean of the gauge analysis, where available\\n(mostly over land), and then combined with the gauge analysis using a\\nsimple inverse estimated-random-error variance weighting. Regions with poor\\ngauge coverage, like central Africa and the oceans, have a higher weighting\\non the satellite input.\\n\\nSee the [algorithm description](https://trmm.gsfc.nasa.gov/3b43.html)\\nand the [file specification](https://pps.gsfc.nasa.gov/Documents/filespec.TRMM.V7.pdf)\\nfor details.\\n","license":"proprietary","providers":[{"name":"NASA GES DISC at NASA Goddard Space Flight Center","roles":["producer","licensor"],"url":"https://doi.org/10.5067/TRMM/TMPA/MONTH/7"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B43V7"}],"extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1998-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/TRMM/3B43V7"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/TRMM_3B43V7_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B43V7#terms-of-use"},{"rel":"source","href":"https://disc2.gesdisc.eosdis.nasa.gov/data/TRMM_L3/TRMM_3B43.7/"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"TUBerlin/BigEarthNet/v1","title":"TUBerlin/BigEarthNet/v1","description":"BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches.\\nTo construct BigEarthNet, 125 Sentinel-2 tiles acquired between June 2017 and May 2018 over the 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg, Portugal, Serbia, Switzerland) of Europe were initially selected. All the tiles were atmospherically corrected by the Sentinel-2 Level 2A product generation and formatting tool (sen2cor).\\nThen, they were divided into 590,326 non-overlapping image patches. Each image patch was annotated by the multiple land-cover classes (i.e., multi-labels) that were provided from the CORINE Land Cover database of the year 2018 (CLC 2018).\\n","license":"proprietary","providers":[{"name":"BigEarthNet","roles":["producer","licensor"],"url":"http://bigearth.net/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/TUBerlin_BigEarthNet_v1"}],"extent":{"spatial":{"bbox":[[-9,36.9,31.6,68.1]]},"temporal":{"interval":[["2017-06-01T00:00:00Z","2018-05-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/TUBerlin/BigEarthNet/v1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/TUBerlin_BigEarthNet_v1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/TUBerlin_BigEarthNet_v1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"Tsinghua/FROM-GLC/GAIA/v10","title":"Tsinghua FROM-GLC year of change to impervious surface","description":"This dataset contains annual change information of global impervious surface area from 1985 to 2018 at a 30m resolution. Change from pervious to impervious was determined using a combined approach of supervised classification and temporal consistency checking. Impervious pixels are defined as above 50% impervious. The year of the transition (from pervious to impervious) can be identified from the pixel value, ranging from 34 (year: 1985) to 1 (year: 2018). For example, the impervious surface in 1990 can be revealed as the pixel value greater than 29 (see the lookup table). This dataset is temporally consistent, following the conversion from pervious (e.g., non-urban) to impervious (e.g., urban) monotonically. For more information about the mapping approach and assessment, see [https://doi.org/10.1016/j.rse.2019.111510](\\"Annual maps of global artificial impervious area (GAIA) between 1985 and 2018\\" (Gong et al. 2020)).","license":"proprietary","providers":[{"name":"Tsinghua University","roles":["producer","licensor"],"url":"http://data.ess.tsinghua.edu.cn/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/Tsinghua/FROM-GLC/GAIA/v10"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/Tsinghua_FROM-GLC_GAIA_v10_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10#terms-of-use"},{"rel":"source","href":"http://data.ess.tsinghua.edu.cn/fromglc2017v1.html"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UCSB-CHG/CHIRPS/DAILY","title":"CHIRPS Daily: Climate Hazards Group InfraRed Precipitation with Station Data (version 2.0 final)","description":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)\\nis a 30+ year quasi-global rainfall dataset. CHIRPS incorporates\\n0.05° resolution satellite imagery with in-situ station data\\nto create gridded rainfall time series for trend analysis and seasonal\\ndrought monitoring.\\n","license":"proprietary","providers":[{"name":"UCSB/CHG","roles":["producer","licensor"],"url":"https://chc.ucsb.edu/data/chirps"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY"}],"extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRPS/DAILY"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CHIRPS_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UCSB-CHG/CHIRPS/PENTAD","title":"CHIRPS Pentad: Climate Hazards Group InfraRed Precipitation with Station Data (version 2.0 final)","description":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)\\nis a 30+ year quasi-global rainfall dataset. CHIRPS incorporates\\n0.05° resolution satellite imagery with in-situ station data\\nto create gridded rainfall time series for trend analysis and seasonal\\ndrought monitoring.\\n","license":"proprietary","providers":[{"name":"UCSB/CHG","roles":["producer","licensor"],"url":"https://chc.ucsb.edu/data/chirps"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD"}],"extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRPS/PENTAD"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CHIRPS_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1","title":"Primary humid tropical forests","description":"Primary humid tropical forests provide numerous global ecosystem services,\\nbut are under continuing threat of clearing from economic drivers. To facilitate\\nnational land use planning and balancing the goals of economic development and\\nmaintenance of ecosystem services, a primary humid tropical forest map was\\ncreated by the UMD GLAD team. The primary forest extent was mapped\\nfor the year 2001 at a spatial resolution of 30 meters using globally acquired,\\nfree-of-charge, and consistently processed Landsat imagery.\\n","license":"proprietary","providers":[{"name":"UMD/GLAD","roles":["producer","licensor"],"url":"https://glad.umd.edu/dataset/primary-forest-humid-tropics"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UMD_GLAD_PRIMARY_HUMID_TROPICAL_FORESTS_v1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2002-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/UMD_GLAD_PRIMARY_HUMID_TROPICAL_FORESTS_v1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UMD_GLAD_PRIMARY_HUMID_TROPICAL_FORESTS_v1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UMD/hansen/global_forest_change_2019_v1_7","title":"Hansen Global Forest Change v1.7 (2000-2019)","description":"Results from time-series analysis of Landsat images in characterizing\\nglobal forest extent and change.\\n\\nThe \'first\' and \'last\' bands are reference multispectral imagery from the\\nfirst and last available years for Landsat spectral bands 3, 4, 5, and 7.\\nReference composite imagery represents median observations from a set of\\nquality-assessed growing-season observations for each of these bands.\\n\\n\\nPlease see the [User Notes](http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html)\\nfor this Version 1.7 update, as well as the associated journal article:\\nHansen, Potapov, Moore, Hancher et al. “High-resolution global maps of\\n21st-century forest cover change.” Science 342.6160 (2013): 850-853.\\n","license":"proprietary","providers":[{"name":"Hansen/UMD/Google/USGS/NASA","roles":["producer","licensor"],"url":"http://earthenginepartners.appspot.com/science-2013-global-forest"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2019_v1_7"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UMD/hansen/global_forest_change_2019_v1_7"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/UMD_hansen_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2019_v1_7#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UMN/PGC/ArcticDEM/V3/2m","title":"ArcticDEM Strips","description":"ArcticDEM is a National Geospatial-Intelligence Agency (NGA) and National\\nScience Foundation (NSF) public-private initiative to automatically produce\\na high-resolution, high-quality digital surface model (DSM) of the Arctic\\nusing optical stereo imagery, high-performance computing, and open source\\nphotogrammetry software. It includes vegetation, tree canopy, buildings, and\\nother man-made surface features. The 2m asset is a collection of strips rather\\nthan a single mosaic due to projection differences between strips.\\n\\n\\nStrip DEM files correspond to the overlapping area of the input stereopair\\nimage swaths as they are collected by DigitalGlobe’s constellation of\\npolar-orbiting satellites. Strip DEM dimensions will vary according to the\\nsatellite sensor that acquired the images and the off-nadir angle of\\ncollection. 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Strip DEM dimensions will vary according to\\nthe satellite sensor that acquired the images and the off-nadir angle of\\ncollection. 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Strip DEM dimensions will vary according to\\nthe satellite sensor that acquired the images and the off-nadir angle of\\ncollection. 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GPP is the amount of\\ncarbon captured by plants in an ecosystem and is an essential component in\\nthe calculations of Net Primary Production (NPP). GPP is calculated using the\\nMOD17 algorithm (see [MOD17 User\\nGuide](http://www.ntsg.umt.edu/files/modis/MOD17UsersGuide2015_v3.pdf)) with\\nLandsat Surface Reflectance, gridMET, and the National Land Cover Database.\\n","license":"proprietary","providers":[{"name":"University of Montana Numerical Terradynamic Simulation Group (NTSG)","roles":["producer","licensor"],"url":"http://www.ntsg.umt.edu/project/landsat/landsat-productivity.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UMT_NTSG_v2_LANDSAT_GPP"}],"extent":{"spatial":{"bbox":[[-124.84,24.42,-64.82,49.72]]},"temporal":{"interval":[["1986-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/LANDSAT/GPP"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/UMT_NTSG_v2_LANDSAT_GPP_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UMT_NTSG_v2_LANDSAT_GPP#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UMT/NTSG/v2/LANDSAT/NPP","title":"Landsat Net Primary Production CONUS","description":"The Landsat Net Primary Production (NPP) CONUS dataset estimates NPP using\\nLandsat Surface Reflectance for CONUS. 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NPP is calculated using the MOD17 algorithm (see [MOD17 User\\nGuide](http://www.ntsg.umt.edu/files/modis/MOD17UsersGuide2015_v3.pdf)) with\\nMODIS Surface Reflectance, gridMET, and the National Land Cover Database.\\n","license":"proprietary","providers":[{"name":"University of Montana Numerical Terradynamic Simulation Group (NTSG)","roles":["producer","licensor"],"url":"http://www.ntsg.umt.edu/project/landsat/landsat-productivity.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UMT_NTSG_v2_MODIS_NPP"}],"extent":{"spatial":{"bbox":[[-124.84,24.42,-64.82,49.72]]},"temporal":{"interval":[["2001-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/MODIS/NPP"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/UMT_NTSG_v2_MODIS_NPP_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UMT_NTSG_v2_MODIS_NPP#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UQ/murray/Intertidal/v1_1/data_mask","title":"Murray Global Intertidal Change Data Mask","description":"The Murray Global Intertidal Change Dataset contains global maps of tidal flat\\necosystems produced via a supervised classification of 707,528 Landsat\\nArchive images. Each pixel was classified into tidal flat, permanent water\\nor other with reference to a globally distributed set of training data.\\n\\nThe classification was implemented along the entire global coastline\\nbetween 60° North and 60° South from 1 January 1984 to 31 December 2016.\\nThe image collection consists consists of a time-series of 11 global maps\\nof tidal flats at 30m pixel resolution for set time-periods (1984−1986;\\n1987−1989; 1990−1992; 1993−1995; 1996−1998; 1999−2001; 2002−2004;\\n2005−2007; 2008−2010; 2011−2013; 2014−2016)\\n\\n\\nFlag indicating the spatial limits of the implementation of the tidal flat\\nclassifier as defined by altitudinal (+100m) and bathymetric (-100m) limits.\\n","license":"proprietary","providers":[{"name":"Murray/UQ/Google/USGS/NASA","roles":["producer","licensor"],"url":"https://intertidal.app"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_data_mask"}],"extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/data_mask"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/UQ_murray_Intertidal_v1_1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_data_mask#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1038/s41586-018-0805-8"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UQ/murray/Intertidal/v1_1/global_intertidal","title":"Murray Global Intertidal Change Classification","description":"The Murray Global Intertidal Change Dataset contains global maps of tidal flat\\necosystems produced via a supervised classification of 707,528 Landsat\\nArchive images. Each pixel was classified into tidal flat, permanent water\\nor other with reference to a globally distributed set of training data.\\n\\nThe classification was implemented along the entire global coastline\\nbetween 60° North and 60° South from 1 January 1984 to 31 December 2016.\\nThe image collection consists consists of a time-series of 11 global maps\\nof tidal flats at 30m pixel resolution for set time-periods (1984−1986;\\n1987−1989; 1990−1992; 1993−1995; 1996−1998; 1999−2001; 2002−2004;\\n2005−2007; 2008−2010; 2011−2013; 2014−2016)\\n\\n\\nThis product depicts tidal flat ecosystems around the global coastline.\\n\\nPixels classified as tidal flat in the analysis represent several types of\\ntidal flat ecosystems, including unconsolidated fine-grain sediments (tidal\\nmudflats), unconsolidated coarse-grain sediments (tidal sand flats), and\\nconsolidated sediments, organic material or rocks (wide tidal\\nrock-platforms), while excluding spectral signatures indicating the\\npresence of vegetation dominated intertidal ecosystems such as mangroves\\nand vegetated marshes. The analysis aimed to identify pixels that are\\nsubject to regular tidal inundation, and therefore may also include other\\nintertidal systems where intertidal dynamics are observable.\\n","license":"proprietary","providers":[{"name":"Murray/UQ/Google/USGS/NASA","roles":["producer","licensor"],"url":"https://intertidal.app"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_global_intertidal"}],"extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/global_intertidal"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/UQ_murray_Intertidal_v1_1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_global_intertidal#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1038/s41586-018-0805-8"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UQ/murray/Intertidal/v1_1/qa_pixel_count","title":"Murray Global Intertidal Change QA Pixel Count","description":"The Murray Global Intertidal Change Dataset contains global maps of tidal flat\\necosystems produced via a supervised classification of 707,528 Landsat\\nArchive images. Each pixel was classified into tidal flat, permanent water\\nor other with reference to a globally distributed set of training data.\\n\\nThe classification was implemented along the entire global coastline\\nbetween 60° North and 60° South from 1 January 1984 to 31 December 2016.\\nThe image collection consists consists of a time-series of 11 global maps\\nof tidal flats at 30m pixel resolution for set time-periods (1984−1986;\\n1987−1989; 1990−1992; 1993−1995; 1996−1998; 1999−2001; 2002−2004;\\n2005−2007; 2008−2010; 2011−2013; 2014−2016)\\n\\n\\nThe number of Landsat images used to develop the Landsat covariate layers\\nin each time step of the tidal flat classification. Each image in the image\\ncollection refers to a single time step.\\n","license":"proprietary","providers":[{"name":"Murray/UQ/Google/USGS/NASA","roles":["producer","licensor"],"url":"https://intertidal.app"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_qa_pixel_count"}],"extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/qa_pixel_count"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/UQ_murray_Intertidal_v1_1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_qa_pixel_count#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1038/s41586-018-0805-8"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USDA/NAIP/DOQQ","title":"NAIP: National Agriculture Imagery Program","description":"The National Agriculture Imagery Program (NAIP) acquires aerial imagery\\nduring the agricultural growing seasons in the continental U.S.\\n\\nNAIP projects are contracted each year based upon available funding and the\\nFSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on\\na 5-year cycle. 2008 was a transition year, and a three-year cycle began\\nin 2009.\\n\\nNAIP imagery is acquired at a one-meter ground sample distance (GSD) with a\\nhorizontal accuracy that matches within six meters of photo-identifiable\\nground control points, which are used during image inspection.\\n\\nOlder images were collected using 3 bands (Red, Green, and Blue: RGB), but\\nnewer imagery is usually collected with an additional near-infrared band\\n(RGBN). RGB asset ids begin with \'n_\', NRG asset ids begin with \'c_\', RGBN\\nasset ids begin with \'m_\'.\\n","license":"proprietary","providers":[{"name":"USDA Farm Service Agency","roles":["producer","licensor"],"url":"https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ"}],"extent":{"spatial":{"bbox":[[-124.84,24.42,-64.82,49.72]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USDA/NAIP/DOQQ"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/USDA_NAIP_DOQQ_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USDA/NASS/CDL","title":"USDA NASS Cropland Data Layers","description":"The Cropland Data Layer (CDL) is a crop-specific land cover data layer\\ncreated annually for the continental United States using moderate\\nresolution satellite imagery and extensive agricultural ground truth.\\nThe CDL is created by the USDA, National Agricultural Statistics\\nService (NASS), Research and Development Division, Geospatial\\nInformation Branch, Spatial Analysis Research Section.\\n\\nFor detailed FAQ please visit [CropScape and Cropland Data\\nLayers - FAQs](https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php).\\n","license":"proprietary","providers":[{"name":"USDA National Agricultural Statistics Service","roles":["producer","licensor"],"url":"https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL"}],"extent":{"spatial":{"bbox":[[-104.43,45.7,-96.54,49.23]]},"temporal":{"interval":[["1997-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USDA/NASS/CDL"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/CROPLAND_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USGS/GAP/AK/2001","title":"USGS GAP Alaska 2001","description":"The GAP/LANDFIRE National Terrestrial Ecosystems data represents a detailed\\nvegetation and land cover classification for the Conterminous U.S., Alaska,\\nHawaii, and Puerto Rico.GAP/LF 2011 Ecosystems for the Conterminous U.S. is\\nan update of the National Gap Analysis Program Land Cover Data - 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The GFSAD products are derived\\nthrough multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR),\\nsecondary data, and field-plot data and aims at documenting cropland\\ndynamics.\\n\\nAt a nominal 1km scale, V0.1 provides the spatial distribution of a\\ndisaggregated five-class global cropland extent map derived from four\\nmajor studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010),\\nYu et al. (2013), and Friedl et al. (2010). V1.0 is a 5-class product that\\nprovides information on global cropland extent and irrigated versus rainfed\\ncropping. There is no crop type or crop type dominance information. Cropping\\nintensity (single, double, triple, and continuous crops) can be obtained for\\nevery pixel using time-series remote sensing data. The GFSAD1000 nominal\\n2010 product was created with data from 2007 to 2012.\\n","license":"proprietary","providers":[{"name":"Global Food Security-support Analysis Data at 30m Project (GFSAD30)","roles":["producer","licensor"],"url":"http://geography.wr.usgs.gov/science/croplands/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GFSAD1000_V1"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GFSAD1000_V1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/USGS_GFSAD1000_V1_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GFSAD1000_V1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USGS/GMTED2010","title":"GMTED2010: Global Multi-resolution Terrain Elevation Data 2010","description":"The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) dataset contains elevation data for the globe collected from various sources. The version of the dataset available here is Breakline Emphasis, 7.5 arc-seconds resolution. Breakline emphasis maintains the critical topographic features (streams or ridges) within the landscape by maintaining any minimum elevation or maximum elevation value on a breakline that passes within the specified analysis window. More details are available in the dataset [report](https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf).\\n\\nThe primary source dataset for GMTED2010 is NGA\'s SRTM Digital Terrain Elevation Data (DTED®, [http://www2.jpl.nasa.gov/srtm/](http://www2.jpl.nasa.gov/srtm/)) (void-filled) 1-arc-second data. For the geographic areas outside the SRTM coverage area and to fill in remaining holes in the SRTM data, the following sources were used: non-SRTM DTED®, Canadian Digital Elevation Data (CDED) at two resolutions, Satellite Pour l\'Observation de la Terre (SPOT 5) Reference3D, National Elevation Dataset (NED) for the continental United States and Alaska, GEODATA 9 second digital elevation model (DEM) for Australia, an Antarctica satellite radar and laser altimeter DEM, and a Greenland satellite radar altimeter DEM.\\n\\nThis dataset replaces the GTOPO30 Elevation Model.","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://www.usgs.gov/core-science-systems/eros/coastal-changes-and-impacts/gmted2010"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GMTED2010"}],"extent":{"spatial":{"bbox":[[-180,-56,180,84]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GMTED2010"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/USGS_GMTED2010_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GMTED2010#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USGS/GTOPO30","title":"GTOPO30: Global 30 Arc-Second Elevation","description":"GTOPO30 is a global digital elevation model (DEM) with a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). The DEM was derived from several raster and vector sources of topographic information. Completed in late 1996, GTOPO30 was developed over a three-year period through a collaborative effort led by the U.S. Geological Survey\'s Center for Earth Resources Observation and Science (EROS). The following organizations participated by contributing funding or source data: the National Aeronautics and Space Administration (NASA), the United Nations Environment Programme/Global Resource Information Database (UNEP/GRID), the U.S. Agency for International Development (USAID), the Instituto Nacional de Estadistica Geografica e Informatica (INEGI) of Mexico, the Geographical Survey Institute (GSI) of Japan, Manaaki Whenua Landcare Research of New Zealand, and the Scientific Committee on Antarctic Research (SCAR).","license":"proprietary","providers":[{"name":"United States Geological Survey","roles":["producer","licensor"],"url":"https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-30-arc-second-elevation-gtopo30"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GTOPO30"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1996-01-01T00:00:00Z","1996-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GTOPO30"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/USGS_GTOPO30_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GTOPO30#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USGS/LIMA/MOSAIC","title":"Landsat Image Mosaic of Antarctica (LIMA) 16-Bit Pan-sharpened Mosaic","description":"The Landsat Image Mosaic of Antarctica (LIMA) is a seamless\\nand virtually cloudless mosaic created from processed\\nLandsat 7 ETM+ scenes.\\n\\n\\nThis LIMA dataset is the 16-bit Intermediate LIMA.\\nThe mosaic consists of pan-sharpened normalized surface reflectance\\nscenes (Landsat ETM+ bands 1, 2, 3, and 4). The mosaic was\\nconstructed by ordering cloud free images on top and trimming\\nimage boundaries when tile discontinuities occurred.\\n\\nUsers can find the mosaic tile footprints available at:\\n[USGS/LIMA/MOSAIC_TILE_FOOTPRINTS](https://code.earthengine.google.com/?asset=USGS/LIMA/MOSAIC_TILE_FOOTPRINTS)\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://lima.usgs.gov/index.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USGS_LIMA_MOSAIC"}],"extent":{"spatial":{"bbox":[[-180,-90,180,-55]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USGS/LIMA/MOSAIC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LIMA_MOSAIC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_LIMA_MOSAIC#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USGS/LIMA/SR","title":"Landsat Image Mosaic of Antarctica (LIMA) - Processed Landsat Scenes (16 bit)","description":"The Landsat Image Mosaic of Antarctica (LIMA) is a seamless\\nand virtually cloudless mosaic created from processed\\nLandsat 7 ETM+ scenes.\\n\\n\\nProcessed Landsat Scenes (16 bit) are Level 1Gt NLAPS\\nscenes converted to 16 bit, processed with sun-angle\\ncorrection, and converted to reflectance values\\n([Bindschadler 2008](https://lima.usgs.gov/LIMA_paper.pdf)).\\n\\nEach Landsat scene is processed with elevation\\ndata and sun-angle correction to ensure surface features\\nwere accurately represented. The sun\'s angle in Antarctica\\ngives the appearance of a setting sun. Because of the low\\nsun angle, as Landsat passes over Antarctica, the outer\\nedges of the continent appear brighter than areas closer\\nto the South Pole, so scenes have bright and dark areas.\\nInconsistent sun angles and shadows where corrected for\\nthese scenes. Without this process, mosaicking would produce\\na patchwork of scenes since each scene would have a brighter\\nand a darker side.\\n\\nUsers can find individual image metadata available as a table at:\\n[USGS/LIMA/SR_METADATA](https://code.earthengine.google.com/?asset=USGS/LIMA/SR_METADATA)\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"https://lima.usgs.gov/index.php"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USGS_LIMA_SR"}],"extent":{"spatial":{"bbox":[[-180,-90,180,-55]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USGS/LIMA/SR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/LIMA_MOSAIC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_LIMA_SR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USGS/NED","title":"USGS National Elevation Dataset 1/3 arc-second","description":"The National Elevation Dataset (NED) used to be* the\\nprimary elevation data product of the USGS. The NED is a seamless\\ndataset with the best available raster elevation data of the\\nconterminous United States, parts of Alaska, Hawaii, and some\\nterritorial islands. The NED is derived from diverse source data\\nthat are processed to a common coordinate system and unit of\\nvertical measure. NED data are distributed in conformance with\\nthe North American Datum of 1983 (NAD 83). All elevation values\\nare in meters and, over the conterminous United States, are referenced\\nto the North American Vertical Datum of 1988 (NAVD 88). The vertical\\nreference will vary in other areas.\\n\\n*For over 15 years, the NED was the primary elevation data product\\nof the USGS\' National Map. However, it has been renamed and now\\nconsidered just one component of elevation in The National\\nMap after the 3D Elevation Program became operational.\\n","license":"proprietary","providers":[{"name":"United States Geological Survey","roles":["producer","licensor"],"url":"https://nationalmap.gov/elevation.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NED"}],"extent":{"spatial":{"bbox":[[-171,-15,164,70]]},"temporal":{"interval":[["2012-02-07T00:00:00Z","2012-02-07T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NED"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/NED_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NED#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USGS/NLCD","title":"NLCD: USGS National Land Cover Database","description":"NLCD (the National Land Cover Database) is a 30-m Landsat-based land cover\\ndatabase spanning 8 epochs (1992, 2001, 2004, 2006, 2008, 2011, 2013 and 2016).\\n1992 data are primarily based on unsupervised classification of Landsat data,\\nwhile the rest of the images rely on the imperviousness data layer for the\\nurban classes and on a decision-tree classification for the rest. The 1992\\nimage is not directly comparable to any later editions of NLCD.\\n\\nThis dataset has one image for the continental US for each of the\\neight epochs, plus separate images for Alaska, Hawaii, and Puerto Rico in 2001 and 2011.\\n(They could not be merged in due to having a different projection from the continental US\\nimages.)\\n\\nNote that the impervious layers and tree cover layers for Alaska 2011\\nonly have data for parts of Alaska. For the impervious layer, only the\\nparts of Kenai Peninsula Borough and Anchorage Municipality are available.\\nTree cover is only available on a strip along the coast from the southern\\ntip of the Alaskan Panhandle up through the Kenai Peninsula, plus Kodiak\\nIsland.\\n\\nNLCD 2016 Shrub Component products characterize the percentage of each\\n30-meter pixel in the Western United States covered by shrub, herbaceous,\\nbare ground, litter, sagebrush, big sagebrush and annual herbaceous, along\\nwith estimating shrub height and sagebrush height. These products have\\nbeen produced by USGS in collaboration with the Bureau of Land Management.\\n\\nNLCD products are created by the Multi-Resolution Land Characteristics\\n(MRLC) Consortium, a partnership of federal agencies led by the\\nU.S. Geological Survey.\\n","license":"proprietary","providers":[{"name":"USGS","roles":["producer","licensor"],"url":"http://www.mrlc.gov"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD"}],"extent":{"spatial":{"bbox":[[-130.24,21.75,-63.66,57.68]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/USGS_NLCD_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"USGS/SRTMGL1_003","title":"NASA SRTM Digital Elevation 30m","description":"The Shuttle Radar Topography Mission (SRTM, see [Farr\\net al. 2007](http://onlinelibrary.wiley.com/doi/10.1029/2005RG000183/full))\\ndigital elevation data is an international research effort that\\nobtained digital elevation models on a near-global scale. This\\nSRTM V3 product (SRTM Plus) is provided by NASA JPL\\nat a resolution of 1 arc-second (approximately 30m).\\n\\nThis dataset has undergone a void-filling process using open-source data\\n(ASTER GDEM2, GMTED2010, and NED), as opposed to other versions that\\ncontain voids or have been void-filled with commercial sources.\\nFor more information on the different versions see the\\n[SRTM Quick Guide](https://lpdaac.usgs.gov/documents/13/SRTM_Quick_Guide.pdf).\\n\\nDocumentation:\\n\\n* [User\'s Guide](https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf)\\n\\n* [General Documentation](https://lpdaac.usgs.gov/documents/13/SRTM_Quick_Guide.pdf)\\n\\n* [Algorithm Theoretical Basis Document (ATBD)](https://doi.org/10.1029/2005RG000183)\\n","license":"proprietary","providers":[{"name":"NASA / USGS / JPL-Caltech","roles":["producer","licensor"],"url":"https://cmr.earthdata.nasa.gov/search/concepts/C1000000240-LPDAAC_ECS.html"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003"}],"extent":{"spatial":{"bbox":[[-180,-56,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/USGS/SRTMGL1_003"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/SRTM90_V4_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"UTOKYO/WTLAB/KBDI/v1","title":"KBDI: Keetch-Byram Drought Index","description":"Keetch-Byram Drought Index (KBDI) is a continuous reference scale for estimating the dryness of\\nthe soil and duff layers. The index increases for each day without rain (the amount of increase\\ndepends on the daily high temperature) and decreases when it rains. This system is based\\nprimarily on recent rainfall patterns. It is a measure of meteorological drought; it reflects\\nwater gain or loss within the soil.\\n\\nThe scale ranges from 0 (no moisture deficit) to 800 (extreme drought). The range of the index\\nis determined by assuming that there is 20 cm of moisture in a saturated soil that is readily\\navailable to the vegetation (Keetch and Byram, 1968). KBDI is world widely used for drought\\nmonitoring for national weather forecast, wildfire prevention and usefully especially in regions\\nwith rain-fed crops.\\n","license":"proprietary","providers":[{"name":"Institute of Industrial Science, The University of Tokyo, Japan","roles":["producer","licensor"],"url":"http://wtlab.iis.u-tokyo.ac.jp/DMEWS/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1"}],"extent":{"spatial":{"bbox":[[60,-60,180,60]]},"temporal":{"interval":[["2007-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/UTOKYO/WTLAB/KBDI/v1"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/UTOKYO_WTLAB_KBDI_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"VITO/PROBAV/C1/S1_TOC_100M","title":"PROBA-V C1 Top Of Canopy Daily Synthesis 100m","description":"Proba-V is a satellite mission tasked to map land cover and vegetation\\ngrowth. It was designed to provide continuity for the VGT optical instrument\\nfrom the SPOT-4 and SPOT-5 missions.\\n\\nThe sensor collects data in three VNIR (visible and near-infrared) bands\\nand one SWIR (short-wave infrared) spectral band with a 2250km field of\\nview. Global images are produced every 2 days at 300m resolution, and a 100m\\nimage from nadir observations every 5 days. These images are later\\ncomposited to produce this daily synthesis dataset. The description of\\nthe compositing and atmospheric correction procedures can be found in\\nthe [user manual](http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf).\\n\\nThe reflectances provided in this dataset are presented as Digital Count\\nNumbers (DN) and must be converted according to the guidelines in\\nSection 4.6.1 of the [user manual](http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf).\\n","license":"proprietary","providers":[{"name":"Vito / ESA","roles":["producer","licensor"],"url":"http://proba-v.vgt.vito.be/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_100M"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-10-17T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/C1/S1_TOC_100M"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/VITO_PROBAV_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_100M#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"VITO/PROBAV/C1/S1_TOC_333M","title":"PROBA-V C1 Top Of Canopy Daily Synthesis 333m","description":"Proba-V is a satellite mission tasked to map land cover and vegetation\\ngrowth. It was designed to provide continuity for the VGT optical instrument\\nfrom the SPOT-4 and SPOT-5 missions.\\n\\nThe sensor collects data in three VNIR (visible and near-infrared) bands\\nand one SWIR (short-wave infrared) spectral band with a 2250km field of\\nview. Global images are produced every 2 days at 300m resolution, and a 100m\\nimage from nadir observations every 5 days. These images are later\\ncomposited to produce this daily synthesis dataset. The description of\\nthe compositing and atmospheric correction procedures can be found in\\nthe [user manual](http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf).\\n\\nThe reflectances provided in this dataset are presented as Digital Count\\nNumbers (DN) and must be converted according to the guidelines in\\nSection 4.6.1 of the [user manual](http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf).\\n","license":"proprietary","providers":[{"name":"Vito / ESA","roles":["producer","licensor"],"url":"http://proba-v.vgt.vito.be/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_333M"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-10-17T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/C1/S1_TOC_333M"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/VITO_PROBAV_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_333M#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WCMC/biomass_carbon_density/v1_0","title":"WCMC Above and below ground biomass carbon density","description":"This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.\\nThe dataset was constructed by combining the most reliable publicly available datasets and overlaying them with the ESA CCI landcover map for the year 2010 (ESA, 2017), assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell\'s landcover type.\\nInput carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature.\\nTo determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see Table 1 in paper cited for further information on datasets selected).\\nAfter aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas.\\nWoodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest.\\nBiomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.\\nBelow-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above-and-below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.\\nThis dataset has not been validated.\\n\\nReferences:\\n\\n* Bouvet, A. et al. 2018. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sensing of the Environment 206, 156-173.\\n* ESA (2017) [Land Cover CCI Product User Guide Version 2. Tech. Rep.](https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf).\\n* IPCC (2006) 2006 IPCC guidelines for national greenhouse gas inventories (eds HS Eggleston, L Buendia, K Miwa, T Ngara, K Tanabe.) Kanagawa, Japan: IGES.\\n* Santoro, M. et al. (2018). A detailed portrait of the forest aboveground biomass pool for the year 2010 obtained from multiple remote sensing observations. Geophysical Research Abstracts 20, EGU2018–EG18932.\\n* Spawn SA et al. (2017) New global biomass map for the year 2010. New Orleans, LA: American Geophysical Union.\\n* Xia, J. et al. (2014) Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006. Remote Sensing 6, 1783–1802.\\n","license":"proprietary","providers":[{"name":"UNEP-WCMC (UN Environment Programme World Conservation Monitoring Centre)","roles":["producer","licensor"],"url":"https://doi.org/10.34892/rh7v-hg80"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WCMC_biomass_carbon_density_v1_0"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WCMC/biomass_carbon_density/v1_0"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WCMC_biomass_carbon_density_v1_0_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WCMC_biomass_carbon_density_v1_0#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.34892/rh7v-hg80"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WHBU/NBAR_1YEAR","title":"MODIS 1-year NBAR Mosaic","description":"The mosaics are created by using the MODIS 8-Day 500 meter\\nBRDF-Albedo Quality product. Data Quality flags are used to select\\nthe best observations from the MODIS 8-day 500 meter Nadir\\nBRDF-Adjusted Reflectance imagery product. This specific mosaic picks\\nobservations from the three highest quality categories over\\na 1-year period of MODIS data.\\n\\nThe MODIS NBAR annual mosaics have been used by government\\nagencies and NGOs in tropical countries as input to generate\\naboveground biomass/carbon maps. The Woods Hole Research Center has\\nbeen using the dataset for country capacity building and technical\\ntraining workshops in tropical countries.\\n","license":"proprietary","providers":[{"name":"Baccini, A. (Woods Hole Research Center), Sulla-Menashe, D. (Boston University)","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_1YEAR"}],"extent":{"spatial":{"bbox":[[-180,-60.1,180,60.1]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_1YEAR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/baccini_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_1YEAR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WHBU/NBAR_2YEAR","title":"MODIS 2-year NBAR Mosaic","description":"The mosaics are created by using the MODIS 8-Day 500 meter\\nBRDF-Albedo Quality product. Data Quality flags are used to select\\nthe best observations from the MODIS 8-day 500 meter Nadir\\nBRDF-Adjusted Reflectance imagery product. This specific mosaic picks\\nobservations from the three highest quality categories over\\na 2-year period of MODIS data.\\n\\nThe MODIS NBAR annual mosaics have been used by government\\nagencies and NGOs in tropical countries as input to generate\\naboveground biomass/carbon maps. The Woods Hole Research Center has\\nbeen using the dataset for country capacity building and technical\\ntraining workshops in tropical countries.\\n","license":"proprietary","providers":[{"name":"Baccini, A. (Woods Hole Research Center), Sulla-Menashe, D. (Boston University)","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_2YEAR"}],"extent":{"spatial":{"bbox":[[-180,-60.1,180,60.1]]},"temporal":{"interval":[["2001-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_2YEAR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/baccini_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_2YEAR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WHBU/NBAR_3YEAR","title":"MODIS 3-year NBAR Mosaic","description":"The mosaics are created by using the MODIS 8-Day 500 meter\\nBRDF-Albedo Quality product. Data Quality flags are used to select\\nthe best observations from the MODIS 8-day 500 meter Nadir\\nBRDF-Adjusted Reflectance imagery product. This specific mosaic picks\\nobservations from the three highest quality categories over\\na 3-year period of MODIS data.\\n\\nThe MODIS NBAR annual mosaics have been used by government\\nagencies and NGOs in tropical countries as input to generate\\naboveground biomass/carbon maps. The Woods Hole Research Center has\\nbeen using the dataset for country capacity building and technical\\ntraining workshops in tropical countries.\\n","license":"proprietary","providers":[{"name":"Baccini, A. (Woods Hole Research Center), Sulla-Menashe, D. (Boston University)","roles":["producer","licensor"],"url":""},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_3YEAR"}],"extent":{"spatial":{"bbox":[[-180,-60.1,180,60.1]]},"temporal":{"interval":[["2002-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_3YEAR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/baccini_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_3YEAR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WHRC/biomass/tropical","title":"WHRC Pantropical National Level Carbon Stock Dataset","description":"A national-level map of above-ground live woody biomass density for\\ntropical countries at 500m. This dataset was assembled from a combination\\nof co-located field measurements, LiDAR observations, and imagery recorded\\nfrom the Moderate Resolution Imaging Spectroradiometer (MODIS).\\n","license":"proprietary","providers":[{"name":"WHRC","roles":["producer","licensor"],"url":"http://whrc.org/publications-data/datasets/pantropical-national-level-carbon-stock/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WHRC_biomass_tropical"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-29T00:00:00Z","2012-01-29T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WHRC/biomass/tropical"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WHRC_biomass_tropical_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WHRC_biomass_tropical#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WORLDCLIM/V1/BIO","title":"WorldClim BIO Variables V1","description":"WorldClim V1 Bioclim provides bioclimatic variables that are derived from\\nthe monthly temperature and rainfall in order to generate more biologically\\nmeaningful values.\\n\\nThe bioclimatic variables represent annual trends (e.g., mean annual\\ntemperature, annual precipitation), seasonality (e.g., annual range in\\ntemperature and precipitation), and extreme or limiting environmental\\nfactors (e.g., temperature of the coldest and warmest month, and\\nprecipitation of the wet and dry quarters).\\n\\nThe bands scheme follows that of ANUCLIM, except that for temperature\\nseasonality the standard deviation was used because a coefficient of\\nvariation does not make sense with temperatures between -1 and 1.\\n\\nWorldClim version 1 was developed by Robert J. Hijmans, Susan Cameron,\\nand Juan Parra, at the Museum of Vertebrate Zoology, University of\\nCalifornia, Berkeley, in collaboration with Peter Jones and Andrew Jarvis\\n(CIAT), and with Karen Richardson (Rainforest CRC).\\n","license":"proprietary","providers":[{"name":"University of California, Berkeley","roles":["producer","licensor"],"url":"http://www.worldclim.org/version1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1960-01-01T00:00:00Z","1991-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WORLDCLIM/V1/BIO"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WORLDCLIM_V1_BIO_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WORLDCLIM/V1/MONTHLY","title":"WorldClim Climatology V1","description":"WorldClim version 1 has average monthly global climate data for minimum,\\nmean, and maximum temperature and for precipitation.\\n\\nWorldClim version 1 was developed by Robert J. Hijmans, Susan Cameron,\\nand Juan Parra, at the Museum of Vertebrate Zoology, University\\nof California, Berkeley, in collaboration with Peter Jones and\\nAndrew Jarvis (CIAT), and with Karen Richardson (Rainforest CRC).\\n","license":"proprietary","providers":[{"name":"University of California, Berkeley","roles":["producer","licensor"],"url":"http://www.worldclim.org/version1"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_MONTHLY"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1960-01-01T00:00:00Z","1991-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WORLDCLIM/V1/MONTHLY"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WORLDCLIM_V1_BIO_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_MONTHLY#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WRI/GFW/FORMA/alerts","title":"FORMA Alerts","description":"\'**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\\nuser experience and reduce redundancy.\\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\\n[GLAD](http://glad-forest-alert.appspot.com/) were more frequently used.\\nMoreover, using GLAD as a standard, found that Terra-i outperformed\\nFORMA globally.\\n\\nFORMA alerts are detected using a combination of two MODIS\\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\\n(Fires Information for Resource Management System). NDVI updates are\\nprocessed every 16 days, while fire updates are processed daily. Models\\nare developed individually for each ecogroup to relate the two inputs to\\nthe area of clearing, using the Hansen annual tree cover loss data to train\\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\\ncleared, though thresholds vary by ecogroup to minimize false positives.\\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\\nfor a quick introduction to the FORMA datasets.\'\\n\\n\\nThe percentage of clearing takes a value of 0, no clearing detected,\\nor in the range [ecogroup_bound:100), where ecogroup_bound is given by\\nWRI/GFW/FORMA/thresholds .\\nThe time periods over which data is collected varies by N-days, where N is the number\\nof days between the alert_date and the last MODIS NDVI update.\\n","license":"proprietary","providers":[{"name":"World Resources Institute / Global Forest Watch","roles":["producer","licensor"],"url":"http://www.globalforestwatch.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_alerts"}],"extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2012-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/alerts"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WRI_GFW_FORMA_alerts_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_alerts#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WRI/GFW/FORMA/raw_output_firms","title":"FORMA Raw Output FIRMS","description":"\'**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\\nuser experience and reduce redundancy.\\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\\n[GLAD](http://glad-forest-alert.appspot.com/) were more frequently used.\\nMoreover, using GLAD as a standard, found that Terra-i outperformed\\nFORMA globally.\\n\\nFORMA alerts are detected using a combination of two MODIS\\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\\n(Fires Information for Resource Management System). NDVI updates are\\nprocessed every 16 days, while fire updates are processed daily. Models\\nare developed individually for each ecogroup to relate the two inputs to\\nthe area of clearing, using the Hansen annual tree cover loss data to train\\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\\ncleared, though thresholds vary by ecogroup to minimize false positives.\\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\\nfor a quick introduction to the FORMA datasets.\'\\n\\n\\nThe images in this ImageCollection contain the raw FORMA data calculated after new \\nMODIS FIRMS data becomes available, approximately every day. \\n\\nEach band gives a percentage of clearing (from 0 to 100) for\\ndifferent accumulation periods. \\"N\\" is the number of days between the latest FIRMS update\\nand the previous NDVI update. N is given by the ‘date_delta’ property.\\n","license":"proprietary","providers":[{"name":"World Resources Institute / Global Forest Watch","roles":["producer","licensor"],"url":"http://www.globalforestwatch.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_raw_output_firms"}],"extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2017-04-08T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/raw_output_firms"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WRI_GFW_FORMA_raw_output_firms_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_raw_output_firms#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WRI/GFW/FORMA/raw_output_ndvi","title":"FORMA Raw Output NDVI","description":"\'**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\\nuser experience and reduce redundancy.\\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\\n[GLAD](http://glad-forest-alert.appspot.com/) were more frequently used.\\nMoreover, using GLAD as a standard, found that Terra-i outperformed\\nFORMA globally.\\n\\nFORMA alerts are detected using a combination of two MODIS\\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\\n(Fires Information for Resource Management System). NDVI updates are\\nprocessed every 16 days, while fire updates are processed daily. Models\\nare developed individually for each ecogroup to relate the two inputs to\\nthe area of clearing, using the Hansen annual tree cover loss data to train\\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\\ncleared, though thresholds vary by ecogroup to minimize false positives.\\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\\nfor a quick introduction to the FORMA datasets.\'\\n\\n\\nThe images in this ImageCollection contain the raw FORMA data calculated after new \\nMODIS NDVI data becomes available, approximately every 16 days. \\n\\nThe image contains 4 bands. The ‘delta’, ‘near_term_delta’, and ‘clearing’ bands \\nall give a percentage of clearing (from 0 to 100) for different accumulation periods. \\nAccuracy gives a measure of confidence in these predictions \\n(0 = very little confidence, 100 is high confidence).\\n","license":"proprietary","providers":[{"name":"World Resources Institute / Global Forest Watch","roles":["producer","licensor"],"url":"http://www.globalforestwatch.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_raw_output_ndvi"}],"extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2012-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/raw_output_ndvi"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WRI_GFW_FORMA_raw_output_ndvi_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_raw_output_ndvi#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WRI/GFW/FORMA/thresholds","title":"FORMA alert thresholds","description":"\'**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\\nuser experience and reduce redundancy.\\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\\n[GLAD](http://glad-forest-alert.appspot.com/) were more frequently used.\\nMoreover, using GLAD as a standard, found that Terra-i outperformed\\nFORMA globally.\\n\\nFORMA alerts are detected using a combination of two MODIS\\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\\n(Fires Information for Resource Management System). NDVI updates are\\nprocessed every 16 days, while fire updates are processed daily. Models\\nare developed individually for each ecogroup to relate the two inputs to\\nthe area of clearing, using the Hansen annual tree cover loss data to train\\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\\ncleared, though thresholds vary by ecogroup to minimize false positives.\\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\\nfor a quick introduction to the FORMA datasets.\'\\n\\n\\nThis image contains the thresholds at which, when crossed, a FORMA alert is\\nproduced. The values are equal to max(25,40%*RegionalMax), where RegionalMax\\nis the historical maximum clearing for a pixel in this ecogroup. Ecogroup\\ngeometries can be found [here](https://fusiontables.google.com/DataSource?docid=1sqNt2yENpQI5JqLpFONJ2ppnqL77G9XkzKM9VdyS#rows:id=1).\\n","license":"proprietary","providers":[{"name":"World Resources Institute / Global Forest Watch","roles":["producer","licensor"],"url":"http://www.globalforestwatch.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_thresholds"}],"extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/thresholds"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WRI_GFW_FORMA_thresholds_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_thresholds#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WRI/GFW/FORMA/vegetation_tstats","title":"FORMA Vegetation T-Statistics","description":"\'**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\\nuser experience and reduce redundancy.\\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\\n[GLAD](http://glad-forest-alert.appspot.com/) were more frequently used.\\nMoreover, using GLAD as a standard, found that Terra-i outperformed\\nFORMA globally.\\n\\nFORMA alerts are detected using a combination of two MODIS\\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\\n(Fires Information for Resource Management System). NDVI updates are\\nprocessed every 16 days, while fire updates are processed daily. Models\\nare developed individually for each ecogroup to relate the two inputs to\\nthe area of clearing, using the Hansen annual tree cover loss data to train\\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\\ncleared, though thresholds vary by ecogroup to minimize false positives.\\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\\nfor a quick introduction to the FORMA datasets.\'\\n\\n\\nThe images in this ImageCollection contain the “reversed rectified\\nt-statistics” used in calculating NTT, the vegetation color index derived\\nfrom MODIS NDVI that FORMA uses to measure browning. Using a sum reducer on\\nover various date ranges in this ImageCollection produces an “NTT” image.\\n\\nThe images are broken by \\"ecogroup\\". Ecogroup geometries can be found\\n[here](https://fusiontables.google.com/DataSource?docid=1sqNt2yENpQI5JqLpFONJ2ppnqL77G9XkzKM9VdyS#rows:id=1).\\n","license":"proprietary","providers":[{"name":"World Resources Institute / Global Forest Watch","roles":["producer","licensor"],"url":"http://www.globalforestwatch.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_vegetation_tstats"}],"extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2012-01-01T00:00:00Z",null]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/vegetation_tstats"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WRI_GFW_FORMA_vegetation_tstats_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_vegetation_tstats#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/03CONDEM","title":"WWF HydroSHEDS Hydrologically Conditioned DEM, 3 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis hydrologically conditioned elevation dataset is the result of an iterative conditioning and correction process. Note that the conditioning process alters the original DEM and may render it incorrect for applications other than deriving drainage directions. Endorheic basins (inland sinks) are \'seeded\' with a no-data cell at their lowest point in order to terminate the flow. Full details of the underlying digital elevation model are available in the HydroSHEDS website and documentation.\\nThis dataset is at 3 arc-second resolution. The datasets available at 3 arc-seconds are the Void-Filled DEM, Hydrologically Conditioned DEM, and Drainage (Flow) Direction.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03CONDEM"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03CONDEM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_03CONDEM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03CONDEM#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/03DIR","title":"WWF HydroSHEDS Drainage Direction, 3 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis drainage direction dataset defines the direction of flow from each cell in the conditioned DEM to its steepest down-slope neighbor. Values of drainage direction vary from 1 to 128. All final outlet cells to the ocean are flagged with a value of 0. All cells that mark the lowest point of an endorheic basin (inland sink) are flagged with a value of -1. The drainage direction values follow the convention adopted by ESRI\'s flow direction implementation: 1=E, 2=SE, 4=S, 8=SW, 16=W, 32=NW, 64=N, 128=NE. \\n\\n This dataset is at 3 arc-second resolution. The datasets available at 3 arc-seconds are the Void-Filled DEM, Hydrologically Conditioned DEM, and Drainage (Flow) Direction.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03DIR"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03DIR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_03DIR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03DIR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/03VFDEM","title":"WWF HydroSHEDS Void-Filled DEM, 3 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis void-filled elevation dataset is the first step towards producing the conditioned DEM datasets. Spikes and wells in the SRTM data were detected and voided out. Small voids were filled by interpolation of surrounding elevations. The ocean was set to an elevation of 0 meters, and lakes, islands, and rivers were filled using other techniques. Full details of the underlying digital elevation model are available in the HydroSHEDS website and documentation. \\n\\n This dataset is at 3 arc-second resolution. The datasets available at 3 arc-seconds are the Void-Filled DEM, Hydrologically Conditioned DEM, and Drainage (Flow) Direction.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03VFDEM"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03VFDEM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_03VFDEM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03VFDEM#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/15ACC","title":"WWF HydroSHEDS Flow Accumulation, 15 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis flow accumulation dataset defines the amount of upstream area (in number of cells) draining into each cell. The drainage direction layer is used to define which cells flow into the target cell. The number of accumulated cells is essentially a measure of the upstream catchment area. However, since the cell size of the HydroSHEDS data set depends on latitude, the cell accumulation value cannot directly be translated into drainage areas in square kilometers. Values range from 1 at topographic highs (river sources) to very large numbers (on the order of millions of cells) at the mouths of large rivers. \\n\\n This dataset is at 15 arc-second resolution. The datasets available at 15 arc-seconds are the Hydrologically Conditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15ACC"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15ACC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_15ACC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15ACC#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/15CONDEM","title":"WWF HydroSHEDS Hydrologically Conditioned DEM, 15 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis hydrologically conditioned elevation dataset is the result of an iterative conditioning and correction process. Note that the conditioning process alters the original DEM and may render it incorrect for applications other than deriving drainage directions. Endorheic basins (inland sinks) are \'seeded\' with a no-data cell at their lowest point in order to terminate the flow. Full details of the underlying digital elevation model are available in the HydroSHEDS website and documentation.\\n\\nThis dataset is at 15 arc-second resolution. The datasets available at 15 arc-seconds are the Hydrologically Conditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15CONDEM"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15CONDEM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_15CONDEM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15CONDEM#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/15DIR","title":"WWF HydroSHEDS Drainage Direction, 15 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis drainage direction dataset defines the direction of flow from each cell in the conditioned DEM to its steepest down-slope neighbor. Values of drainage direction vary from 1 to 128. All final outlet cells to the ocean are flagged with a value of 0. All cells that mark the lowest point of an endorheic basin (inland sink) are flagged with a value of -1. The drainage direction values follow the convention adopted by ESRI\'s flow direction implementation: 1=E, 2=SE, 4=S, 8=SW, 16=W, 32=NW, 64=N, 128=NE. \\n\\n This dataset is at 15 arc-second resolution. The datasets available at 15 arc-seconds are the Hydrologically Conditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15DIR"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15DIR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_15DIR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15DIR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/30ACC","title":"WWF HydroSHEDS Flow Accumulation, 30 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis flow accumulation dataset defines the amount of upstream area (in number of cells) draining into each cell. The drainage direction layer is used to define which cells flow into the target cell. The number of accumulated cells is essentially a measure of the upstream catchment area. However, since the cell size of the HydroSHEDS data set depends on latitude, the cell accumulation value cannot directly be translated into drainage areas in square kilometers. Values range from 1 at topographic highs (river sources) to very large numbers (on the order of millions of cells) at the mouths of large rivers. \\n\\n This dataset is at 30 arc-second resolution. The datasets available at 30 arc-seconds are the Hydrologically Conditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30ACC"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30ACC"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_30ACC_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30ACC#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/30CONDEM","title":"WWF HydroSHEDS Hydrologically Conditioned DEM, 30 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis hydrologically conditioned elevation dataset is the result of an iterative conditioning and correction process. Note that the conditioning process alters the original DEM and may render it incorrect for applications other than deriving drainage directions. Endorheic basins (inland sinks) are \'seeded\' with a no-data cell at their lowest point in order to terminate the flow. Full details of the underlying digital elevation model are available in the HydroSHEDS website and documentation.\\n\\nThis dataset is at 30 arc-second resolution. The datasets available at 30 arc-seconds are the Hydrologically Conditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30CONDEM"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30CONDEM"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_30CONDEM_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30CONDEM#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WWF/HydroSHEDS/30DIR","title":"WWF HydroSHEDS Drainage Direction, 30 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\\ninformation for regional and global-scale applications in a consistent\\nformat. It offers a suite of geo-referenced datasets (vector and\\nraster) at various scales, including river networks, watershed\\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\\nis based on elevation data obtained in 2000 by NASA\'s Shuttle Radar\\nTopography Mission (SRTM).\\n\\n\\nThis drainage direction dataset defines the direction of flow from each cell in the conditioned DEM to its steepest down-slope neighbor. Values of drainage direction vary from 1 to 128. All final outlet cells to the ocean are flagged with a value of 0. All cells that mark the lowest point of an endorheic basin (inland sink) are flagged with a value of -1. The drainage direction values follow the convention adopted by ESRI\'s flow direction implementation: 1=E, 2=SE, 4=S, 8=SW, 16=W, 32=NW, 64=N, 128=NE. \\n\\n This dataset is at 30 arc-second resolution. The datasets available at 30 arc-seconds are the Hydrologically Conditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.","license":"proprietary","providers":[{"name":"WWF","roles":["producer","licensor"],"url":"http://www.hydrosheds.org/"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30DIR"}],"extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30DIR"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WWF_HydroSHEDS_30DIR_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30DIR#terms-of-use"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"WorldPop/GP/100m/pop","title":"WorldPop Global Project Population Data: Estimated Residential Population per 100x100m Grid Square","description":"Global high-resolution, contemporary data on human population distributions are\\na prerequisite for the accurate measurement of the impacts of population\\ngrowth, for monitoring changes, and for planning interventions. The WorldPop\\nproject aims to meet these needs through the provision of detailed and open\\naccess population distribution datasets built using transparent and\\npeer-reviewed approaches.\\n\\nFull details on the methods and datasets used in constructing the\\ndata, along with open access publications, are provided on the\\n[WorldPop website](http://www.worldpop.org.uk). In brief, recent census-based\\npopulation counts matched to their associated administrative units are\\ndisaggregated to ≈100x100m grid cells through machine learning approaches\\nthat exploit the relationships between population densities and a range of\\ngeospatial covariate layers. The datasets depict estimated number of people\\nresiding in each grid cell in 2010, 2015, and other years. The mapping approach\\nis Random Forest-based dasymetric redistribution.\\n\\nFurther WorldPop gridded datasets on population age structures,\\npoverty, urban growth, and population dynamics are freely available on\\nthe WorldPop website. WorldPop represents a collaboration between\\nresearchers at the University of Southampton, Universite Libre de\\nBruxelles, and University of Louisville. The project is principally funded by\\nthe Bill and Melinda Gates Foundation.\\n","license":"proprietary","providers":[{"name":"WorldPop","roles":["producer","licensor"],"url":"http://www.worldpop.org"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop"}],"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/WorldPop_GP_100m_pop_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop#terms-of-use"},{"rel":"cite-as","href":"https://doi.org/10.1038/sdata.2015.45"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"YALE/YCEO/UHI/Summer_UHI_yearly_pixel/v4","title":"YCEO Surface Urban Heat Islands: Pixel-level composites of yearly summertime daytime and nighttime intensity","description":"\'This dataset contains annual, summertime, and wintertime surface urban\\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\\nAQUA land surface temperature (LST) products, the Landscan urban extent\\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\\nusing the Simplified Urban-Extent Algorithm. The product is available both at\\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\\nmeans from 2003 to 2018. The monthly composites are only available as urban\\ncluster means.\\n\\nA summary of older versions,\\nincluding changes from the dataset created and analyzed in the originally\\npublished manuscript can be found on the\\n[Yale Center for Earth Observation website](https://yceo.yale.edu/research/global-surface-uhi-explorer).\\nThe dataset can also be explored using the [Global Surface UHI\\nExplorer web application](https://yceo.users.earthengine.app/view/uhimap).\\n\\nThe dataset is split into the following six components:\\n\\n1. **UHI_all_averaged:** Image containing cluster-mean\\ncomposite daytime and nighttime SUHI intensity for annual, summer,\\nand winter.\\n\\n2. **UHI_monthly_averaged:** Image containing cluster-mean\\nmonthly composites of daytime and nighttime SUHI intensity.\\n\\n3. **UHI_yearly_averaged:** Image collection of cluster-mean\\nyearly composites of daytime and nighttime SUHI intensity from 2003.\\nto 2018.\\n\\n4. **UHI_yearly_pixel:** Image collection of spatially\\ndisaggregated (nominal scale of 300 m) annual daytime and nighttime\\nSUHI intensity from 2003 to 2018.\\n\\n5. **Summer_UHI_yearly_pixel:** Image collection of spatially\\ndisaggregated (nominal scale of 300 m) summertime daytime and\\nnighttime SUHI intensity from 2003 to 2018.\\n\\n6. **Winter_UHI_yearly_pixel:** Image collection of spatially\\ndisaggregated (nominal scale of 300 m) wintertime daytime and\\nnighttime SUHI intensity from 2003 to 2018.\\n\'\\n\\n\\nThis asset is the fifth component.","license":"proprietary","providers":[{"name":"Yale Center for Earth Observation (YCEO)","roles":["producer","licensor"],"url":"https://yceo.yale.edu/research/global-surface-uhi-explorer"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_Summer_UHI_yearly_pixel_v4"}],"extent":{"spatial":{"bbox":[[-180,-49.98,180,69.7]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/Summer_UHI_yearly_pixel/v4"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/YALE_YCEO_UHI_pixel_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_Summer_UHI_yearly_pixel_v4#terms-of-use"},{"rel":"license","href":"https://yceo.yale.edu/research/global-surface-uhi-explorer"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"YALE/YCEO/UHI/UHI_all_averaged/v4","title":"YCEO Surface Urban Heat Islands: Spatially-averaged daytime and nighttime intensity for annual, summer, and winter","description":"\'This dataset contains annual, summertime, and wintertime surface urban\\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\\nAQUA land surface temperature (LST) products, the Landscan urban extent\\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\\nusing the Simplified Urban-Extent Algorithm. The product is available both at\\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\\nmeans from 2003 to 2018. The monthly composites are only available as urban\\ncluster means.\\n\\nA summary of older versions,\\nincluding changes from the dataset created and analyzed in the originally\\npublished manuscript can be found on the\\n[Yale Center for Earth Observation website](https://yceo.yale.edu/research/global-surface-uhi-explorer).\\nThe dataset can also be explored using the [Global Surface UHI\\nExplorer web application](https://yceo.users.earthengine.app/view/uhimap).\\n\\nThe dataset is split into the following six components:\\n\\n1. **UHI_all_averaged:** Image containing cluster-mean\\ncomposite daytime and nighttime SUHI intensity for annual, summer,\\nand winter.\\n\\n2. **UHI_monthly_averaged:** Image containing cluster-mean\\nmonthly composites of daytime and nighttime SUHI intensity.\\n\\n3. **UHI_yearly_averaged:** Image collection of cluster-mean\\nyearly composites of daytime and nighttime SUHI intensity from 2003.\\nto 2018.\\n\\n4. **UHI_yearly_pixel:** Image collection of spatially\\ndisaggregated (nominal scale of 300 m) annual daytime and nighttime\\nSUHI intensity from 2003 to 2018.\\n\\n5. **Summer_UHI_yearly_pixel:** Image collection of spatially\\ndisaggregated (nominal scale of 300 m) summertime daytime and\\nnighttime SUHI intensity from 2003 to 2018.\\n\\n6. **Winter_UHI_yearly_pixel:** Image collection of spatially\\ndisaggregated (nominal scale of 300 m) wintertime daytime and\\nnighttime SUHI intensity from 2003 to 2018.\\n\'\\n\\n\\nThis asset is the first component.","license":"proprietary","providers":[{"name":"Yale Center for Earth Observation (YCEO)","roles":["producer","licensor"],"url":"https://yceo.yale.edu/research/global-surface-uhi-explorer"},{"name":"Google Earth Engine","roles":["host"],"url":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_UHI_all_averaged_v4"}],"extent":{"spatial":{"bbox":[[-180,-49.98,180,69.7]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_all_averaged/v4"},{"rel":"parent","href":"https://earthengine.openeo.org/v1.0/collections"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/"},{"rel":"preview","href":"https://mw1.google.com/ges/dd/images/YALE_YCEO_UHI_averaged_sample.png"},{"rel":"license","href":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_UHI_all_averaged_v4#terms-of-use"},{"rel":"license","href":"https://yceo.yale.edu/research/global-surface-uhi-explorer"}]},{"stac_version":"1.0.0-beta.2","stac_extensions":[],"id":"YALE/YCEO/UHI/UHI_monthly_averaged/v4","title":"YCEO Surface Urban Heat Islands: Spatially-averaged monthly composites of daytime and nighttime intensity","description":"\'This dataset contains annual, summertime, and wintertime surface urban\\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\\nAQUA land surface temperature (LST) products, the Landscan urban extent\\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\\nusing the Simplified Urban-Extent Algorithm. The product is available both at\\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\\nmeans from 2003 to 2018. 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For example, the fourth dekad is Feb, 1 - Feb, 10 each year.\\n* `month`: Month of the year\\n* `season`: Three month periods of the calendar seasons (December - February, March - May, June - August, September - November).\\n* `tropical-season`: Six month periods of the tropical seasons (November - April, May - October).\\n* `year`: Proleptic years\\n* `decade`: Ten year periods ([0-to-9 decade](https://en.wikipedia.org/wiki/Decade#0-to-9_decade)), from a year ending in a 0 to the next year ending in a 9.\\n* `decade-ad`: Ten year periods ([1-to-0 decade](https://en.wikipedia.org/wiki/Decade#1-to-0_decade)) better aligned with the anno Domini (AD) calendar era, from a year ending in a 1 to the next year ending in a 0.","schema":{"type":"string","enum":["hour","day","week","dekad","month","season","tropical-season","year","decade","decade-ad"]}},{"name":"reducer","description":"A reducer to be applied for the values contained in each period. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category \'reducer\' for such processes. Periods may not contain any values, which for most reducers leads to no-data (`null`) values by default.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"data","description":"A labeled array with elements of any type. If there\'s no data for the period, the array is empty.","schema":{"type":"array","subtype":"labeled-array","items":{"description":"Any data type."}}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the new data cube.","schema":{"description":"Any data type."}}}},{"name":"dimension","description":"The name of the temporal dimension for aggregation. All data along the dimension is passed through the specified reducer. If the dimension is not set or set to `null`, the data cube is expected to only have one temporal dimension. Fails with a `TooManyDimensions` exception if it has more dimensions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.","schema":{"type":["string","null"]},"optional":true,"default":null},{"name":"context","description":"Additional data to be passed to the reducer.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A new data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the given temporal dimension. The specified temporal dimension has the following dimension labels (`YYYY` = four-digit year, `MM` = two-digit month, `DD` two-digit day of month):\\n\\n* `hour`: `YYYY-MM-DD-00` - `YYYY-MM-DD-23`\\n* `day`: `YYYY-001` - `YYYY-365`\\n* `week`: `YYYY-01` - `YYYY-52`\\n* `dekad`: `YYYY-00` - `YYYY-36`\\n* `month`: `YYYY-01` - `YYYY-12`\\n* `season`: `YYYY-djf` (December - February), `YYYY-mam` (March - May), `YYYY-jja` (June - August), `YYYY-son` (September - November).\\n* `tropical-season`: `YYYY-ndjfma` (November - April), `YYYY-mjjaso` (May - October).\\n* `year`: `YYYY`\\n* `decade`: `YYY0`\\n* `decade-ad`: `YYY1`","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"TooManyDimensions":{"message":"The data cube contains multiple temporal dimensions. The parameter `dimension` must be specified."},"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."},"DistinctDimensionLabelsRequired":{"message":"The dimension labels have duplicate values. Distinct labels must be specified."}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#aggregate","rel":"about","title":"Aggregation explained in the openEO documentation"}]},{"id":"aggregate_temporal","summary":"Temporal aggregations","description":"Computes a temporal aggregation based on an array of temporal intervals.\\n\\nFor common regular calendar hierarchies such as year, month, week or seasons ``aggregate_temporal_period()`` can be used. Other calendar hierarchies must be transformed into specific intervals by the clients.\\n\\nFor each interval, all data along the dimension will be passed through the reducer.\\n\\nThe computed values will be projected to the labels. If no labels are specified, the start of the temporal interval will be used as label for the corresponding values. In case of a conflict (i.e. the user-specified values for the start times of the temporal intervals are not distinct), the user-defined labels must be specified in the parameter `labels` as otherwise a `DistinctDimensionLabelsRequired` exception would be thrown. The number of user-defined labels and the number of intervals need to be equal.\\n\\nIf the dimension is not set or is set to `null`, the data cube is expected to only have one temporal dimension.","categories":["cubes","aggregate & resample"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"intervals","description":"Left-closed temporal intervals, which are allowed to overlap. Each temporal interval in the array has exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified instance in time is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified instance in time is **excluded** from the interval.\\n\\nThe specified temporal strings follow [RFC 3339](https://www.rfc-editor.org/rfc/rfc3339.html). Although [RFC 3339 prohibits the hour to be \'24\'](https://www.rfc-editor.org/rfc/rfc3339.html#section-5.7), **this process allows the value \'24\' for the hour** of an end time in order to make it possible that left-closed time intervals can fully cover the day.","schema":{"type":"array","subtype":"temporal-intervals","minItems":1,"items":{"type":"array","subtype":"temporal-interval","minItems":2,"maxItems":2,"items":{"anyOf":[{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"},{"type":"string","format":"time","subtype":"time"},{"type":"string","subtype":"year","minLength":4,"maxLength":4,"pattern":"^\\\\d{4}$"},{"type":"null"}]}},"examples":[[["2015-01-01","2016-01-01"],["2016-01-01","2017-01-01"],["2017-01-01","2018-01-01"]],[["00:00:00Z","12:00:00Z"],["12:00:00Z","24:00:00Z"]]]}},{"name":"reducer","description":"A reducer to be applied for the values contained in each interval. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category \'reducer\' for such processes. Intervals may not contain any values, which for most reducers leads to no-data (`null`) values by default.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"data","description":"A labeled array with elements of any type. If there\'s no data for the interval, the array is empty.","schema":{"type":"array","subtype":"labeled-array","items":{"description":"Any data type."}}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the new data cube.","schema":{"description":"Any data type."}}}},{"name":"labels","description":"Distinct labels for the intervals, which can contain dates and/or times. Is only required to be specified if the values for the start of the temporal intervals are not distinct and thus the default labels would not be unique. The number of labels and the number of groups need to be equal.","schema":{"type":"array","items":{"type":["number","string"]}},"default":[],"optional":true},{"name":"dimension","description":"The name of the temporal dimension for aggregation. All data along the dimension is passed through the specified reducer. If the dimension is not set or set to `null`, the data cube is expected to only have one temporal dimension. Fails with a `TooManyDimensions` exception if it has more dimensions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.","schema":{"type":["string","null"]},"default":null,"optional":true},{"name":"context","description":"Additional data to be passed to the reducer.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A new data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the given temporal dimension.","schema":{"type":"object","subtype":"raster-cube"}},"examples":[{"arguments":{"data":{"from_parameter":"data"},"intervals":[["2015-01-01","2016-01-01"],["2016-01-01","2017-01-01"],["2017-01-01","2018-01-01"],["2018-01-01","2019-01-01"],["2019-01-01","2020-01-01"]],"labels":["2015","2016","2017","2018","2019"],"reducer":{"process_graph":{"mean1":{"process_id":"mean","arguments":{"data":{"from_parameter":"data"}},"result":true}}}}}],"exceptions":{"TooManyDimensions":{"message":"The data cube contains multiple temporal dimensions. The parameter `dimension` must be specified."},"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."},"DistinctDimensionLabelsRequired":{"message":"The dimension labels have duplicate values. Distinct labels must be specified."}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#aggregate","rel":"about","title":"Aggregation explained in the openEO documentation"}]},{"id":"all","summary":"Are all of the values true?","description":"Checks if **all** of the values in `data` are true. If no value is given (i.e. the array is empty) the process returns `null`.\\n\\nBy default all no-data values are ignored so that the process returns `null` if all values are no-data, `true` if all values are true and `false` otherwise. Setting the `ignore_nodata` flag to `false` takes no-data values into account and the array values are reduced pairwise according to the following truth table:\\n\\n```\\n || null | false | true\\n----- || ----- | ----- | -----\\nnull || null | false | null\\nfalse || false | false | false\\ntrue || null | false | true\\n```\\n\\n**Remark:** The process evaluates all values from the first to the last element and stops once the outcome is unambiguous. A result is ambiguous unless a value is `false` or all values have been taken into account.","categories":["logic","reducer"],"parameters":[{"name":"data","description":"A set of boolean values.","schema":{"type":"array","items":{"type":["boolean","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not and ignores them by default.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"Boolean result of the logical operation.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"data":[false,null]},"returns":false},{"arguments":{"data":[true,null]},"returns":true},{"arguments":{"data":[false,null],"ignore_nodata":false},"returns":false},{"arguments":{"data":[true,null],"ignore_nodata":false},"returns":null},{"arguments":{"data":[true,false,true,false]},"returns":false},{"arguments":{"data":[true,false]},"returns":false},{"arguments":{"data":[true,true]},"returns":true},{"arguments":{"data":[true]},"returns":true},{"arguments":{"data":[null],"ignore_nodata":false},"returns":null},{"arguments":{"data":[]},"returns":null}]},{"id":"and","summary":"Logical AND","description":"Checks if **both** values are true.\\n\\nEvaluates parameter `x` before `y` and stops once the outcome is unambiguous. If any argument is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ----- | ----- | -----\\nnull || null | false | null\\nfalse || false | false | false\\ntrue || null | false | true\\n```","categories":["logic"],"parameters":[{"name":"x","description":"A boolean value.","schema":{"type":["boolean","null"]}},{"name":"y","description":"A boolean value.","schema":{"type":["boolean","null"]}}],"returns":{"description":"Boolean result of the logical AND.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":true,"y":true},"returns":true},{"arguments":{"x":true,"y":false},"returns":false},{"arguments":{"x":false,"y":false},"returns":false},{"arguments":{"x":false,"y":null},"returns":false},{"arguments":{"x":true,"y":null},"returns":null}],"process_graph":{"all":{"process_id":"all","arguments":{"data":[{"from_parameter":"x"},{"from_parameter":"y"}],"ignore_nodata":false},"result":true}}},{"id":"anomaly","summary":"Compute anomalies","description":"Computes anomalies based on normals for temporal periods. It compares the data for each label in the temporal dimension with the corresponding data in the normals data cube by subtracting the normal from the data.","categories":["climatology","math"],"parameters":[{"name":"data","description":"A data cube with exactly one temporal dimension and the following dimension labels for the given period (`YYYY` = four-digit year, `MM` = two-digit month, `DD` two-digit day of month):\\n\\n* `hour`: `YYYY-MM-DD-00` - `YYYY-MM-DD-23`\\n* `day`: `YYYY-001` - `YYYY-365`\\n* `week`: `YYYY-01` - `YYYY-52`\\n* `dekad`: `YYYY-00` - `YYYY-36`\\n* `month`: `YYYY-01` - `YYYY-12`\\n* `season`: `YYYY-djf` (December - February), `YYYY-mam` (March - May), `YYYY-jja` (June - August), `YYYY-son` (September - November).\\n* `tropical-season`: `YYYY-ndjfma` (November - April), `YYYY-mjjaso` (May - October).\\n* `year`: `YYYY`\\n* `decade`: `YYY0`\\n* `decade-ad`: `YYY1`\\n* `single-period` / `climatology-period`: Any\\n\\n``aggregate_temporal_period()`` can compute such a data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"normals","description":"A data cube with normals, e.g. daily, monthly or yearly values computed from a process such as ``climatological_normal()``. Must contain exactly one temporal dimension with the following dimension labels for the given period:\\n\\n* `hour`: `00` - `23`\\n* `day`: `001` - `365`\\n* `week`: `01` - `52`\\n* `dekad`: `00` - `36`\\n* `month`: `01` - `12`\\n* `season`: `djf` (December - February), `mam` (March - May), `jja` (June - August), `son` (September - November)\\n* `tropical-season`: `ndjfma` (November - April), `mjjaso` (May - October)\\n* `year`: Four-digit year numbers\\n* `decade`: Four-digit year numbers, the last digit being a `0`\\n* `decade-ad`: Four-digit year numbers, the last digit being a `1`\\n* `single-period` / `climatology-period`: A single dimension label with any name is expected.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"period","description":"Specifies the time intervals available in the normals data cube. The following options are available:\\n\\n* `hour`: Hour of the day\\n* `day`: Day of the year\\n* `week`: Week of the year\\n* `dekad`: Ten day periods, counted per year with three periods per month (day 1 - 10, 11 - 20 and 21 - end of month). The third dekad of the month can range from 8 to 11 days. For example, the fourth dekad is Feb, 1 - Feb, 10 each year.\\n* `month`: Month of the year\\n* `season`: Three month periods of the calendar seasons (December - February, March - May, June - August, September - November).\\n* `tropical-season`: Six month periods of the tropical seasons (November - April, May - October).\\n* `year`: Proleptic years\\n* `decade`: Ten year periods ([0-to-9 decade](https://en.wikipedia.org/wiki/Decade#0-to-9_decade)), from a year ending in a 0 to the next year ending in a 9.\\n* `decade-ad`: Ten year periods ([1-to-0 decade](https://en.wikipedia.org/wiki/Decade#1-to-0_decade)) better aligned with the anno Domini (AD) calendar era, from a year ending in a 1 to the next year ending in a 0.\\n* `single-period` / `climatology-period`: A single period of arbitrary length","schema":{"type":"string","enum":["hour","day","week","dekad","month","season","tropical-season","year","decade","decade-ad","climatology-period","single-period"]}}],"returns":{"description":"A data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}}},{"id":"any","summary":"Is at least one value true?","description":"Checks if **any** (i.e. at least one) value in `data` is `true`. If no value is given (i.e. the array is empty) the process returns `null`.\\n\\nBy default all no-data values are ignored so that the process returns `null` if all values are no-data, `true` if at least one value is true and `false` otherwise. Setting the `ignore_nodata` flag to `false` takes no-data values into account and the array values are reduced pairwise according to the following truth table:\\n\\n```\\n || null | false | true\\n----- || ---- | ----- | ----\\nnull || null | null | true\\nfalse || null | false | true\\ntrue || true | true | true\\n```\\n\\n**Remark:** The process evaluates all values from the first to the last element and stops once the outcome is unambiguous. A result is ambiguous unless a value is `true`.","categories":["logic","reducer"],"parameters":[{"name":"data","description":"A set of boolean values.","schema":{"type":"array","items":{"type":["boolean","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not and ignores them by default.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"Boolean result of the logical operation.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"data":[false,null]},"returns":false},{"arguments":{"data":[true,null]},"returns":true},{"arguments":{"data":[false,null],"ignore_nodata":false},"returns":null},{"arguments":{"data":[true,null],"ignore_nodata":false},"returns":true},{"arguments":{"data":[true,false,true,false]},"returns":true},{"arguments":{"data":[true,false]},"returns":true},{"arguments":{"data":[false,false]},"returns":false},{"arguments":{"data":[true]},"returns":true},{"arguments":{"data":[null],"ignore_nodata":false},"returns":null},{"arguments":{"data":[]},"returns":null}]},{"id":"apply_dimension","summary":"Apply a process to pixels along a dimension","description":"Applies a process to all pixel values along a dimension of a raster data cube. For example, if the temporal dimension is specified the process will work on a time series of pixel values.\\n\\nThe process ``reduce_dimension()`` also applies a process to pixel values along a dimension, but drops the dimension afterwards. The process ``apply()`` applies a process to each pixel value in the data cube.\\n\\nThe target dimension is the source dimension if not specified otherwise in the `target_dimension` parameter. The pixel values in the target dimension get replaced by the computed pixel values. The name, type and reference system are preserved.\\n\\nThe dimension labels are preserved when the target dimension is the source dimension and the number of pixel values in the source dimension is equal to the number of values computed by the process. Otherwise, the dimension labels will be incrementing integers starting from zero, which can be changed using ``rename_labels()`` afterwards. The number of labels will equal to the number of values computed by the process.","categories":["cubes"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"process","description":"Process to be applied on all pixel values. The specified process needs to accept an array and must return an array with at least one element. A process may consist of multiple sub-processes.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"data","description":"A labeled array with elements of any type.","schema":{"type":"array","subtype":"labeled-array","items":{"description":"Any data type."}}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the new data cube.","schema":{"type":"array","items":{"description":"Any data type."}}}}},{"name":"dimension","description":"The name of the source dimension to apply the process on. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.","schema":{"type":"string"}},{"name":"target_dimension","description":"The name of the target dimension or `null` (the default) to use the source dimension specified in the parameter `dimension`.\\n\\nBy specifying a target dimension, the source dimension is removed. The target dimension with the specified name and the type `other` (see ``add_dimension()``) is created, if it doesn\'t exist yet.","schema":{"type":["string","null"]},"default":null,"optional":true},{"name":"context","description":"Additional data to be passed to the process.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A data cube with the newly computed values.\\n\\nAll dimensions stay the same, except for the dimensions specified in corresponding parameters. There are three cases how the dimensions can change:\\n\\n1. The source dimension is the target dimension:\\n - The (number of) dimensions remain unchanged as the source dimension is the target dimension.\\n - The source dimension properties name and type remain unchanged.\\n - The dimension labels, the reference system and the resolution are preserved only if the number of pixel values in the source dimension is equal to the number of values computed by the process. Otherwise, all other dimension properties change as defined in the list below.\\n2. The source dimension is not the target dimension and the latter exists:\\n - The number of dimensions decreases by one as the source dimension is dropped.\\n - The target dimension properties name and type remain unchanged. All other dimension properties change as defined in the list below.\\n3. The source dimension is not the target dimension and the latter does not exist:\\n - The number of dimensions remain unchanged, but the source dimension is replaced with the target dimension.\\n - The target dimension has the specified name and the type other. All other dimension properties are set as defined in the list below.\\n\\nUnless otherwise stated above, for the given (target) dimension the following applies:\\n\\n- the number of dimension labels is equal to the number of values computed by the process,\\n- the dimension labels are incrementing integers starting from zero,\\n- the resolution changes, and\\n- the reference system is undefined.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#apply","rel":"about","title":"Apply explained in the openEO documentation"}]},{"id":"apply_kernel","summary":"Apply a spatial convolution with a kernel","description":"Applies a 2D convolution (i.e. a focal operation with a weighted kernel) on the horizontal spatial dimensions (axes `x` and `y`) of the data cube.\\n\\nEach value in the kernel is multiplied with the corresponding pixel value and all products are summed up afterwards. The sum is then multiplied with the factor.\\n\\nThe process can\'t handle non-numerical or infinite numerical values in the data cube. Boolean values are converted to integers (`false` = 0, `true` = 1), but all other non-numerical or infinite values are replaced with zeroes by default (see parameter `replace_invalid`).\\n\\nFor cases requiring more generic focal operations or non-numerical values, see ``apply_neighborhood()``.","categories":["cubes","math > image filter"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"kernel","description":"Kernel as a two-dimensional array of weights. The inner level of the nested array aligns with the `x` axis and the outer level aligns with the `y` axis. Each level of the kernel must have an uneven number of elements, otherwise the process throws a `KernelDimensionsUneven` exception.","schema":{"description":"A two-dimensional array of numbers.","type":"array","subtype":"kernel","items":{"type":"array","items":{"type":"number"}}}},{"name":"factor","description":"A factor that is multiplied to each value after the kernel has been applied.\\n\\nThis is basically a shortcut for explicitly multiplying each value by a factor afterwards, which is often required for some kernel-based algorithms such as the Gaussian blur.","schema":{"type":"number"},"default":1,"optional":true},{"name":"border","description":"Determines how the data is extended when the kernel overlaps with the borders. Defaults to fill the border with zeroes.\\n\\nThe following options are available:\\n\\n* *numeric value* - fill with a user-defined constant number `n`: `nnnnnn|abcdefgh|nnnnnn` (default, with `n` = 0)\\n* `replicate` - repeat the value from the pixel at the border: `aaaaaa|abcdefgh|hhhhhh`\\n* `reflect` - mirror/reflect from the border: `fedcba|abcdefgh|hgfedc`\\n* `reflect_pixel` - mirror/reflect from the center of the pixel at the border: `gfedcb|abcdefgh|gfedcb`\\n* `wrap` - repeat/wrap the image: `cdefgh|abcdefgh|abcdef`","schema":[{"type":"string","enum":["replicate","reflect","reflect_pixel","wrap"]},{"type":"number"}],"default":0,"optional":true},{"name":"replace_invalid","description":"This parameter specifies the value to replace non-numerical or infinite numerical values with. By default, those values are replaced with zeroes.","schema":{"type":"number"},"default":0,"optional":true}],"returns":{"description":"A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"KernelDimensionsUneven":{"message":"Each dimension of the kernel must have an uneven number of elements."}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#apply","rel":"about","title":"Apply explained in the openEO documentation"},{"rel":"about","href":"http://www.songho.ca/dsp/convolution/convolution.html","title":"Convolutions explained"},{"rel":"about","href":"http://www.songho.ca/dsp/convolution/convolution2d_example.html","title":"Example of 2D Convolution"}]},{"id":"apply_neighborhood","summary":"Apply a process to pixels in a n-dimensional neighborhood","description":"Applies a focal process to a data cube.\\n\\nA focal process is a process that works on a \'neighborhood\' of pixels. The neighborhood can extend into multiple dimensions, this extent is specified by the `size` argument. It is not only (part of) the size of the input window, but also the size of the output for a given position of the sliding window. The sliding window moves with multiples of `size`.\\n\\nAn overlap can be specified so that neighborhoods can have overlapping boundaries. This allows for continuity of the output. The values included in the data cube as overlap can\'t be modified by the given `process`. The missing overlap at the borders of the original data cube are made available as no-data (`null`) in the sub data cubes.\\n\\nThe neighborhood size should be kept small enough, to avoid running beyond computational resources, but a too small size will result in a larger number of process invocations, which may slow down processing. Window sizes for spatial dimensions typically are in the range of 64 to 512 pixels, while overlaps of 8 to 32 pixels are common.\\n\\nThe process must not add new dimensions, or remove entire dimensions, but the result can have different dimension labels.\\n\\nFor the special case of 2D convolution, it is recommended to use ``apply_kernel()``.","categories":["cubes"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"process","description":"Process to be applied on all neighborhoods.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"data","description":"A subset of the data cube as specified in `context` and `overlap`.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) must remain unchanged, otherwise a `DataCubePropertiesImmutable` exception will be thrown.","schema":{"type":"object","subtype":"raster-cube"}}}},{"name":"size","description":"Neighborhood sizes along each dimension.\\n\\nThis object maps dimension names to either a physical measure (e.g. 100 m, 10 days) or pixels (e.g. 32 pixels). For dimensions not specified, the default is to provide all values. Be aware that including all values from overly large dimensions may not be processed at once.","schema":{"type":"array","items":{"type":"object","subtype":"chunk-size","required":["dimension","value"],"properties":{"dimension":{"type":"string"},"value":{"default":null,"anyOf":[{"type":"null","title":"All values"},{"type":"number","minimum":0,"description":"See the `unit` parameter for more information."},{"type":"string","subtype":"duration","description":"[ISO 8601 duration](https://en.wikipedia.org/wiki/ISO_8601#Durations), e.g. `P1D` for one day.","pattern":"^(-?)P(?=\\\\d|T\\\\d)(?:(\\\\d+)Y)?(?:(\\\\d+)M)?(?:(\\\\d+)([DW]))?(?:T(?:(\\\\d+)H)?(?:(\\\\d+)M)?(?:(\\\\d+(?:\\\\.\\\\d+)?)S)?)?$"}]},"unit":{"type":"string","description":"The unit the values are given in, either in meters (`m`) or pixels (`px`). If no unit is given, uses the unit specified for the dimension or otherwise the default unit of the reference system.","enum":["px","m"]}}}}},{"name":"overlap","description":"Overlap of neighborhoods along each dimension to avoid border effects.\\n\\nFor instance a temporal dimension can add 1 month before and after a neighborhood. In the spatial dimensions, this is often a number of pixels. The overlap specified is added before and after, so an overlap of 8 pixels will add 8 pixels on both sides of the window, so 16 in total.\\n\\nBe aware that large overlaps increase the need for computational resources and modifying overlapping data in subsequent operations have no effect.","optional":true,"schema":{"type":"array","items":{"type":"object","subtype":"chunk-size","required":["dimension","value"],"properties":{"dimension":{"type":"string"},"value":{"default":null,"anyOf":[{"type":"null","title":"No values"},{"type":"number","minimum":0,"description":"See the `unit` parameter for more information."},{"type":"string","subtype":"duration","description":"[ISO 8601 duration](https://en.wikipedia.org/wiki/ISO_8601#Durations), e.g. `P1D` for one day.","pattern":"^(-?)P(?=\\\\d|T\\\\d)(?:(\\\\d+)Y)?(?:(\\\\d+)M)?(?:(\\\\d+)([DW]))?(?:T(?:(\\\\d+)H)?(?:(\\\\d+)M)?(?:(\\\\d+(?:\\\\.\\\\d+)?)S)?)?$"}]},"unit":{"type":"string","description":"The unit the values are given in, either in meters (`m`) or pixels (`px`). If no unit is given, uses the unit specified for the dimension or otherwise the default unit of the reference system.","enum":["px","m"]}}}}},{"name":"context","description":"Additional data to be passed to the process.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"examples":[{"arguments":{"data":{"from_parameter":"data"},"process":{"process_graph":{"udf":{"process_id":"run_udf","arguments":{"data":{"from_parameter":"data"},"udf":"ml.py","runtime":"Python"},"result":true}}},"size":[{"dimension":"x","value":128,"unit":"px"},{"dimension":"y","value":128,"unit":"px"},{"dimension":"t","value":"P5D"}],"overlap":[{"dimension":"x","value":16,"unit":"px"},{"dimension":"y","value":16,"unit":"px"},{"dimension":"t","value":"P3D"}]}}],"exceptions":{"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."},"DataCubePropertiesImmutable":{"message":"The dimension properties (name, type, labels, reference system and resolution) must remain unchanged."}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#apply","rel":"about","title":"Apply explained in the openEO documentation"}]},{"id":"apply","summary":"Apply a process to each pixel","description":"Applies a process to each pixel value in the data cube (i.e. a local operation). In contrast, the process ``apply_dimension()`` applies a process to all pixel values along a particular dimension.","categories":["cubes"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"process","description":"A process that accepts and returns a single value and is applied on each individual value in the data cube. The process may consist of multiple sub-processes and could, for example, consist of processes such as ``abs()`` or ``linear_scale_range()``.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"x","description":"The value to process.","schema":{"description":"Any data type."}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the new data cube.","schema":{"description":"Any data type."}}}},{"name":"context","description":"Additional data to be passed to the process.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#apply","rel":"about","title":"Apply explained in the openEO documentation"}]},{"id":"arccos","summary":"Inverse cosine","description":"Computes the arc cosine of `x`. The arc cosine is the inverse function of the cosine so that *`arccos(cos(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed angle in radians.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":1},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/InverseCosine.html","title":"Inverse cosine explained by Wolfram MathWorld"}]},{"id":"arcosh","summary":"Inverse hyperbolic cosine","description":"Computes the inverse hyperbolic cosine of `x`. It is the inverse function of the hyperbolic cosine so that *`arcosh(cosh(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed angle in radians.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":1},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/InverseHyperbolicCosine.html","title":"Inverse hyperbolic cosine explained by Wolfram MathWorld"}]},{"id":"arcsin","summary":"Inverse sine","description":"Computes the arc sine of `x`. The arc sine is the inverse function of the sine so that *`arcsin(sin(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed angle in radians.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/InverseSine.html","title":"Inverse sine explained by Wolfram MathWorld"}]},{"id":"arctan","summary":"Inverse tangent","description":"Computes the arc tangent of `x`. The arc tangent is the inverse function of the tangent so that *`arctan(tan(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed angle in radians.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/InverseTangent.html","title":"Inverse tangent explained by Wolfram MathWorld"}]},{"id":"arctan2","summary":"Inverse tangent of two numbers","description":"Computes the arc tangent of two numbers `x` and `y`. It is similar to calculating the arc tangent of *`y / x`*, except that the signs of both arguments are used to determine the quadrant of the result.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.","categories":["math > trigonometric"],"parameters":[{"name":"y","description":"A number to be used as the dividend.","schema":{"type":["number","null"]}},{"name":"x","description":"A number to be used as the divisor.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed angle in radians.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"y":0,"x":0},"returns":0},{"arguments":{"y":null,"x":1.5},"returns":null}],"links":[{"rel":"about","href":"https://en.wikipedia.org/wiki/Atan2","title":"Two-argument inverse tangent explained by Wikipedia"}]},{"id":"array_apply","summary":"Apply a process to each array element","description":"Applies a process to each individual value in the array. This is basically what other languages call either a `for each` loop or a `map` function.","categories":["arrays"],"parameters":[{"name":"data","description":"An array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"process","description":"A process that accepts and returns a single value and is applied on each individual value in the array. The process may consist of multiple sub-processes and could, for example, consist of processes such as ``abs()`` or ``linear_scale_range()``.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"x","description":"The value of the current element being processed.","schema":{"description":"Any data type."}},{"name":"index","description":"The zero-based index of the current element being processed.","schema":{"type":"integer","minimum":0}},{"name":"label","description":"The label of the current element being processed. Only populated for labeled arrays.","schema":[{"type":"number"},{"type":"string"},{"type":"null"}],"default":null,"optional":true},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the new array.","schema":{"description":"Any data type."}}}},{"name":"context","description":"Additional data to be passed to the process.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"An array with the newly computed values. The number of elements are the same as for the original array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},"links":[{"rel":"example","type":"application/json","href":"https://processes.openeo.org/1.1.0/examples/array_find_nodata.json","title":"Find no-data values in arrays"},{"rel":"example","type":"application/json","href":"https://processes.openeo.org/1.1.0/examples/array_contains_nodata.json","title":"Check for no-data values in arrays"}]},{"id":"array_contains","summary":"Check whether the array contains a given value","description":"Checks whether the array specified for `data` contains the value specified in `value`. Returns `true` if there\'s a match, otherwise `false`.\\n\\n**Remarks:**\\n\\n* To get the index or the label of the value found, use ``array_find()``.\\n* All definitions for the process ``eq()`` regarding the comparison of values apply here as well. A `null` return value from ``eq()`` is handled exactly as `false` (no match).\\n* Data types MUST be checked strictly. For example, a string with the content *1* is not equal to the number *1*.\\n* An integer *1* is equal to a floating-point number *1.0* as `integer` is a sub-type of `number`. Still, this process may return unexpectedly `false` when comparing floating-point numbers due to floating-point inaccuracy in machine-based computation.\\n* Temporal strings are treated as normal strings and MUST NOT be interpreted.\\n* If the specified value is an array, object or null, the process always returns `false`. See the examples for one to check for `null` values.","categories":["arrays","comparison","reducer"],"parameters":[{"name":"data","description":"List to find the value in.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"value","description":"Value to find in `data`.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if the list contains the value, false` otherwise.","schema":{"type":"boolean"}},"examples":[{"arguments":{"data":[1,2,3],"value":2},"returns":true},{"arguments":{"data":["A","B","C"],"value":"b"},"returns":false},{"arguments":{"data":[1,2,3],"value":"2"},"returns":false},{"arguments":{"data":[1,2,null],"value":null},"returns":true},{"arguments":{"data":[[1,2],[3,4]],"value":[1,2]},"returns":false},{"arguments":{"data":[[1,2],[3,4]],"value":2},"returns":false},{"arguments":{"data":[{"a":"b"},{"c":"d"}],"value":{"a":"b"}},"returns":false}],"links":[{"rel":"example","type":"application/json","href":"https://processes.openeo.org/1.1.0/examples/array_contains_nodata.json","title":"Check for no-data values in arrays"}],"process_graph":{"find":{"process_id":"array_find","arguments":{"data":{"from_parameter":"data"},"value":{"from_parameter":"value"}}},"is_nodata":{"process_id":"is_nodata","arguments":{"x":{"from_node":"find"}}},"not":{"process_id":"not","arguments":{"x":{"from_node":"is_nodata"}},"result":true}}},{"id":"array_element","summary":"Get an element from an array","description":"Gives the element with the specified index or label from the array.\\n\\nEither the parameter `index` or `label` must be specified, otherwise the `ArrayElementParameterMissing` exception is thrown. If both parameters are set the `ArrayElementParameterConflict` exception is thrown.","categories":["arrays","reducer"],"parameters":[{"name":"data","description":"An array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"index","description":"The zero-based index of the element to retrieve.","schema":{"type":"integer","minimum":0},"optional":true},{"name":"label","description":"The label of the element to retrieve. Throws an `ArrayNotLabeled` exception, if the given array is not a labeled array and this parameter is set.","schema":[{"type":"number"},{"type":"string"}],"optional":true},{"name":"return_nodata","description":"By default this process throws an `ArrayElementNotAvailable` exception if the index or label is invalid. If you want to return `null` instead, set this flag to `true`.","schema":{"type":"boolean"},"default":false,"optional":true}],"returns":{"description":"The value of the requested element.","schema":{"description":"Any data type is allowed."}},"exceptions":{"ArrayElementNotAvailable":{"message":"The array has no element with the specified index or label."},"ArrayElementParameterMissing":{"message":"The process `array_element` requires either the `index` or `labels` parameter to be set."},"ArrayElementParameterConflict":{"message":"The process `array_element` only allows that either the `index` or the `labels` parameter is set."},"ArrayNotLabeled":{"message":"The array is not a labeled array, but the `label` parameter is set. Use the `index` instead."}},"examples":[{"arguments":{"data":[9,8,7,6,5],"index":2},"returns":7},{"arguments":{"data":["A","B","C"],"index":0},"returns":"A"},{"arguments":{"data":[],"index":0,"return_nodata":true},"returns":null}]},{"id":"array_filter","summary":"Filter an array based on a condition","description":"Filters the array elements based on a logical expression so that afterwards an array is returned that only contains the values, indices and/or labels conforming to the condition.","categories":["arrays","filter"],"parameters":[{"name":"data","description":"An array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"condition","description":"A condition that is evaluated against each value, index and/or label in the array. Only the array elements for which the condition returns `true` are preserved.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"x","description":"The value of the current element being processed.","schema":{"description":"Any data type."}},{"name":"index","description":"The zero-based index of the current element being processed.","schema":{"type":"integer","minimum":0}},{"name":"label","description":"The label of the current element being processed. Only populated for labeled arrays.","schema":[{"type":"number"},{"type":"string"},{"type":"null"}],"default":null,"optional":true},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"`true` if the value should be kept in the array, otherwise `false`.","schema":{"type":"boolean"}}}},{"name":"context","description":"Additional data to be passed to the condition.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"An array filtered by the specified condition. The number of elements are less than or equal compared to the original array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}}},{"id":"array_find","summary":"Get the index for a value in an array","description":"Checks whether the array specified for `data` contains the value specified in `value` and returns the zero-based index for the first match. If there\'s no match, `null` is returned.\\n\\n**Remarks:**\\n\\n* To get a boolean value returned use ``array_contains()``.\\n* All definitions for the process ``eq()`` regarding the comparison of values apply here as well. A `null` return value from ``eq()`` is handled exactly as `false` (no match).\\n* Data types MUST be checked strictly. For example, a string with the content *1* is not equal to the number *1*.\\n* An integer *1* is equal to a floating-point number *1.0* as `integer` is a sub-type of `number`. Still, this process may return unexpectedly `false` when comparing floating-point numbers due to floating-point inaccuracy in machine-based computation.\\n* Temporal strings are treated as normal strings and MUST NOT be interpreted.\\n* If the specified value is an array, object or null, the process always returns `null`. See the examples for one to find `null` values.","categories":["arrays","reducer"],"parameters":[{"name":"data","description":"List to find the value in.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"value","description":"Value to find in `data`.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"The index of the first element with the specified value. If no element was found, `null` is returned.","schema":[{"type":"null"},{"type":"integer","minimum":0}]},"examples":[{"arguments":{"data":[1,2,3],"value":2},"returns":1},{"arguments":{"data":["A","B","C"],"value":"b"},"returns":null},{"arguments":{"data":[1,2,3],"value":"2"},"returns":null},{"arguments":{"data":[1,null,2,null],"value":null},"returns":1},{"arguments":{"data":[[1,2],[3,4]],"value":[1,2]},"returns":null},{"arguments":{"data":[[1,2],[3,4]],"value":2},"returns":null},{"arguments":{"data":[{"a":"b"},{"c":"d"}],"value":{"a":"b"}},"returns":null}],"links":[{"rel":"example","type":"application/json","href":"https://processes.openeo.org/1.1.0/examples/array_find_nodata.json","title":"Find no-data values in arrays"}]},{"id":"array_labels","summary":"Get the labels for an array","description":"Gives all labels for a labeled array. The labels have the same order as in the array.","categories":["arrays"],"parameters":[{"name":"data","description":"An array with labels.","schema":{"type":"array","subtype":"labeled-array","items":{"description":"Any data type."}}}],"returns":{"description":"The labels as an array.","schema":{"type":"array","items":{"type":["number","string"]}}}},{"id":"arsinh","summary":"Inverse hyperbolic sine","description":"Computes the inverse hyperbolic sine of `x`. It is the inverse function of the hyperbolic sine so that *`arsinh(sinh(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed angle in radians.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/InverseHyperbolicSine.html","title":"Inverse hyperbolic sine explained by Wolfram MathWorld"}]},{"id":"artanh","summary":"Inverse hyperbolic tangent","description":"Computes the inverse hyperbolic tangent of `x`. It is the inverse function of the hyperbolic tangent so that *`artanh(tanh(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed angle in radians.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/InverseHyperbolicTangent.html","title":"Inverse hyperbolic tangent explained by Wolfram MathWorld"}]},{"id":"between","summary":"Between comparison","description":"By default, this process checks whether `x` is greater than or equal to `min` and lower than or equal to `max`, which is the same as computing `and(gte(x, min), lte(x, max))`. Therefore, all definitions from ``and()``, ``gte()`` and ``lte()`` apply here as well.\\n\\nIf `exclude_max` is set to `true` the upper bound is excluded so that the process checks whether `x` is greater than or equal to `min` and lower than `max`. In this case, the process works the same as computing `and(gte(x, min), lt(x, max))`.\\n\\nLower and upper bounds are not allowed to be swapped. So `min` MUST be lower than or equal to `max` or otherwise the process always returns `false`.","categories":["comparison"],"parameters":[{"name":"x","description":"The value to check.","schema":{"description":"Any data type is allowed."}},{"name":"min","description":"Lower boundary (inclusive) to check against.","schema":[{"type":"number"},{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"},{"type":"string","format":"time","subtype":"time"}]},{"name":"max","description":"Upper boundary (inclusive) to check against.","schema":[{"type":"number"},{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"},{"type":"string","format":"time","subtype":"time"}]},{"name":"exclude_max","description":"Exclude the upper boundary `max` if set to `true`. Defaults to `false`.","schema":{"type":"boolean"},"default":false,"optional":true}],"returns":{"description":"`true` if `x` is between the specified bounds, otherwise `false`.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":null,"min":0,"max":1},"returns":null},{"arguments":{"x":1,"min":0,"max":1},"returns":true},{"arguments":{"x":1,"min":0,"max":1,"exclude_max":true},"returns":false},{"description":"Swapped bounds (min is greater than max) MUST always return `false`.","arguments":{"x":0.5,"min":1,"max":0},"returns":false},{"arguments":{"x":-0.5,"min":-1,"max":0},"returns":true},{"arguments":{"x":"00:59:59Z","min":"01:00:00+01:00","max":"01:00:00Z"},"returns":true},{"arguments":{"x":"2018-07-23T17:22:45Z","min":"2018-01-01T00:00:00Z","max":"2018-12-31T23:59:59Z"},"returns":true},{"arguments":{"x":"2000-01-01","min":"2018-01-01","max":"2020-01-01"},"returns":false},{"arguments":{"x":"2018-12-31T17:22:45Z","min":"2018-01-01","max":"2018-12-31"},"returns":true},{"arguments":{"x":"2018-12-31T17:22:45Z","min":"2018-01-01","max":"2018-12-31","exclude_max":true},"returns":false}],"process_graph":{"gte":{"process_id":"gte","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"min"}}},"lte":{"process_id":"lte","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"max"}}},"lt":{"process_id":"lt","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"max"}}},"if":{"process_id":"if","arguments":{"value":{"from_parameter":"exclude_max"},"accept":{"from_node":"lte"},"reject":{"from_node":"lt"}}},"and":{"process_id":"and","arguments":{"x":{"from_node":"gte"},"y":{"from_node":"if"}},"result":true}}},{"id":"ceil","summary":"Round fractions up","description":"The least integer greater than or equal to the number `x`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > rounding"],"parameters":[{"name":"x","description":"A number to round up.","schema":{"type":["number","null"]}}],"returns":{"description":"The number rounded up.","schema":{"type":["integer","null"]}},"examples":[{"arguments":{"x":0},"returns":0},{"arguments":{"x":3.5},"returns":4},{"arguments":{"x":-0.4},"returns":0},{"arguments":{"x":-3.5},"returns":-3}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/CeilingFunction.html","title":"Ceiling explained by Wolfram MathWorld"}]},{"id":"climatological_normal","summary":"Compute climatology normals","description":"Climatological normal period is a usually 30 year average of a weather variable. Climatological normals are used as an average or baseline to evaluate climate events and provide context for yearly, monthly, daily or seasonal variability. The default climatology period is from 1981 until 2010 (both inclusive).","categories":["climatology"],"parameters":[{"name":"data","description":"A data cube with exactly one temporal dimension. The data cube must span at least the temporal interval specified in the parameter `climatology-period`.\\n\\nSeasonal periods may span two consecutive years, e.g. temporal winter that includes months December, January and February. If the required months before the actual climate period are available, the season is taken into account. If not available, the first season is not taken into account and the seasonal mean is based on one year less than the other seasonal normals. The incomplete season at the end of the last year is never taken into account.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"period","description":"The time intervals to aggregate the average value for. The following pre-defined frequencies are supported:\\n\\n* `day`: Day of the year\\n* `month`: Month of the year\\n* `climatology-period`: The period specified in the `climatology-period`.\\n* `season`: Three month periods of the calendar seasons (December - February, March - May, June - August, September - November).\\n* `tropical-season`: Six month periods of the tropical seasons (November - April, May - October).","schema":{"type":"string","enum":["day","month","season","tropical-season","climatology-period"]}},{"name":"climatology_period","description":"The climatology period as a closed temporal interval. The first element of the array is the first year to be fully included in the temporal interval. The second element is the last year to be fully included in the temporal interval. The default period is from 1981 until 2010 (both inclusive).","schema":{"type":"array","subtype":"temporal-interval","minItems":2,"maxItems":2,"items":{"type":"string","subtype":"year","minLength":4,"maxLength":4,"pattern":"^\\\\d{4}$"}},"default":["1981","2010"],"optional":true}],"returns":{"description":"A data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the temporal dimension. The temporal dimension has the following dimension labels:\\n\\n* `day`: `001` - `365`\\n* `month`: `01` - `12`\\n* `climatology-period`: `climatology-period`\\n* `season`: `djf` (December - February), `mam` (March - May), `jja` (June - August), `son` (September - November)\\n* `tropical-season`: `ndjfma` (November - April), `mjjaso` (May - October)","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"rel":"about","href":"https://en.wikipedia.org/wiki/Climatological_normal","title":"Background information on climatology normal by Wikipedia"}]},{"id":"clip","summary":"Clip a value between a minimum and a maximum","description":"Clips a number between specified minimum and maximum values. A value larger than the maximum value is set to the maximum value, a value lower than the minimum value is set to the minimum value.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}},{"name":"min","description":"Minimum value. If the value is lower than this value, the process will return the value of this parameter.","schema":{"type":"number"}},{"name":"max","description":"Maximum value. If the value is greater than this value, the process will return the value of this parameter.","schema":{"type":"number"}}],"returns":{"description":"The value clipped to the specified range.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":-5,"min":-1,"max":1},"returns":-1},{"arguments":{"x":10.001,"min":1,"max":10},"returns":10},{"arguments":{"x":0.000001,"min":0,"max":0.02},"returns":0.000001},{"arguments":{"x":null,"min":0,"max":1},"returns":null}],"process_graph":{"min":{"process_id":"min","arguments":{"data":[{"from_parameter":"max"},{"from_parameter":"x"}]}},"max":{"process_id":"max","arguments":{"data":[{"from_parameter":"min"},{"from_node":"min"}]},"result":true}}},{"id":"constant","summary":"Define a constant value","description":"Defines a constant value that can be reused in multiple places of a process.","categories":["math > constants"],"parameters":[{"name":"x","description":"The value of the constant.","schema":{"description":"Any data type."}}],"returns":{"description":"The value of the constant.","schema":{"description":"Any data type."}}},{"id":"cos","summary":"Cosine","description":"Computes the cosine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"An angle in radians.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed cosine of `x`.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":1}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Cosine.html","title":"Cosine explained by Wolfram MathWorld"}]},{"id":"cosh","summary":"Hyperbolic cosine","description":"Computes the hyperbolic cosine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"An angle in radians.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed hyperbolic cosine of `x`.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":1}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/HyperbolicCosine.html","title":"Hyperbolic cosine explained by Wolfram MathWorld"}]},{"id":"count","summary":"Count the number of elements","description":"Gives the number of elements in an array that matches the specified condition.\\n\\n**Remarks:**\\n\\n* Counts the number of valid elements by default (`condition` is set to `null`). A valid element is every element for which ``is_valid()`` returns `true`.\\n* To count all elements in a list set the `condition` parameter to boolean `true`.","categories":["arrays","reducer"],"parameters":[{"name":"data","description":"An array with elements of any data type.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"condition","description":"A condition consists of one or more processes, which in the end return a boolean value. It is evaluated against each element in the array. An element is counted only if the condition returns `true`. Defaults to count valid elements in a list (see ``is_valid()``). Setting this parameter to boolean `true` counts all elements in the list.","schema":[{"title":"Condition","description":"A logical expression that is evaluated against each element in the array.","type":"object","subtype":"process-graph","parameters":[{"name":"x","description":"The value of the current element being processed.","schema":{"description":"Any data type."}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"`true` if the element should increase the counter, otherwise `false`.","schema":{"type":"boolean"}}},{"title":"All elements","description":"Boolean `true` counts all elements in the list.","type":"boolean","const":true},{"title":"Valid elements","description":"`null` counts valid elements in the list.","type":"null"}],"default":null,"optional":true},{"name":"context","description":"Additional data to be passed to the condition.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The counted number of elements.","schema":{"type":"number"}},"examples":[{"arguments":{"data":[]},"returns":0},{"arguments":{"data":[1,0,3,2]},"returns":4},{"arguments":{"data":["ABC",null]},"returns":1},{"arguments":{"data":[false,null],"condition":true},"returns":2},{"arguments":{"data":[0,1,2,3,4,5,null],"condition":{"gt":{"process_id":"gt","arguments":{"x":{"from_parameter":"element"},"y":2},"result":true}}},"returns":3}]},{"id":"create_raster_cube","summary":"Create an empty raster data cube","description":"Creates a new raster data cube without dimensions. Dimensions can be added with ``add_dimension()``.","categories":["cubes"],"parameters":[],"returns":{"description":"An empty raster data cube with zero dimensions.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html","rel":"about","title":"Data Cubes explained in the openEO documentation"}]},{"id":"dimension_labels","summary":"Get the dimension labels","description":"Gives all labels for a dimension in the data cube. The labels have the same order as in the data cube.\\n\\nIf a dimension with the specified name does not exist, the process fails with a `DimensionNotAvailable` exception.","categories":["cubes"],"parameters":[{"name":"data","description":"The data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"dimension","description":"The name of the dimension to get the labels for.","schema":{"type":"string"}}],"returns":{"description":"The labels as an array.","schema":{"type":"array","items":{"type":["number","string"]}}},"exceptions":{"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."}}},{"id":"divide","summary":"Division of two numbers","description":"Divides argument `x` by the argument `y` (*`x / y`*) and returns the computed result.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, a division by zero results in ±infinity if the processing environment supports it. Otherwise, a `DivisionByZero` exception must the thrown.","categories":["math"],"parameters":[{"name":"x","description":"The dividend.","schema":{"type":["number","null"]}},{"name":"y","description":"The divisor.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed result.","schema":{"type":["number","null"]}},"exceptions":{"DivisionByZero":{"message":"Division by zero is not supported."}},"examples":[{"arguments":{"x":5,"y":2.5},"returns":2},{"arguments":{"x":-2,"y":4},"returns":-0.5},{"arguments":{"x":1,"y":null},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Division.html","title":"Division explained by Wolfram MathWorld"},{"rel":"about","href":"https://ieeexplore.ieee.org/document/8766229","title":"IEEE Standard 754-2019 for Floating-Point Arithmetic"}]},{"id":"drop_dimension","summary":"Remove a dimension","description":"Drops a dimension from the data cube.\\n\\nDropping a dimension only works on dimensions with a single dimension label left, otherwise the process fails with a `DimensionLabelCountMismatch` exception. Dimension values can be reduced to a single value with a filter such as ``filter_bands()`` or the ``reduce_dimension()`` process. If a dimension with the specified name does not exist, the process fails with a `DimensionNotAvailable` exception.","categories":["cubes"],"parameters":[{"name":"data","description":"The data cube to drop a dimension from.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"name","description":"Name of the dimension to drop.","schema":{"type":"string"}}],"returns":{"description":"A data cube without the specified dimension. The number of dimensions decreases by one, but the dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"DimensionLabelCountMismatch":{"message":"The number of dimension labels exceeds one, which requires a reducer."},"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."}}},{"id":"e","summary":"Euler\'s number (e)","description":"The real number *e* is a mathematical constant that is the base of the natural logarithm such that *`ln(e) = 1`*. The numerical value is approximately *2.71828*.","categories":["math > constants","math > exponential & logarithmic"],"parameters":[],"returns":{"description":"The numerical value of Euler\'s number.","schema":{"type":"number"}},"links":[{"rel":"about","href":"http://mathworld.wolfram.com/e.html","title":"Mathematical constant e explained by Wolfram MathWorld"}]},{"id":"eq","summary":"Equal to comparison","description":"Compares whether `x` is strictly equal to `y`.\\n\\n**Remarks:**\\n\\n* Data types MUST be checked strictly. For example, a string with the content *1* is not equal to the number *1*. Nevertheless, an integer *1* is equal to a floating-point number *1.0* as `integer` is a sub-type of `number`.\\n* If any operand is `null`, the return value is `null`. Therefore, `eq(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* Strings are expected to be encoded in UTF-8 by default.\\n* Temporal strings MUST be compared differently than other strings and MUST NOT be compared based on their string representation due to different possible representations. For example, the time zone representation `Z` (for UTC) has the same meaning as `+00:00`.","categories":["texts","comparison"],"parameters":[{"name":"x","description":"First operand.","schema":{"description":"Any data type is allowed."}},{"name":"y","description":"Second operand.","schema":{"description":"Any data type is allowed."}},{"name":"delta","description":"Only applicable for comparing two numbers. If this optional parameter is set to a positive non-zero number the equality of two numbers is checked against a delta value. This is especially useful to circumvent problems with floating-point inaccuracy in machine-based computation.\\n\\nThis option is basically an alias for the following computation: `lte(abs(minus([x, y]), delta)`","schema":{"type":["number","null"]},"default":null,"optional":true},{"name":"case_sensitive","description":"Only applicable for comparing two strings. Case sensitive comparison can be disabled by setting this parameter to `false`.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"`true` if `x` is equal to `y`, `null` if any operand is `null`, otherwise `false`.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":1,"y":null},"returns":null},{"arguments":{"x":null,"y":null},"returns":null},{"arguments":{"x":1,"y":1},"returns":true},{"arguments":{"x":1,"y":"1"},"returns":false},{"arguments":{"x":0,"y":false},"returns":false},{"arguments":{"x":1.02,"y":1,"delta":0.01},"returns":false},{"arguments":{"x":-1,"y":-1.001,"delta":0.01},"returns":true},{"arguments":{"x":115,"y":110,"delta":10},"returns":true},{"arguments":{"x":"Test","y":"test"},"returns":false},{"arguments":{"x":"Test","y":"test","case_sensitive":false},"returns":true},{"arguments":{"x":"Ä","y":"ä","case_sensitive":false},"returns":true},{"arguments":{"x":"00:00:00+00:00","y":"00:00:00Z"},"returns":true},{"description":"`y` is not a valid date-time representation and therefore will be treated as a string so that the provided values are not equal.","arguments":{"x":"2018-01-01T12:00:00Z","y":"2018-01-01T12:00:00"},"returns":false},{"description":"01:00 in the time zone +1 is equal to 00:00 in UTC.","arguments":{"x":"2018-01-01T00:00:00Z","y":"2018-01-01T01:00:00+01:00"},"returns":true},{"arguments":{"x":[1,2,3],"y":[1,2,3]},"returns":false}]},{"id":"exp","summary":"Exponentiation to the base e","description":"Exponential function to the base *e* raised to the power of `p`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > exponential & logarithmic"],"parameters":[{"name":"p","description":"The numerical exponent.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed value for *e* raised to the power of `p`.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"p":0},"returns":1},{"arguments":{"p":null},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/ExponentialFunction.html","title":"Exponential function explained by Wolfram MathWorld"}],"process_graph":{"e":{"process_id":"e","arguments":{}},"power":{"process_id":"power","arguments":{"base":{"from_node":"e"},"p":{"from_parameter":"p"}},"result":true}}},{"id":"extrema","summary":"Minimum and maximum values","description":"Two element array containing the minimum and the maximum values of `data`.\\n\\nThis process is basically an alias for calling both ``min()`` and ``max()``, but may be implemented more performant by back-ends as it only needs to iterate over the data once instead of twice.","categories":["math"],"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that an array with two `null` values is returned if any value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"An array containing the minimum and maximum values for the specified numbers. The first element is the minimum, the second element is the maximum. If the input array is empty both elements are set to `null`.","schema":[{"type":"array","minItems":2,"maxItems":2,"items":{"type":"number"}},{"type":"array","minItems":2,"maxItems":2,"items":{"type":"null"}}]},"examples":[{"arguments":{"data":[1,0,3,2]},"returns":[0,3]},{"arguments":{"data":[5,2.5,null,-0.7]},"returns":[-0.7,5]},{"arguments":{"data":[1,0,3,null,2],"ignore_nodata":false},"returns":[null,null]},{"description":"The input array is empty: return two `null` values.","arguments":{"data":[]},"returns":[null,null]}]},{"id":"filter_bands","summary":"Filter the bands by names","description":"Filters the bands in the data cube so that bands that don\'t match any of the criteria are dropped from the data cube. The data cube is expected to have only one dimension of type `bands`. Fails with a `DimensionMissing` exception if no such dimension exists.\\n\\nThe following criteria can be used to select bands:\\n\\n* `bands`: band name or common band name (e.g. `B01`, `B8A`, `red` or `nir`)\\n* `wavelengths`: ranges of wavelengths in micrometers (μm) (e.g. 0.5 - 0.6)\\n\\nAll these information are exposed in the band metadata of the collection. To keep algorithms interoperable it is recommended to prefer the common band names or the wavelengths over band names that are specific to the collection and/or back-end.\\n\\nIf multiple criteria are specified, any of them must match and not all of them, i.e. they are combined with an OR-operation. If no criteria are specified, the `BandFilterParameterMissing` exception must be thrown.\\n\\n**Important:** The order of the specified array defines the order of the bands in the data cube, which can be important for subsequent processes. If multiple bands are matched by a single criterion (e.g. a range of wavelengths), they stay in the original order.","categories":["cubes","filter"],"parameters":[{"name":"data","description":"A data cube with bands.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"bands","description":"A list of band names. Either the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands). If the unique band name and the common name conflict, the unique band name has a higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.","schema":{"type":"array","items":{"type":"string","subtype":"band-name"}},"default":[],"optional":true},{"name":"wavelengths","description":"A list of sub-lists with each sub-list consisting of two elements. The first element is the minimum wavelength and the second element is the maximum wavelength. Wavelengths are specified in micrometers (μm).\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match the wavelengths, all matched bands are included in the original order.","schema":{"type":"array","items":{"type":"array","minItems":2,"maxItems":2,"items":{"type":"number"},"examples":[[[0.45,0.5],[0.6,0.7]]]}},"default":[],"optional":true}],"returns":{"description":"A data cube limited to a subset of its original bands. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the dimension of type `bands` has less (or the same) dimension labels.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"BandFilterParameterMissing":{"message":"The process `filter_bands` requires any of the parameters `bands`, `common_names` or `wavelengths` to be set."},"DimensionMissing":{"message":"A band dimension is missing."}},"links":[{"rel":"about","href":"https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names","title":"List of common band names as specified by the STAC specification"},{"href":"https://openeo.org/documentation/1.0/datacubes.html#filter","rel":"about","title":"Filters explained in the openEO documentation"}]},{"id":"filter_bbox","summary":"Spatial filter using a bounding box","description":"Limits the data cube to the specified bounding box.\\n\\nThe filter retains a pixel in the data cube if the point at the pixel center intersects with the bounding box (as defined in the Simple Features standard by the OGC).","categories":["cubes","filter"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"extent","description":"A bounding box, which may include a vertical axis (see `base` and `height`).","schema":{"type":"object","subtype":"bounding-box","required":["west","south","east","north"],"properties":{"west":{"description":"West (lower left corner, coordinate axis 1).","type":"number"},"south":{"description":"South (lower left corner, coordinate axis 2).","type":"number"},"east":{"description":"East (upper right corner, coordinate axis 1).","type":"number"},"north":{"description":"North (upper right corner, coordinate axis 2).","type":"number"},"base":{"description":"Base (optional, lower left corner, coordinate axis 3).","type":["number","null"],"default":null},"height":{"description":"Height (optional, upper right corner, coordinate axis 3).","type":["number","null"],"default":null},"crs":{"description":"Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html) or [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.","anyOf":[{"title":"EPSG Code","type":"integer","subtype":"epsg-code","minimum":1000,"examples":[3857]},{"title":"WKT2","type":"string","subtype":"wkt2-definition"},{"title":"PROJ definition","type":"string","subtype":"proj-definition","deprecated":true}],"default":4326}}}}],"returns":{"description":"A data cube restricted to the bounding box. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the spatial dimensions have less (or the same) dimension labels.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#filter","rel":"about","title":"Filters explained in the openEO documentation"},{"rel":"about","href":"https://proj.org/usage/projections.html","title":"PROJ parameters for cartographic projections"},{"rel":"about","href":"http://www.epsg-registry.org","title":"Official EPSG code registry"},{"rel":"about","href":"http://www.epsg.io","title":"Unofficial EPSG code database"},{"href":"http://www.opengeospatial.org/standards/sfa","rel":"about","title":"Simple Features standard by the OGC"}]},{"id":"filter_spatial","summary":"Spatial filter using geometries","description":"Limits the data cube over the spatial dimensions to the specified geometries.\\n\\n- For **polygons**, the filter retains a pixel in the data cube if the point at the pixel center intersects with at least one of the polygons (as defined in the Simple Features standard by the OGC).\\n- For **points**, the process considers the closest pixel center.\\n- For **lines** (line strings), the process considers all the pixels whose centers are closest to at least one point on the line.\\n\\nMore specifically, pixels outside of the bounding box of the given geometry will not be available after filtering. All pixels inside the bounding box that are not retained will be set to `null` (no data).","categories":["cubes","filter"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"geometries","description":"One or more geometries used for filtering, specified as GeoJSON.","schema":{"type":"object","subtype":"geojson"}}],"returns":{"description":"A data cube restricted to the specified geometries. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the spatial dimensions have less (or the same) dimension labels.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#filter","rel":"about","title":"Filters explained in the openEO documentation"},{"href":"http://www.opengeospatial.org/standards/sfa","rel":"about","title":"Simple Features standard by the OGC"}]},{"id":"filter_temporal","summary":"Temporal filter for a temporal intervals","description":"Limits the data cube to the specified interval of dates and/or times.\\n\\nMore precisely, the filter checks whether each of the temporal dimension labels is greater than or equal to the lower boundary (start date/time) and less than the value of the upper boundary (end date/time). This corresponds to a left-closed interval, which contains the lower boundary but not the upper boundary.","categories":["cubes","filter"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"extent","description":"Left-closed temporal interval, i.e. an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified instance in time is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified instance in time is **excluded** from the interval.\\n\\nThe specified temporal strings follow [RFC 3339](https://www.rfc-editor.org/rfc/rfc3339.html). Also supports open intervals by setting one of the boundaries to `null`, but never both.","schema":{"type":"array","subtype":"temporal-interval","minItems":2,"maxItems":2,"items":{"anyOf":[{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"},{"type":"string","subtype":"year","minLength":4,"maxLength":4,"pattern":"^\\\\d{4}$"},{"type":"null"}]},"examples":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"],["2015-01-01","2016-01-01"]]}},{"name":"dimension","description":"The name of the temporal dimension to filter on. If no specific dimension is specified or it is set to `null`, the filter applies to all temporal dimensions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.","schema":{"type":["string","null"]},"default":null,"optional":true}],"returns":{"description":"A data cube restricted to the specified temporal extent. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the temporal dimensions (determined by `dimensions` parameter) may have less dimension labels.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#filter","rel":"about","title":"Filters explained in the openEO documentation"}]},{"id":"first","summary":"First element","description":"Gives the first element of an array.\\n\\nAn array without non-`null` elements resolves always with `null`.","categories":["arrays","reducer"],"parameters":[{"name":"data","description":"An array with elements of any data type.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if the first value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"The first element of the input array.","schema":{"description":"Any data type is allowed."}},"examples":[{"arguments":{"data":[1,0,3,2]},"returns":1},{"arguments":{"data":[null,"A","B"]},"returns":"A"},{"arguments":{"data":[null,2,3],"ignore_nodata":false},"returns":null},{"description":"The input array is empty: return `null`.","arguments":{"data":[]},"returns":null}]},{"id":"floor","summary":"Round fractions down","description":"The greatest integer less than or equal to the number `x`.\\n\\nThis process is *not* an alias for the ``int()`` process as defined by some mathematicians, see the examples for negative numbers in both processes for differences.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > rounding"],"parameters":[{"name":"x","description":"A number to round down.","schema":{"type":["number","null"]}}],"returns":{"description":"The number rounded down.","schema":{"type":["integer","null"]}},"examples":[{"arguments":{"x":0},"returns":0},{"arguments":{"x":3.5},"returns":3},{"arguments":{"x":-0.4},"returns":-1},{"arguments":{"x":-3.5},"returns":-4}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/FloorFunction.html","title":"Floor explained by Wolfram MathWorld"}]},{"id":"gt","summary":"Greater than comparison","description":"Compares whether `x` is strictly greater than `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If any operand is an array or object, the return value is `false`.\\n* If any operand is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.","categories":["comparison"],"parameters":[{"name":"x","description":"First operand.","schema":{"description":"Any data type is allowed."}},{"name":"y","description":"Second operand.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if `x` is strictly greater than `y` or `null` if any operand is `null`, otherwise `false`.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":1,"y":null},"returns":null},{"arguments":{"x":0,"y":0},"returns":false},{"arguments":{"x":2,"y":1},"returns":true},{"arguments":{"x":-0.5,"y":-0.6},"returns":true},{"arguments":{"x":"00:00:00Z","y":"00:00:00+01:00"},"returns":true},{"arguments":{"x":"1950-01-01T00:00:00Z","y":"2018-01-01T12:00:00Z"},"returns":false},{"arguments":{"x":"2018-01-01T12:00:00+00:00","y":"2018-01-01T12:00:00Z"},"returns":false},{"arguments":{"x":true,"y":0},"returns":false},{"arguments":{"x":true,"y":false},"returns":false}]},{"id":"gte","summary":"Greater than or equal to comparison","description":"Compares whether `x` is greater than or equal to `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`. Therefore, `gte(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* If the operands are not equal (see process ``eq()``) and any of them is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.","categories":["comparison"],"parameters":[{"name":"x","description":"First operand.","schema":{"description":"Any data type is allowed."}},{"name":"y","description":"Second operand.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if `x` is greater than or equal to `y`, `null` if any operand is `null`, otherwise `false`.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":1,"y":null},"returns":null},{"arguments":{"x":0,"y":0},"returns":true},{"arguments":{"x":1,"y":2},"returns":false},{"arguments":{"x":-0.5,"y":-0.6},"returns":true},{"arguments":{"x":"00:00:00Z","y":"00:00:00+01:00"},"returns":true},{"arguments":{"x":"1950-01-01T00:00:00Z","y":"2018-01-01T12:00:00Z"},"returns":false},{"arguments":{"x":"2018-01-01T12:00:00+00:00","y":"2018-01-01T12:00:00Z"},"returns":true},{"arguments":{"x":true,"y":false},"returns":false},{"arguments":{"x":[1,2,3],"y":[1,2,3]},"returns":false}],"process_graph":{"eq":{"process_id":"eq","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"y"}}},"gt":{"process_id":"gt","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"y"}}},"or":{"process_id":"or","arguments":{"x":{"from_node":"gt"},"y":{"from_node":"eq"}},"result":true}}},{"id":"if","summary":"If-Then-Else conditional","description":"If the value passed is `true`, returns the value of the `accept` parameter, otherwise returns the value of the `reject` parameter.\\n\\nThis is basically an if-then-else construct as in other programming languages.","categories":["logic","comparison","masks"],"parameters":[{"name":"value","description":"A boolean value.","schema":{"type":["boolean","null"]}},{"name":"accept","description":"A value that is returned if the boolean value is `true`.","schema":{"description":"Any data type is allowed."}},{"name":"reject","description":"A value that is returned if the boolean value is **not** `true`. Defaults to `null`.","schema":{"description":"Any data type is allowed."},"default":null,"optional":true}],"returns":{"description":"Either the `accept` or `reject` argument depending on the given boolean value.","schema":{"description":"Any data type is allowed."}},"examples":[{"arguments":{"value":true,"accept":"A","reject":"B"},"returns":"A"},{"arguments":{"value":null,"accept":"A","reject":"B"},"returns":"B"},{"arguments":{"value":false,"accept":[1,2,3],"reject":[4,5,6]},"returns":[4,5,6]},{"arguments":{"value":true,"accept":123},"returns":123},{"arguments":{"value":false,"accept":1},"returns":null}]},{"id":"int","summary":"Integer part of a number","description":"The integer part of the real number `x`.\\n\\nThis process is *not* an alias for the ``floor()`` process as defined by some mathematicians, see the examples for negative numbers in both processes for differences.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math","math > rounding"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}}],"returns":{"description":"Integer part of the number.","schema":{"type":["integer","null"]}},"examples":[{"arguments":{"x":0},"returns":0},{"arguments":{"x":3.5},"returns":3},{"arguments":{"x":-0.4},"returns":0},{"arguments":{"x":-3.5},"returns":-3}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/IntegerPart.html","title":"Integer Part explained by Wolfram MathWorld"}]},{"id":"is_nan","summary":"Value is not a number","description":"Checks whether the specified value `x` is not a number. Returns `true` for numeric values (integers and floating-point numbers), except for the special value `NaN` as defined by the [IEEE Standard 754](https://ieeexplore.ieee.org/document/4610935). All non-numeric data types MUST also return `true`, including arrays that contain `NaN` values.","categories":["comparison","math > constants"],"parameters":[{"name":"x","description":"The data to check.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if the data is not a number, otherwise `false`.","schema":{"type":"boolean"}},"examples":[{"arguments":{"x":1},"returns":false},{"arguments":{"x":"Test"},"returns":true},{"arguments":{"x":null},"returns":true}],"links":[{"rel":"about","href":"https://ieeexplore.ieee.org/document/4610935","title":"IEEE Standard 754-2008 for Floating-Point Arithmetic"},{"rel":"about","href":"http://mathworld.wolfram.com/NaN.html","title":"NaN explained by Wolfram MathWorld"}]},{"id":"is_nodata","summary":"Value is not a no-data value","description":"Checks whether the specified data is a missing data, i.e. equals to `null` or any of the no-data values specified in the metadata. The special numerical value `NaN` (not a number) as defined by the [IEEE Standard 754](https://ieeexplore.ieee.org/document/4610935) is not considered no-data and must return `false`.","categories":["comparison"],"parameters":[{"name":"x","description":"The data to check.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if the data is a no-data value, otherwise `false`.","schema":{"type":"boolean"}},"examples":[{"arguments":{"x":1},"returns":false},{"arguments":{"x":"Test"},"returns":false},{"arguments":{"x":null},"returns":true},{"arguments":{"x":[null,null]},"returns":false}]},{"id":"is_valid","summary":"Value is valid data","description":"Checks whether the specified value `x` is valid. The following values are considered valid:\\n\\n* Any finite numerical value (integers and floating-point numbers). The definition of finite numbers follows the [IEEE Standard 754](https://ieeexplore.ieee.org/document/4610935) and excludes the special value `NaN` (not a number).\\n* Any other value that is not a no-data value according to ``is_nodata()`. Thus all arrays, objects and strings are valid, regardless of their content.","categories":["comparison"],"parameters":[{"name":"x","description":"The data to check.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if the data is valid, otherwise `false`.","schema":{"type":"boolean"}},"examples":[{"arguments":{"x":1},"returns":true},{"arguments":{"x":"Test"},"returns":true},{"arguments":{"x":null},"returns":false},{"arguments":{"x":[null,null]},"returns":true}],"links":[{"rel":"about","href":"https://ieeexplore.ieee.org/document/4610935","title":"IEEE Standard 754-2008 for Floating-Point Arithmetic"}]},{"id":"last","summary":"Last element","description":"Gives the last element of an array.\\n\\nAn array without non-`null` elements resolves always with `null`.","categories":["arrays","reducer"],"parameters":[{"name":"data","description":"An array with elements of any data type.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if the last value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"The last element of the input array.","schema":{"description":"Any data type is allowed."}},"examples":[{"arguments":{"data":[1,0,3,2]},"returns":2},{"arguments":{"data":["A","B",null]},"returns":"B"},{"arguments":{"data":[0,1,null],"ignore_nodata":false},"returns":null},{"description":"The input array is empty: return `null`.","arguments":{"data":[]},"returns":null}]},{"id":"linear_scale_range","summary":"Linear transformation between two ranges","description":"Performs a linear transformation between the input and output range.\\n\\nThe given number in `x` is clipped to the bounds specified in `inputMin` and `inputMax` so that the underlying formula *`((x - inputMin) / (inputMax - inputMin)) * (outputMax - outputMin) + outputMin`* never returns any value lower than `outputMin` or greater than `outputMax`.\\n\\nPotential use case include\\n\\n* scaling values to the 8-bit range (0 - 255) often used for numeric representation of values in one of the channels of the [RGB colour model](https://en.wikipedia.org/wiki/RGB_color_model#Numeric_representations) or\\n* calculating percentages (0 - 100).\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math"],"parameters":[{"name":"x","description":"A number to transform. The number gets clipped to the bounds specified in `inputMin` and `inputMax`.","schema":{"type":["number","null"]}},{"name":"inputMin","description":"Minimum value the input can obtain.","schema":{"type":"number"}},{"name":"inputMax","description":"Maximum value the input can obtain.","schema":{"type":"number"}},{"name":"outputMin","description":"Minimum value of the desired output range.","schema":{"type":"number"},"default":0,"optional":true},{"name":"outputMax","description":"Maximum value of the desired output range.","schema":{"type":"number"},"default":1,"optional":true}],"returns":{"description":"The transformed number.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0.3,"inputMin":-1,"inputMax":1,"outputMin":0,"outputMax":255},"returns":165.75},{"arguments":{"x":25.5,"inputMin":0,"inputMax":255},"returns":0.1},{"arguments":{"x":null,"inputMin":0,"inputMax":100},"returns":null},{"description":"Shows that the input data is clipped.","arguments":{"x":1.12,"inputMin":0,"inputMax":1,"outputMin":0,"outputMax":255},"returns":255}],"process_graph":{"subtract1":{"process_id":"subtract","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"inputMin"}}},"subtract2":{"process_id":"subtract","arguments":{"x":{"from_parameter":"inputMax"},"y":{"from_parameter":"inputMin"}}},"subtract3":{"process_id":"subtract","arguments":{"x":{"from_parameter":"outputMax"},"y":{"from_parameter":"outputMin"}}},"divide":{"process_id":"divide","arguments":{"x":{"from_node":"subtract1"},"y":{"from_node":"subtract2"}}},"multiply":{"process_id":"multiply","arguments":{"x":{"from_node":"divide"},"y":{"from_node":"subtract3"}}},"add":{"process_id":"add","arguments":{"x":{"from_node":"multiply"},"y":{"from_parameter":"outputMin"}},"result":true}}},{"id":"ln","summary":"Natural logarithm","description":"The natural logarithm is the logarithm to the base *e* of the number `x`, which equals to using the *log* process with the base set to *e*. The natural logarithm is the inverse function of taking *e* to the power x.\\n\\nThe no-data value `null` is passed through.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, *`ln(0)`* results in ±infinity if the processing environment supports it or otherwise an exception is thrown.","categories":["math > exponential & logarithmic"],"parameters":[{"name":"x","description":"A number to compute the natural logarithm for.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed natural logarithm.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":1},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/NaturalLogarithm.html","title":"Natural logarithm explained by Wolfram MathWorld"},{"rel":"about","href":"https://ieeexplore.ieee.org/document/8766229","title":"IEEE Standard 754-2019 for Floating-Point Arithmetic"}],"process_graph":{"e":{"process_id":"e","arguments":{}},"log":{"process_id":"log","arguments":{"x":{"from_parameter":"x"},"base":{"from_node":"e"}},"result":true}}},{"id":"load_collection","summary":"Load a collection","description":"Loads a collection from the current back-end by its id and returns it as a processable data cube. The data that is added to the data cube can be restricted with the additional `spatial_extent`, `temporal_extent`, `bands` and `properties`.\\n\\n**Remarks:**\\n\\n* The bands (and all dimensions that specify nominal dimension labels) are expected to be ordered as specified in the metadata if the `bands` parameter is set to `null`.\\n* If no additional parameter is specified this would imply that the whole data set is expected to be loaded. Due to the large size of many data sets, this is not recommended and may be optimized by back-ends to only load the data that is actually required after evaluating subsequent processes such as filters. This means that the pixel values should be processed only after the data has been limited to the required extent and as a consequence also to a manageable size.","categories":["cubes","import"],"parameters":[{"name":"id","description":"The collection id.","schema":{"type":"string","subtype":"collection-id","pattern":"^[\\\\w\\\\-\\\\.~/]+$"}},{"name":"spatial_extent","description":"Limits the data to load from the collection to the specified bounding box or polygons.\\n\\nThe process puts a pixel into the data cube if the point at the pixel center intersects with the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC).\\n\\nThe GeoJSON can be one of the following feature types:\\n\\n* A `Polygon` or `MultiPolygon` geometry,\\n* a `Feature` with a `Polygon` or `MultiPolygon` geometry,\\n* a `FeatureCollection` containing at least one `Feature` with `Polygon` or `MultiPolygon` geometries, or\\n* a `GeometryCollection` containing `Polygon` or `MultiPolygon` geometries. To maximize interoperability, `GeometryCollection` should be avoided in favour of one of the alternatives above.\\n\\nSet this parameter to `null` to set no limit for the spatial extent. Be careful with this when loading large datasets! It is recommended to use this parameter instead of using ``filter_bbox()`` or ``filter_spatial()`` directly after loading unbounded data.","schema":[{"title":"Bounding Box","type":"object","subtype":"bounding-box","required":["west","south","east","north"],"properties":{"west":{"description":"West (lower left corner, coordinate axis 1).","type":"number"},"south":{"description":"South (lower left corner, coordinate axis 2).","type":"number"},"east":{"description":"East (upper right corner, coordinate axis 1).","type":"number"},"north":{"description":"North (upper right corner, coordinate axis 2).","type":"number"},"base":{"description":"Base (optional, lower left corner, coordinate axis 3).","type":["number","null"],"default":null},"height":{"description":"Height (optional, upper right corner, coordinate axis 3).","type":["number","null"],"default":null},"crs":{"description":"Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html) or [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.","anyOf":[{"title":"EPSG Code","type":"integer","subtype":"epsg-code","minimum":1000,"examples":[3857]},{"title":"WKT2","type":"string","subtype":"wkt2-definition"},{"title":"PROJ definition","type":"string","subtype":"proj-definition","deprecated":true}],"default":4326}}},{"title":"GeoJSON","description":"Limits the data cube to the bounding box of the given geometry. All pixels inside the bounding box that do not intersect with any of the polygons will be set to no data (`null`).","type":"object","subtype":"geojson"},{"title":"No filter","description":"Don\'t filter spatially. All data is included in the data cube.","type":"null"}]},{"name":"temporal_extent","description":"Limits the data to load from the collection to the specified left-closed temporal interval. Applies to all temporal dimensions. The interval has to be specified as an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified instance in time is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified instance in time is **excluded** from the interval.\\n\\nThe specified temporal strings follow [RFC 3339](https://www.rfc-editor.org/rfc/rfc3339.html). Also supports open intervals by setting one of the boundaries to `null`, but never both.\\n\\nSet this parameter to `null` to set no limit for the temporal extent. Be careful with this when loading large datasets! It is recommended to use this parameter instead of using ``filter_temporal()`` directly after loading unbounded data.","schema":[{"type":"array","subtype":"temporal-interval","minItems":2,"maxItems":2,"items":{"anyOf":[{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"},{"type":"string","subtype":"year","minLength":4,"maxLength":4,"pattern":"^\\\\d{4}$"},{"type":"null"}]},"examples":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"],["2015-01-01","2016-01-01"]]},{"title":"No filter","description":"Don\'t filter temporally. All data is included in the data cube.","type":"null"}]},{"name":"bands","description":"Only adds the specified bands into the data cube so that bands that don\'t match the list of band names are not available. Applies to all dimensions of type `bands`.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If the unique band name and the common name conflict, the unique band name has a higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.\\n\\nIt is recommended to use this parameter instead of using ``filter_bands()`` directly after loading unbounded data.","schema":[{"type":"array","items":{"type":"string","subtype":"band-name"}},{"title":"No filter","description":"Don\'t filter bands. All bands are included in the data cube.","type":"null"}],"default":null,"optional":true},{"name":"properties","description":"Limits the data by metadata properties to include only data in the data cube which all given conditions return `true` for (AND operation).\\n\\nSpecify key-value-pairs with the key being the name of the metadata property, which can be retrieved with the openEO Data Discovery for Collections. The value must a condition (user-defined process) to be evaluated against the collection metadata, see the example.","schema":[{"type":"object","subtype":"metadata-filter","title":"Filters","description":"A list of filters to check against. Specify key-value-pairs with the key being the name of the metadata property name and the value being a process evaluated against the metadata values.","additionalProperties":{"type":"object","subtype":"process-graph","parameters":[{"name":"value","description":"The property value to be checked against.","schema":{"description":"Any data type."}}],"returns":{"description":"`true` if the data should be loaded into the data cube, otherwise `false`.","schema":{"type":"boolean"}}}},{"title":"No filter","description":"Don\'t filter by metadata properties.","type":"null"}],"default":null,"optional":true}],"returns":{"description":"A data cube for further processing. The dimensions and dimension properties (name, type, labels, reference system and resolution) correspond to the collection\'s metadata, but the dimension labels are restricted as specified in the parameters.","schema":{"type":"object","subtype":"raster-cube"}},"examples":[{"description":"Loading `Sentinel-2B` data from a `Sentinel-2` collection for 2018, but only with cloud cover between 0 and 50%.","arguments":{"id":"Sentinel-2","spatial_extent":{"west":16.1,"east":16.6,"north":48.6,"south":47.2},"temporal_extent":["2018-01-01","2019-01-01"],"properties":{"eo:cloud_cover":{"process_graph":{"cc":{"process_id":"between","arguments":{"x":{"from_parameter":"value"},"min":0,"max":50},"result":true}}},"platform":{"process_graph":{"pf":{"process_id":"eq","arguments":{"x":{"from_parameter":"value"},"y":"Sentinel-2B","case_sensitive":false},"result":true}}}}}}],"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html","rel":"about","title":"Data Cubes explained in the openEO documentation"},{"rel":"about","href":"https://proj.org/usage/projections.html","title":"PROJ parameters for cartographic projections"},{"rel":"about","href":"http://www.epsg-registry.org","title":"Official EPSG code registry"},{"rel":"about","href":"http://www.epsg.io","title":"Unofficial EPSG code database"},{"href":"http://www.opengeospatial.org/standards/sfa","rel":"about","title":"Simple Features standard by the OGC"},{"rel":"about","href":"https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names","title":"List of common band names as specified by the STAC specification"}]},{"id":"log","summary":"Logarithm to a base","description":"Logarithm to the base `base` of the number `x` is defined to be the inverse function of taking b to the power of x.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, `log(0, 2)` results in ±infinity if the processing environment supports it or otherwise an exception is thrown.","categories":["math > exponential & logarithmic"],"parameters":[{"name":"x","description":"A number to compute the logarithm for.","schema":{"type":["number","null"]}},{"name":"base","description":"The numerical base.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed logarithm.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":10,"base":10},"returns":1},{"arguments":{"x":2,"base":2},"returns":1},{"arguments":{"x":4,"base":2},"returns":2},{"arguments":{"x":1,"base":16},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Logarithm.html","title":"Logarithm explained by Wolfram MathWorld"},{"rel":"about","href":"https://ieeexplore.ieee.org/document/8766229","title":"IEEE Standard 754-2019 for Floating-Point Arithmetic"}]},{"id":"lt","summary":"Less than comparison","description":"Compares whether `x` is strictly less than `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If any operand is an array or object, the return value is `false`.\\n* If any operand is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.","categories":["comparison"],"parameters":[{"name":"x","description":"First operand.","schema":{"description":"Any data type is allowed."}},{"name":"y","description":"Second operand.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if `x` is strictly less than `y`, `null` if any operand is `null`, otherwise `false`.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":1,"y":null},"returns":null},{"arguments":{"x":0,"y":0},"returns":false},{"arguments":{"x":1,"y":2},"returns":true},{"arguments":{"x":-0.5,"y":-0.6},"returns":false},{"arguments":{"x":"00:00:00+01:00","y":"00:00:00Z"},"returns":true},{"arguments":{"x":"1950-01-01T00:00:00Z","y":"2018-01-01T12:00:00Z"},"returns":true},{"arguments":{"x":"2018-01-01T12:00:00+00:00","y":"2018-01-01T12:00:00Z"},"returns":false},{"arguments":{"x":0,"y":true},"returns":false},{"arguments":{"x":false,"y":true},"returns":false}]},{"id":"lte","summary":"Less than or equal to comparison","description":"Compares whether `x` is less than or equal to `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`. Therefore, `lte(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* If the operands are not equal (see process ``eq()``) and any of them is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.","categories":["comparison"],"parameters":[{"name":"x","description":"First operand.","schema":{"description":"Any data type is allowed."}},{"name":"y","description":"Second operand.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if `x` is less than or equal to `y`, `null` if any operand is `null`, otherwise `false`.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":1,"y":null},"returns":null},{"arguments":{"x":0,"y":0},"returns":true},{"arguments":{"x":1,"y":2},"returns":true},{"arguments":{"x":-0.5,"y":-0.6},"returns":false},{"arguments":{"x":"00:00:00+01:00","y":"00:00:00Z"},"returns":true},{"arguments":{"x":"1950-01-01T00:00:00Z","y":"2018-01-01T12:00:00Z"},"returns":true},{"arguments":{"x":"2018-01-01T12:00:00+00:00","y":"2018-01-01T12:00:00Z"},"returns":true},{"arguments":{"x":false,"y":true},"returns":false},{"arguments":{"x":[1,2,3],"y":[1,2,3]},"returns":false}],"process_graph":{"eq":{"process_id":"eq","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"y"}}},"lt":{"process_id":"lt","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"y"}}},"or":{"process_id":"or","arguments":{"x":{"from_node":"lt"},"y":{"from_node":"eq"}},"result":true}}},{"id":"mask_polygon","summary":"Apply a polygon mask","description":"Applies a (multi) polygon mask to a raster data cube. To apply a raster mask use ``mask()``.\\n\\nAll pixels for which the point at the pixel center **does not** intersect with any polygon (as defined in the Simple Features standard by the OGC) are replaced. This behavior can be inverted by setting the parameter `inside` to `true`.\\n\\nThe pixel values are replaced with the value specified for `replacement`, which defaults to `null` (no data). No data values in `data` will be left untouched by the masking operation.","categories":["cubes","masks"],"parameters":[{"name":"data","description":"A raster data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"mask","description":"A GeoJSON object containing at least one polygon. The provided feature types can be one of the following:\\n\\n* A `Polygon` or `MultiPolygon` geometry,\\n* a `Feature` with a `Polygon` or `MultiPolygon` geometry,\\n* a `FeatureCollection` containing at least one `Feature` with `Polygon` or `MultiPolygon` geometries, or\\n* a `GeometryCollection` containing `Polygon` or `MultiPolygon` geometries. To maximize interoperability, `GeometryCollection` should be avoided in favour of one of the alternatives above.","schema":{"type":"object","subtype":"geojson"}},{"name":"replacement","description":"The value used to replace masked values with.","schema":[{"type":"number"},{"type":"boolean"},{"type":"string"},{"type":"null"}],"default":null,"optional":true},{"name":"inside","description":"If set to `true` all pixels for which the point at the pixel center **does** intersect with any polygon are replaced.","schema":{"type":"boolean"},"default":false,"optional":true}],"returns":{"description":"A masked raster data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"http://www.opengeospatial.org/standards/sfa","rel":"about","title":"Simple Features standard by the OGC"}]},{"id":"mask","summary":"Apply a raster mask","description":"Applies a mask to a raster data cube. To apply a vector mask use ``mask_polygon()``.\\n\\nA mask is a raster data cube for which corresponding pixels among `data` and `mask` are compared and those pixels in `data` are replaced whose pixels in `mask` are non-zero (for numbers) or `true` (for boolean values). The pixel values are replaced with the value specified for `replacement`, which defaults to `null` (no data).\\n\\nThe data cubes have to be compatible so that each dimension in the mask must also be available in the raster data cube with the same name, type, reference system, resolution and labels. Dimensions can be missing in the mask with the result that the mask is applied for each label of the missing dimension in the data cube. The process fails if there\'s an incompatibility found between the raster data cube and the mask.","categories":["cubes","masks"],"parameters":[{"name":"data","description":"A raster data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"mask","description":"A mask as a raster data cube. Every pixel in `data` must have a corresponding element in `mask`.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"replacement","description":"The value used to replace masked values with.","schema":{"type":["number","boolean","string","null"]},"default":null,"optional":true}],"returns":{"description":"A masked raster data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}}},{"id":"max","summary":"Maximum value","description":"Computes the largest value of an array of numbers, which is equal to the first element of a sorted (i.e., ordered) version of the array.\\n\\nAn array without non-`null` elements resolves always with `null`.","categories":["math","reducer"],"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"The maximum value.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"data":[1,0,3,2]},"returns":3},{"arguments":{"data":[5,2.5,null,-0.7]},"returns":5},{"arguments":{"data":[1,0,3,null,2],"ignore_nodata":false},"returns":null},{"description":"The input array is empty: return `null`.","arguments":{"data":[]},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Maximum.html","title":"Maximum explained by Wolfram MathWorld"}]},{"id":"mean","summary":"Arithmetic mean (average)","description":"The arithmetic mean of an array of numbers is the quantity commonly called the average. It is defined as the sum of all elements divided by the number of elements.\\n\\nAn array without non-`null` elements resolves always with `null`.","categories":["math","reducer"],"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"The computed arithmetic mean.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"data":[1,0,3,2]},"returns":1.5},{"arguments":{"data":[9,2.5,null,-2.5]},"returns":3},{"arguments":{"data":[1,null],"ignore_nodata":false},"returns":null},{"description":"The input array is empty: return `null`.","arguments":{"data":[]},"returns":null},{"description":"The input array has only `null` elements: return `null`.","arguments":{"data":[null,null]},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/ArithmeticMean.html","title":"Arithmetic mean explained by Wolfram MathWorld"}],"process_graph":{"count_condition":{"process_id":"if","arguments":{"value":{"from_parameter":"ignore_nodata"},"accept":null,"reject":true}},"count":{"process_id":"count","arguments":{"data":{"from_parameter":"data"},"condition":{"from_node":"count_condition"}}},"sum":{"process_id":"sum","arguments":{"data":{"from_parameter":"data"},"ignore_nodata":{"from_parameter":"ignore_nodata"}}},"divide":{"process_id":"divide","arguments":{"x":{"from_node":"sum"},"y":{"from_node":"count"}}},"neq":{"process_id":"neq","arguments":{"x":{"from_node":"count"},"y":0}},"if":{"process_id":"if","arguments":{"value":{"from_node":"neq"},"accept":{"from_node":"divide"}},"result":true}}},{"id":"median","summary":"Statistical median","description":"The statistical median of an array of numbers is the value separating the higher half from the lower half of the data.\\n\\nAn array without non-`null` elements resolves always with `null`.\\n\\n**Remarks:**\\n\\n* For symmetric arrays, the result is equal to the ``mean()``.\\n* The median can also be calculated by computing the ``quantiles()`` with a probability of *0.5*.","categories":["math","reducer"],"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"The computed statistical median.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"data":[1,3,3,6,7,8,9]},"returns":6},{"arguments":{"data":[1,2,3,4,5,6,8,9]},"returns":4.5},{"arguments":{"data":[-1,-0.5,null,1]},"returns":-0.5},{"arguments":{"data":[-1,0,null,1],"ignore_nodata":false},"returns":null},{"description":"The input array is empty: return `null`.","arguments":{"data":[]},"returns":null},{"description":"The input array has only `null` elements: return `null`.","arguments":{"data":[null,null]},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/StatisticalMedian.html","title":"Statistical Median explained by Wolfram MathWorld"}],"process_graph":{"quantiles":{"process_id":"quantiles","arguments":{"data":{"from_parameter":"data"},"probabilities":[0.5],"ignore_nodata":{"from_parameter":"ignore_nodata"}}},"array_element":{"process_id":"array_element","arguments":{"data":{"from_node":"quantiles"},"return_nodata":true,"index":0},"result":true}}},{"id":"merge_cubes","summary":"Merge two data cubes","description":"The data cubes have to be compatible. A merge operation without overlap should be reversible with (a set of) filter operations for each of the two cubes. The process performs the join on overlapping dimensions, with the same name and type.\\n\\nAn overlapping dimension has the same name, type, reference system and resolution in both dimensions, but can have different labels. One of the dimensions can have different labels, for all other dimensions the labels must be equal. If data overlaps, the parameter `overlap_resolver` must be specified to resolve the overlap.\\n\\n**Examples for merging two data cubes:**\\n\\n1. Data cubes with the dimensions (`x`, `y`, `t`, `bands`) have the same dimension labels in `x`, `y` and `t`, but the labels for the dimension `bands` are `B1` and `B2` for the first cube and `B3` and `B4`. An overlap resolver is *not needed*. The merged data cube has the dimensions `x`, `y`, `t` and `bands` and the dimension `bands` has four dimension labels: `B1`, `B2`, `B3`, `B4`.\\n2. Data cubes with the dimensions (`x`, `y`, `t`, `bands`) have the same dimension labels in `x`, `y` and `t`, but the labels for the dimension `bands` are `B1` and `B2` for the first data cube and `B2` and `B3` for the second. An overlap resolver is *required* to resolve overlap in band `B2`. The merged data cube has the dimensions `x`, `y`, `t` and `bands` and the dimension `bands` has three dimension labels: `B1`, `B2`, `B3`.\\n3. Data cubes with the dimensions (`x`, `y`, `t`) have the same dimension labels in `x`, `y` and `t`. There are two options:\\n 1. Keep the overlapping values separately in the merged data cube: An overlap resolver is *not needed*, but for each data cube you need to add a new dimension using ``add_dimension()``. The new dimensions must be equal, except that the labels for the new dimensions must differ by name. The merged data cube has the same dimensions and labels as the original data cubes, plus the dimension added with ``add_dimension()``, which has the two dimension labels after the merge.\\n 2. Combine the overlapping values into a single value: An overlap resolver is *required* to resolve the overlap for all pixels. The merged data cube has the same dimensions and labels as the original data cubes, but all pixel values have been processed by the overlap resolver.\\n4. A data cube with dimensions (`x`, `y`, `t` / `bands`) or (`x`, `y`, `t`, `bands`) and another data cube with dimensions (`x`, `y`) have the same dimension labels in `x` and `y`. Merging them will join dimensions `x` and `y`, so the lower dimension cube is merged with each time step and band available in the higher dimensional cube. This can for instance be used to apply a digital elevation model to a spatio-temporal data cube. An overlap resolver is *required* to resolve the overlap for all pixels.\\n\\nAfter the merge, the dimensions with a natural/inherent label order (with a reference system this is each spatial and temporal dimensions) still have all dimension labels sorted. For other dimensions where there is no inherent order, including bands, the dimension labels keep the order in which they are present in the original data cubes and the dimension labels of `cube2` are appended to the dimension labels of `cube1`.","categories":["cubes"],"parameters":[{"name":"cube1","description":"The first data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"cube2","description":"The second data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"overlap_resolver","description":"A reduction operator that resolves the conflict if the data overlaps. The reducer must return a value of the same data type as the input values are. The reduction operator may be a single process such as ``multiply()`` or consist of multiple sub-processes. `null` (the default) can be specified if no overlap resolver is required.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"x","description":"The overlapping value from the first data cube `cube1`.","schema":{"description":"Any data type."}},{"name":"y","description":"The overlapping value from the second data cube `cube2`.","schema":{"description":"Any data type."}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the merged data cube.","schema":{"description":"Any data type."}}},"default":null,"optional":true},{"name":"context","description":"Additional data to be passed to the overlap resolver.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The merged data cube. See the process description for details regarding the dimensions and dimension properties (name, type, labels, reference system and resolution).","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"OverlapResolverMissing":{"message":"Overlapping data cubes, but no overlap resolver has been specified."}},"links":[{"rel":"about","href":"https://en.wikipedia.org/wiki/Reduction_Operator","title":"Background information on reduction operators (binary reducers) by Wikipedia"}]},{"id":"min","summary":"Minimum value","description":"Computes the smallest value of an array of numbers, which is equal to the last element of a sorted (i.e., ordered) version of the array.\\n\\nAn array without non-`null` elements resolves always with `null`.","categories":["math","reducer"],"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"The minimum value.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"data":[1,0,3,2]},"returns":0},{"arguments":{"data":[5,2.5,null,-0.7]},"returns":-0.7},{"arguments":{"data":[1,0,3,null,2],"ignore_nodata":false},"returns":null},{"arguments":{"data":[]},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Minimum.html","title":"Minimum explained by Wolfram MathWorld"}]},{"id":"mod","summary":"Modulo","description":"Remainder after a division of `x` by `y` for both integers and floating-point numbers.\\n\\nThe result of a modulo operation has the sign of the divisor. The handling regarding the sign of the result [differs between programming languages](https://en.wikipedia.org/wiki/Modulo_operation#In_programming_languages) and needs careful consideration to avoid unexpected results.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`. A modulo by zero results in ±infinity if the processing environment supports it. Otherwise, a `DivisionByZero` exception must the thrown.","categories":["math"],"parameters":[{"name":"x","description":"A number to be used as the dividend.","schema":{"type":["number","null"]}},{"name":"y","description":"A number to be used as the divisor.","schema":{"type":["number","null"]}}],"returns":{"description":"The remainder after division.","schema":{"type":["number","null"]}},"exceptions":{"DivisionByZero":{"message":"Division by zero is not supported."}},"examples":[{"arguments":{"x":27,"y":5},"returns":2},{"arguments":{"x":-27,"y":5},"returns":3},{"arguments":{"x":3.14,"y":-2},"returns":-0.86},{"arguments":{"x":-27,"y":-5},"returns":-2},{"arguments":{"x":27,"y":null},"returns":null},{"arguments":{"x":null,"y":5},"returns":null}],"links":[{"rel":"about","href":"https://en.wikipedia.org/wiki/Modulo_operation","title":"Modulo explained by Wikipedia"}]},{"id":"multiply","summary":"Multiplication of two numbers","description":"Multiplies the two numbers `x` and `y` (*`x * y`*) and returns the computed product.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.","categories":["math"],"parameters":[{"name":"x","description":"The multiplier.","schema":{"type":["number","null"]}},{"name":"y","description":"The multiplicand.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed product of the two numbers.","schema":{"type":["number","null"]}},"exceptions":{"MultiplicandMissing":{"message":"Multiplication requires at least two numbers."}},"examples":[{"arguments":{"x":5,"y":2.5},"returns":12.5},{"arguments":{"x":-2,"y":-4},"returns":8},{"arguments":{"x":1,"y":null},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Product.html","title":"Product explained by Wolfram MathWorld"},{"rel":"about","href":"https://ieeexplore.ieee.org/document/8766229","title":"IEEE Standard 754-2019 for Floating-Point Arithmetic"}],"process_graph":{"product":{"process_id":"product","arguments":{"data":[{"from_parameter":"x"},{"from_parameter":"y"}],"ignore_nodata":false},"result":true}}},{"id":"ndvi","summary":"Normalized Difference Vegetation Index","description":"Computes the Normalized Difference Vegetation Index (NDVI). The NDVI is computed as *`(nir - red) / (nir + red)`*.\\n\\nThe `data` parameter expects a raster data cube with a dimension of type `bands` or a `DimensionAmbiguous` exception is thrown otherwise. By default, the dimension must have at least two bands with the common names `red` and `nir` assigned. Otherwise, the user has to specify the parameters `nir` and `red`. If neither is the case, either the exception `NirBandAmbiguous` or `RedBandAmbiguous` is thrown. The common names for each band are specified in the collection\'s band metadata and are *not* equal to the band names.\\n\\nBy default, the dimension of type `bands` is dropped by this process. To keep the dimension specify a new band name in the parameter `target_band`. This adds a new dimension label with the specified name to the dimension, which can be used to access the computed values. If a band with the specified name exists, a `BandExists` is thrown.\\n\\nThis process is very similar to the process ``normalized_difference()``, but determines the bands automatically based on the common names (`red`/`nir`) specified in the metadata.","categories":["math > indices","vegetation indices"],"parameters":[{"name":"data","description":"A raster data cube with two bands that have the common names `red` and `nir` assigned.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"nir","description":"The name of the NIR band. Defaults to the band that has the common name `nir` assigned.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If the unique band name and the common name conflict, the unique band name has a higher priority.","schema":{"type":"string","subtype":"band-name"},"default":"nir","optional":true},{"name":"red","description":"The name of the red band. Defaults to the band that has the common name `red` assigned.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If the unique band name and the common name conflict, the unique band name has a higher priority.","schema":{"type":"string","subtype":"band-name"},"default":"red","optional":true},{"name":"target_band","description":"By default, the dimension of type `bands` is dropped. To keep the dimension specify a new band name in this parameter so that a new dimension label with the specified name will be added for the computed values.","schema":[{"type":"string","pattern":"^\\\\w+$"},{"type":"null"}],"default":null,"optional":true}],"returns":{"description":"A raster data cube containing the computed NDVI values. The structure of the data cube differs depending on the value passed to `target_band`:\\n\\n* `target_band` is `null`: The data cube does not contain the dimension of type `bands`, the number of dimensions decreases by one. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.\\n* `target_band` is a string: The data cube keeps the same dimensions. The dimension properties remain unchanged, but the number of dimension labels for the dimension of type `bands` increases by one. The additional label is named as specified in `target_band`.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"NirBandAmbiguous":{"message":"The NIR band can\'t be resolved, please specify the specific NIR band name."},"RedBandAmbiguous":{"message":"The red band can\'t be resolved, please specify the specific red band name."},"DimensionAmbiguous":{"message":"dimension of type `bands` is not available or is ambiguous.."},"BandExists":{"message":"A band with the specified target name exists."}},"links":[{"rel":"about","href":"https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index","title":"NDVI explained by Wikipedia"},{"rel":"about","href":"https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php","title":"NDVI explained by NASA"},{"rel":"about","href":"https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names","title":"List of common band names as specified by the STAC specification"}]},{"id":"neq","summary":"Not equal to comparison","description":"Compares whether `x` is *not* strictly equal to `y`.\\n\\n**Remarks:**\\n\\n* Data types MUST be checked strictly. For example, a string with the content *1* is not equal to the number *1*. Nevertheless, an integer *1* is equal to a floating-point number *1.0* as `integer` is a sub-type of `number`.\\n* If any operand is `null`, the return value is `null`. Therefore, `neq(null, null)` returns `null` instead of `false`.\\n* If any operand is an array or object, the return value is `false`.\\n* Strings are expected to be encoded in UTF-8 by default.\\n* Temporal strings MUST be compared differently than other strings and MUST NOT be compared based on their string representation due to different possible representations. For example, the time zone representation `Z` (for UTC) has the same meaning as `+00:00`.","categories":["texts","comparison"],"parameters":[{"name":"x","description":"First operand.","schema":{"description":"Any data type is allowed."}},{"name":"y","description":"Second operand.","schema":{"description":"Any data type is allowed."}},{"name":"delta","description":"Only applicable for comparing two numbers. If this optional parameter is set to a positive non-zero number the non-equality of two numbers is checked against a delta value. This is especially useful to circumvent problems with floating-point inaccuracy in machine-based computation.\\n\\nThis option is basically an alias for the following computation: `gt(abs(minus([x, y]), delta)`","schema":{"type":["number","null"]},"default":null,"optional":true},{"name":"case_sensitive","description":"Only applicable for comparing two strings. Case sensitive comparison can be disabled by setting this parameter to `false`.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"`true` if `x` is *not* equal to `y`, `null` if any operand is `null`, otherwise `false`.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":1,"y":null},"returns":null},{"arguments":{"x":1,"y":1},"returns":false},{"arguments":{"x":1,"y":"1"},"returns":true},{"arguments":{"x":0,"y":false},"returns":true},{"arguments":{"x":1.02,"y":1,"delta":0.01},"returns":true},{"arguments":{"x":-1,"y":-1.001,"delta":0.01},"returns":false},{"arguments":{"x":115,"y":110,"delta":10},"returns":false},{"arguments":{"x":"Test","y":"test"},"returns":true},{"arguments":{"x":"Test","y":"test","case_sensitive":false},"returns":false},{"arguments":{"x":"Ä","y":"ä","case_sensitive":false},"returns":false},{"arguments":{"x":"00:00:00+00:00","y":"00:00:00Z"},"returns":false},{"description":"`y` is not a valid date-time representation and therefore will be treated as a string so that the provided values are not equal.","arguments":{"x":"2018-01-01T12:00:00Z","y":"2018-01-01T12:00:00"},"returns":true},{"description":"01:00 in the time zone +1 is equal to 00:00 in UTC.","arguments":{"x":"2018-01-01T00:00:00Z","y":"2018-01-01T01:00:00+01:00"},"returns":false},{"arguments":{"x":[1,2,3],"y":[1,2,3]},"returns":false}],"process_graph":{"eq":{"process_id":"eq","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"y"},"delta":{"from_parameter":"delta"},"case_sensitive":{"from_parameter":"case_sensitive"}}},"not":{"process_id":"not","arguments":{"x":{"from_node":"eq"}},"result":true}}},{"id":"normalized_difference","summary":"Normalized difference","description":"Computes the normalized difference for two bands. The normalized difference is computed as *`(x - y) / (x + y)`*.\\n\\nThis process could be used for a number of remote sensing indices such as:\\n\\n* [NDVI](https://eos.com/ndvi/): `x` = NIR band, `y` = red band\\n* [NDWI](https://eos.com/ndwi/): `x` = NIR band, `y` = SWIR band\\n* [NDSI](https://eos.com/ndsi/): `x` = green band, `y` = SWIR band\\n\\nSome back-ends may have native processes such as ``ndvi()`` available for convenience.","categories":["math > indices","vegetation indices"],"parameters":[{"name":"x","description":"The value for the first band.","schema":{"type":"number"}},{"name":"y","description":"The value for the second band.","schema":{"type":"number"}}],"returns":{"description":"The computed normalized difference.","schema":{"type":"number","minimum":-1,"maximum":1}},"links":[{"rel":"related","href":"https://eos.com/ndvi/","title":"NDVI explained by EOS"},{"rel":"related","href":"https://eos.com/ndwi/","title":"NDWI explained by EOS"},{"rel":"related","href":"https://eos.com/ndsi/","title":"NDSI explained by EOS"}],"process_graph":{"subtract":{"process_id":"subtract","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"y"}}},"add":{"process_id":"add","arguments":{"x":{"from_parameter":"x"},"y":{"from_parameter":"y"}}},"divide":{"process_id":"divide","arguments":{"x":{"from_node":"subtract"},"y":{"from_node":"add"}},"result":true}}},{"id":"not","summary":"Inverting a boolean","description":"Inverts a single boolean so that `true` gets `false` and `false` gets `true`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["logic"],"parameters":[{"name":"x","description":"Boolean value to invert.","schema":{"type":["boolean","null"]}}],"returns":{"description":"Inverted boolean value.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":null},"returns":null},{"arguments":{"x":false},"returns":true},{"arguments":{"x":true},"returns":false}]},{"id":"or","summary":"Logical OR","description":"Checks if **at least one** of the values is true. Evaluates parameter `x` before `y` and stops once the outcome is unambiguous. If a component is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ---- | ----- | ----\\nnull || null | null | true\\nfalse || null | false | true\\ntrue || true | true | true\\n```","categories":["logic"],"parameters":[{"name":"x","description":"A boolean value.","schema":{"type":["boolean","null"]}},{"name":"y","description":"A boolean value.","schema":{"type":["boolean","null"]}}],"returns":{"description":"Boolean result of the logical OR.","schema":{"type":["boolean","null"]}},"examples":[{"arguments":{"x":true,"y":true},"returns":true},{"arguments":{"x":false,"y":false},"returns":false},{"arguments":{"x":true,"y":null},"returns":true},{"arguments":{"x":null,"y":true},"returns":true},{"arguments":{"x":false,"y":null},"returns":null}],"process_graph":{"any":{"process_id":"any","arguments":{"data":[{"from_parameter":"x"},{"from_parameter":"y"}],"ignore_nodata":false},"result":true}}},{"id":"order","summary":"Create a permutation","description":"Computes a permutation which allows rearranging the data into ascending or descending order. In other words, this process computes the ranked (sorted) element positions in the original list.\\n\\n**Remarks:**\\n\\n* The positions in the result are zero-based.\\n* Ties will be left in their original ordering.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone/time-offset representations.","categories":["arrays","sorting"],"parameters":[{"name":"data","description":"An array to compute the order for.","schema":{"type":"array","items":{"anyOf":[{"type":"number"},{"type":"null"},{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"},{"type":"string","format":"time","subtype":"time"}]}}},{"name":"asc","description":"The default sort order is ascending, with smallest values first. To sort in reverse (descending) order, set this parameter to `false`.","schema":{"type":"boolean"},"default":true,"optional":true},{"name":"nodata","description":"Controls the handling of no-data values (`null`). By default, they are removed. If set to `true`, missing values in the data are put last; if set to `false`, they are put first.","schema":{"type":["boolean","null"]},"default":null,"optional":true}],"returns":{"description":"The computed permutation.","schema":{"type":"array","items":{"type":"integer","minimum":0}}},"examples":[{"arguments":{"data":[6,-1,2,null,7,4,null,8,3,9,9]},"returns":[1,2,8,5,0,4,7,9,10]},{"arguments":{"data":[6,-1,2,null,7,4,null,8,3,9,9],"nodata":true},"returns":[1,2,8,5,0,4,7,9,10,3,6]},{"arguments":{"data":[6,-1,2,null,7,4,null,8,3,9,9],"asc":false,"nodata":true},"returns":[9,10,7,4,0,5,8,2,1,3,6]},{"arguments":{"data":[6,-1,2,null,7,4,null,8,3,9,9],"asc":false,"nodata":false},"returns":[3,6,9,10,7,4,0,5,8,2,1]}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Permutation.html","title":"Permutation explained by Wolfram MathWorld"}]},{"id":"pi","summary":"Pi (π)","description":"The real number Pi (π) is a mathematical constant that is the ratio of the circumference of a circle to its diameter. The numerical value is approximately *3.14159*.","categories":["math > constants","math > trigonometric"],"parameters":[],"returns":{"description":"The numerical value of Pi.","schema":{"type":"number"}},"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Pi.html","title":"Mathematical constant Pi explained by Wolfram MathWorld"}]},{"id":"power","summary":"Exponentiation","description":"Computes the exponentiation for the base `base` raised to the power of `p`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.","categories":["math","math > exponential & logarithmic"],"parameters":[{"name":"base","description":"The numerical base.","schema":{"type":["number","null"]}},{"name":"p","description":"The numerical exponent.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed value for `base` raised to the power of `p`.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"base":0,"p":2},"returns":0},{"arguments":{"base":2.5,"p":0},"returns":1},{"arguments":{"base":3,"p":3},"returns":27},{"arguments":{"base":5,"p":-1},"returns":0.2},{"arguments":{"base":1,"p":0.5},"returns":1},{"arguments":{"base":1,"p":null},"returns":null},{"arguments":{"base":null,"p":2},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Power.html","title":"Power explained by Wolfram MathWorld"}]},{"id":"product","summary":"Compute the product by multiplying numbers","description":"Multiplies all elements in a sequential array of numbers and returns the computed product.\\n\\nBy default no-data values are ignored. Setting `ignore_nodata` to `false` considers no-data values so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.","categories":["math","reducer"],"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"The computed product of the sequence of numbers.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"data":[5,0]},"returns":0},{"arguments":{"data":[-2,4,2.5]},"returns":-20},{"arguments":{"data":[1,null],"ignore_nodata":false},"returns":null},{"arguments":{"data":[-1]},"returns":-1},{"arguments":{"data":[null],"ignore_nodata":false},"returns":null},{"arguments":{"data":[]},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Product.html","title":"Product explained by Wolfram MathWorld"},{"rel":"about","href":"https://ieeexplore.ieee.org/document/8766229","title":"IEEE Standard 754-2019 for Floating-Point Arithmetic"}]},{"id":"aggregate_spatial_window","summary":"Zonal statistics for rectangular windows","description":"Aggregates statistics over the horizontal spatial dimensions (axes `x` and `y`) of the data cube.\\n\\nThe pixel grid for the axes `x` and `y` is divided into non-overlapping windows with the size specified in the parameter `size`. If the number of values for the axes `x` and `y` is not a multiple of the corresponding window size, the behavior specified in the parameters `boundary` and `align` is applied.\\nFor each of these windows, the reducer process computes the result.","categories":["cubes","aggregate & resample"],"experimental":true,"parameters":[{"name":"data","description":"A raster data cube with exactly two horizontal spatial dimensions and an arbitrary number of additional dimensions. The process is applied to all additional dimensions individually.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"reducer","description":"A reducer to be applied on the list of values, which contain all pixels covered by the window. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category \'reducer\' for such processes.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"data","description":"An array with elements of any type.","schema":{"type":"array","items":{"description":"Any data type."}}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the new data cube.","schema":{"description":"Any data type."}}}},{"name":"size","description":"Window size in pixels along the horizontal spatial dimensions.\\n\\nThe first value corresponds to the `x` axis, the second value corresponds to the `y` axis.","schema":{"type":"array","minItems":2,"maxItems":2,"items":{"type":"integer","minimum":1}}},{"name":"boundary","description":"Behavior to apply if the number of values for the axes `x` and `y` is not a multiple of the corresponding value in the `size` parameter. Options are:\\n\\n- `pad` (default): pad the data cube with the no-data value `null` to fit the required window size.\\n\\n- `trim`: trim the data cube to fit the required window size.\\n\\nSet the parameter `align` to specifies to which corner the data is aligned to.","schema":{"type":"string","enum":["pad","trim"]},"optional":true,"default":"pad"},{"name":"align","description":"If the data requires padding or trimming (see parameter `boundary`), specifies to which corner of the spatial extent the data is aligned to. For example, if the data is aligned to the upper left, the process pads/trims at the lower-right.","schema":{"type":"string","enum":["lower-left","upper-left","lower-right","upper-right"]},"default":"upper-left","optional":true},{"name":"context","description":"Additional data to be passed to the reducer.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A data cube with the newly computed values and the same dimensions.\\n\\nThe resolution will change depending on the chosen values for the `size` and `boundary` parameter. It usually decreases for the dimensions which have the corresponding parameter `size` set to values greater than 1.\\n\\nThe dimension labels will be set to the coordinate at the center of the window. The other dimension properties (name, type and reference system) remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#aggregate","rel":"about","title":"Aggregation explained in the openEO documentation"}]},{"id":"ard_normalized_radar_backscatter","summary":"CARD4L compliant SAR NRB generation","description":"Computes CARD4L compliant backscatter from SAR input. The radiometric correction coefficient is gamma0 (terrain), which is the ground area computed with terrain earth model in sensor line of sight.\\n\\nNote that backscatter computation may require instrument specific metadata that is tightly coupled to the original SAR products. As a result, this process may only work in combination with loading data from specific collections, not with general data cubes.\\n\\nThis process uses bilinear interpolation, both for resampling the DEM and the backscatter.","categories":["cubes","sar","ard"],"experimental":true,"parameters":[{"name":"data","description":"The source data cube containing SAR input.","schema":{"subtype":"raster-cube","type":"object"}},{"name":"elevation_model","description":"The digital elevation model to use. Set to `null` (the default) to allow the back-end to choose, which will improve portability, but reduce reproducibility.","optional":true,"default":null,"schema":[{"type":"string","subtype":"collection-id"},{"type":"null"}]},{"name":"contributing_area","description":"If set to `true`, a DEM-based local contributing area band named `contributing_area` is added. The values are given in square meters.","optional":true,"default":false,"schema":{"type":"boolean"}},{"name":"ellipsoid_incidence_angle","description":"If set to `true`, an ellipsoidal incidence angle band named `ellipsoid_incidence_angle` is added. The values are given in degrees.","optional":true,"default":false,"schema":{"type":"boolean"}},{"name":"noise_removal","description":"If set to `false`, no noise removal is applied. Defaults to `true`, which removes noise.","optional":true,"default":true,"schema":{"type":"boolean"}}],"returns":{"description":"Backscatter values expressed as gamma0 in linear scale.\\n\\nIn addition to the bands `contributing_area` and `ellipsoid_incidence_angle` that can optionally be added with corresponding parameters, the following bands are always added to the data cube:\\n\\n- `mask`: A data mask that indicates which values are valid (1), invalid (0) or contain no-data (null).\\n- `local_incidence_angle`: A band with DEM-based local incidence angles in degrees.\\n\\nThe data returned is CARD4L compliant with corresponding metadata.","schema":{"subtype":"raster-cube","type":"object"}},"exceptions":{"DigitalElevationModelInvalid":{"message":"The digital elevation model specified is either not a DEM or can\'t be used with the data cube given."}},"links":[{"rel":"about","href":"http://ceos.org/ard/files/PFS/NRB/v5.0/CARD4L-PFS_Normalised_Radar_Backscatter-v5.0.pdf","title":"CEOS CARD4L specification"},{"rel":"about","href":"https://bok.eo4geo.eu/PP2-2-4-3","title":"Gamma nought (0) explained by EO4GEO body of knowledge."},{"rel":"about","href":"https://doi.org/10.3390/data4030093","title":"Reasoning behind the choice of bilinear resampling"}],"process_graph":{"nrb":{"process_id":"sar_backscatter","arguments":{"data":{"from_parameter":"data"},"coefficient":"gamma0-terrain","elevation_model":{"from_parameter":"elevation_model"},"mask":true,"contributing_area":{"from_parameter":"contributing_area"},"local_incidence_angle":true,"ellipsoid_incidence_angle":{"from_parameter":"ellipsoid_incidence_angle"},"noise_removal":{"from_parameter":"noise_removal"}},"result":true}}},{"id":"ard_surface_reflectance","summary":"CARD4L compliant Surface Reflectance generation","description":"Computes CARD4L compliant surface (bottom of atmosphere/top of canopy) reflectance values from optical input.","categories":["cubes","sar","ard"],"experimental":true,"parameters":[{"description":"The source data cube containing multi-spectral optical top of the atmosphere (TOA) reflectances. There must be a single dimension of type `bands` available.","name":"data","schema":{"subtype":"raster-cube","type":"object"}},{"description":"The atmospheric correction method to use.","name":"atmospheric_correction_method","schema":{"type":["string"],"enum":["FORCE","iCOR"]}},{"description":"The cloud detection method to use.\\n\\nEach method supports detecting different atmospheric disturbances such as clouds, cloud shadows, aerosols, haze, ozone and/or water vapour in optical imagery.","name":"cloud_detection_method","schema":{"type":["string"],"enum":["Fmask","s2cloudless","Sen2Cor"]}},{"description":"The digital elevation model to use. Set to `null` (the default) to allow the back-end to choose, which will improve portability, but reduce reproducibility.","name":"elevation_model","optional":true,"default":null,"schema":[{"type":"string","subtype":"collection-id"},{"type":"null"}]},{"description":"Proprietary options for the atmospheric correction method. Specifying proprietary options will reduce portability.","name":"atmospheric_correction_options","optional":true,"default":{},"schema":{"type":"object","additionalProperties":false}},{"description":"Proprietary options for the cloud detection method. Specifying proprietary options will reduce portability.","name":"cloud_detection_options","optional":true,"default":{},"schema":{"type":"object","additionalProperties":false}}],"returns":{"description":"Data cube containing bottom of atmosphere reflectances for each spectral band in the source data cube, with atmospheric disturbances like clouds and cloud shadows removed. No-data values (null) are directly set in the bands. Depending on the methods used, several additional bands will be added to the data cube:\\n\\nData cube containing bottom of atmosphere reflectances for each spectral band in the source data cube, with atmospheric disturbances like clouds and cloud shadows removed. Depending on the methods used, several additional bands will be added to the data cube:\\n\\n- `date` (optional): Specifies per-pixel acquisition timestamps.\\n- `incomplete-testing` (required): Identifies pixels with a value of 1 for which the per-pixel tests (at least saturation, cloud and cloud shadows, see CARD4L specification for details) have not all been successfully completed. Otherwise, the value is 0.\\n- `saturation` (required) / `saturation_{band}` (optional): Indicates where pixels in the input spectral bands are saturated (1) or not (0). If the saturation is given per band, the band names are `saturation_{band}` with `{band}` being the band name from the source data cube.\\n- `cloud`, `shadow` (both required),`aerosol`, `haze`, `ozone`, `water_vapor` (all optional): Indicates the probability of pixels being an atmospheric disturbance such as clouds. All bands have values between 0 (clear) and 1, which describes the probability that it is an atmospheric disturbance.\\n- `snow-ice` (optional): Points to a file that indicates whether a pixel is assessed as being snow/ice (1) or not (0). All values describe the probability and must be between 0 and 1.\\n- `land-water` (optional): Indicates whether a pixel is assessed as being land (1) or water (0). All values describe the probability and must be between 0 and 1.\\n- `incidence-angle` (optional): Specifies per-pixel incidence angles in degrees.\\n- `azimuth` (optional): Specifies per-pixel azimuth angles in degrees.\\n- `sun-azimuth:` (optional): Specifies per-pixel sun azimuth angles in degrees.\\n- `sun-elevation` (optional): Specifies per-pixel sun elevation angles in degrees.\\n- `terrain-shadow` (optional): Indicates with a value of 1 whether a pixel is not directly illuminated due to terrain shadowing. Otherwise, the value is 0.\\n- `terrain-occlusion` (optional): Indicates with a value of 1 whether a pixel is not visible to the sensor due to terrain occlusion during off-nadir viewing. Otherwise, the value is 0.\\n- `terrain-illumination` (optional): Contains coefficients used for terrain illumination correction are provided for each pixel.\\n\\nThe data returned is CARD4L compliant with corresponding metadata.","schema":{"subtype":"raster-cube","type":"object"}},"links":[{"rel":"about","href":"http://ceos.org/ard/files/PFS/SR/v5.0/CARD4L_Product_Family_Specification_Surface_Reflectance-v5.0.pdf","title":"CEOS CARD4L specification"}]},{"id":"array_append","summary":"Append a value to an array","description":"Appends a value to the end of the array. Array labels get discarded from the array.","categories":["arrays"],"experimental":true,"parameters":[{"name":"data","description":"An array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"value","description":"Value to append to the array.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"The new array with the value being appended.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},"examples":[{"arguments":{"data":[1,2],"value":3},"returns":[1,2,3]}],"process_graph":{"append":{"process_id":"array_concat","arguments":{"array1":{"from_parameter":"data"},"array2":[{"from_parameter":"value"}]},"result":true}}},{"id":"array_concat","summary":"Merge two arrays","description":"Concatenates two arrays into a single array by appending the second array to the first array. Array labels get discarded from both arrays before merging.","categories":["arrays"],"experimental":true,"parameters":[{"name":"array1","description":"The first array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"array2","description":"The second array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}}],"returns":{"description":"The merged array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},"examples":[{"description":"Concatenates two arrays containing different data type.","arguments":{"array1":["a","b"],"array2":[1,2]},"returns":["a","b",1,2]}]},{"id":"array_create_labeled","summary":"Create a labeled array","description":"Creates a new labeled array by using the values from the `labels` array as labels and the values from the `data` array as the corresponding values.\\n\\nThe exception `ArrayLengthMismatch` is thrown, if the number of the values in the given arrays don\'t match exactly.\\n\\nThe primary use case here is to be able to transmit labeled arrays from the client to the server as JSON doesn\'t support this data type.","categories":["arrays"],"experimental":true,"parameters":[{"name":"data","description":"An array of values to be used.","schema":{"description":"Any data type is allowed."}},{"name":"labels","description":"An array of labels to be used.","schema":{"type":"array","items":{"type":["number","string"]}}}],"returns":{"description":"The newly created labeled array.","schema":{"type":"array","subtype":"labeled-array","items":{"description":"Any data type is allowed."}}},"exceptions":{"ArrayLengthMismatch":{"message":"The number of values in the parameters `data` and `labels` don\'t match."}}},{"id":"array_create","summary":"Create an array","description":"Creates a new array, which by default is empty.\\n\\nThe second parameter `repeat` allows to add the given array multiple times to the new array.\\n\\nIn most cases you can simply pass a (native) array to processes directly, but this process is especially useful to create a new array that is getting returned by a child process, for example in ``apply_dimension()``.","categories":["arrays"],"experimental":true,"parameters":[{"name":"data","description":"A (native) array to fill the newly created array with. Defaults to an empty array.","optional":true,"default":[],"schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"repeat","description":"The number of times the (native) array specified in `data` is repeatedly added after each other to the new array being created. Defaults to `1`.","optional":true,"default":1,"schema":{"type":"integer","minimum":1}}],"returns":{"description":"The newly created array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},"examples":[{"arguments":{},"returns":[]},{"arguments":{"data":["this","is","a","test"]},"returns":["this","is","a","test"]},{"arguments":{"data":[null],"repeat":3},"returns":[null,null,null]},{"arguments":{"data":[1,2,3],"repeat":2},"returns":[1,2,3,1,2,3]}]},{"id":"array_find_label","summary":"Get the index for a label in a labeled array","description":"Checks whether the labeled array specified for `data` has the label specified in `label` and returns the zero-based index for it. If there\'s no match as either the label doesn\'t exist or the array is not labeled, `null` is returned.","categories":["arrays","reducer"],"experimental":true,"parameters":[{"name":"data","description":"List to find the label in.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"label","description":"Label to find in `data`.","schema":[{"type":"number"},{"type":"string"}]}],"returns":{"description":"The index of the element with the specified label assigned. If no such label was found, `null` is returned.","schema":[{"type":"null"},{"type":"integer","minimum":0}]}},{"id":"array_interpolate_linear","summary":"One-dimensional linear interpolation for arrays","description":"Performs a linear interpolation for each of the no-data values (`null`) in the array given, except for leading and trailing no-data values.\\n\\nThe linear interpolants are defined by the array indices or labels (x) and the values in the array (y).","categories":["arrays","math","math > interpolation"],"experimental":true,"parameters":[{"name":"data","description":"An array of numbers and no-data values.\\n\\nIf the given array is a labeled array, the labels must have a natural/inherent label order and the process expects the labels to be sorted accordingly. This is the default behavior in openEO for spatial and temporal dimensions.","schema":{"type":"array","items":{"type":["number","null"]}}}],"returns":{"description":"An array with no-data values being replaced with interpolated values. If not at least 2 numerical values are available in the array, the array stays the same.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"data":[null,1,null,6,null,-8]},"returns":[null,1,3.5,6,-1,-8]}],"links":[{"rel":"about","href":"https://en.wikipedia.org/wiki/Linear_interpolation","title":"Linear interpolation explained by Wikipedia"}]},{"id":"array_modify","summary":"Change the content of an array (insert, remove, update)","description":"Allows to insert into, remove from or update an array.\\n\\nAll labels get discarded and the array indices are always a sequence of numbers with the step size of 1 and starting at 0.","categories":["arrays"],"experimental":true,"parameters":[{"name":"data","description":"An array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"values","description":"The values to fill the array with.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"index","description":"The index of the element to insert the value(s) before. If the index is greater than the number of elements, the process throws an `ArrayElementNotAvailable` exception.\\n\\nTo insert after the last element, there are two options:\\n\\n1. Use the simpler processes ``array_append()`` to append a single value or ``array_concat`` to append multiple values.\\n2. Specify the number of elements in the array. You can retrieve the number of elements with the process ``count()``, having the parameter `condition` set to `true`.","schema":{"type":"integer","minimum":0}},{"name":"length","description":"The number of elements to replace. This parameter has no effect in case the given `index` does not exist in the array given.","optional":true,"default":1,"schema":{"type":"integer","minimum":0}}],"returns":{"description":"An array with values added, updated or removed.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},"exceptions":{"ArrayElementNotAvailable":{"message":"The array has no element with the specified index."}},"examples":[{"description":"Replace a single value in the array.","arguments":{"data":["a","d","c"],"values":["b"],"index":1},"returns":["a","b","c"]},{"description":"Replace multiple values in the array.","arguments":{"data":["a","b",4,5],"values":[1,2,3],"index":0,"length":2},"returns":[1,2,3,4,5]},{"description":"Insert a value to the array at a given position.","arguments":{"data":["a","c"],"values":["b"],"index":1,"length":0},"returns":["a","b","c"]},{"description":"Add a value at a specific non-existing position after the array, fill missing elements with `null`.","arguments":{"data":["a","b"],"values":["e"],"index":4},"returns":["a","b",null,null,"e"]},{"description":"Remove a single value from the array.","arguments":{"data":["a","b",null,"c"],"values":[],"index":2},"returns":["a","b","c"]},{"description":"Remove multiple values from the array.","arguments":{"data":[null,null,"a","b","c"],"values":[],"index":0,"length":2},"returns":["a","b","c"]}]},{"id":"atmospheric_correction","summary":"Apply atmospheric correction","description":"Applies an atmospheric correction that converts top of atmosphere reflectance values into bottom of atmosphere/top of canopy reflectance values.","categories":["cubes","optical"],"experimental":true,"parameters":[{"description":"Data cube containing multi-spectral optical top of atmosphere reflectances to be corrected.","name":"data","schema":{"subtype":"raster-cube","type":"object"}},{"description":"The atmospheric correction method to use. To get reproducible results, you have to set a specific method.\\n\\nSet to `null` to allow the back-end to choose, which will improve portability, but reduce reproducibility as you *may* get different results if you run the processes multiple times.","name":"method","schema":[{"type":["string"],"enum":["FORCE","iCOR"]},{"type":"null"}]},{"description":"The digital elevation model to use. Set to `null` (the default) to allow the back-end to choose, which will improve portability, but reduce reproducibility.","name":"elevation_model","optional":true,"default":null,"schema":[{"type":"string","subtype":"collection-id"},{"type":"null"}]},{"description":"Proprietary options for the atmospheric correction method. Specifying proprietary options will reduce portability.","name":"options","optional":true,"default":{},"schema":{"type":"object","additionalProperties":false}}],"returns":{"description":"Data cube containing bottom of atmosphere reflectances.","schema":{"subtype":"raster-cube","type":"object"}},"exceptions":{"DigitalElevationModelInvalid":{"message":"The digital elevation model specified is either not a DEM or can\'t be used with the data cube given."}},"links":[{"rel":"about","href":"https://bok.eo4geo.eu/IP1-7-1","title":"Atmospheric correction explained by EO4GEO body of knowledge."}]},{"id":"cloud_detection","summary":"Create cloud masks","description":"Detects atmospheric disturbances such as clouds, cloud shadows, aerosols, haze, ozone and/or water vapour in optical imagery.\\n\\nIt creates a data cube with the spatial and temporal dimensions compatible to the source data cube and a dimension that contains a dimension label for each of the supported/considered atmospheric disturbances. The naming of the bands follows these pre-defined values:\\n\\n- `cloud`\\n- `shadow`\\n- `aerosol`\\n- `haze`\\n- `ozone`\\n- `water_vapor`\\n\\nAll bands have values between 0 (clear) and 1, which describes the probability that it is an atmospheric disturbance.","categories":["cubes","optical"],"experimental":true,"parameters":[{"description":"The source data cube containing multi-spectral optical top of the atmosphere (TOA) reflectances on which to perform cloud detection.","name":"data","schema":{"subtype":"raster-cube","type":"object"}},{"description":"The cloud detection method to use. To get reproducible results, you have to set a specific method.\\n\\nSet to `null` to allow the back-end to choose, which will improve portability, but reduce reproducibility as you *may* get different results if you run the processes multiple times.","name":"method","schema":[{"type":["string"],"enum":["Fmask","s2cloudless","Sen2Cor"]},{"type":"null"}]},{"description":"Proprietary options for the cloud detection method. Specifying proprietary options will reduce portability.","name":"options","optional":true,"default":{},"schema":{"type":"object","additionalProperties":false}}],"returns":{"description":"A data cube with bands for the atmospheric disturbances. Each of the masks contains values between 0 and 1. The data cube has the same spatial and temporal dimensions as the source data cube and a dimension that contains a dimension label for each of the supported/considered atmospheric disturbance.","schema":{"subtype":"raster-cube","type":"object"}},"links":[{"rel":"about","href":"https://bok.eo4geo.eu/TA14-2-2-1-3","title":"Cloud mask explained by EO4GEO body of knowledge."}]},{"id":"cummax","summary":"Cumulative maxima","description":"Finds cumulative maxima of an array of numbers. Every computed element is equal to the bigger one between the current element and the previously computed element. The returned array and the input array have always the same length.\\n\\nBy default, no-data values are skipped, but stay in the result. Setting the `ignore_nodata` flag to `true` makes that once a no-data value (`null`) is reached all following elements are set to `null` in the result.","categories":["math > cumulative"],"experimental":true,"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not and ignores them by default. Setting this flag to `false` considers no-data values so that `null` is set for all the following elements.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"An array with the computed cumulative maxima.","schema":{"type":"array","items":{"type":["number","null"]}}},"examples":[{"arguments":{"data":[1,3,5,3,1]},"returns":[1,3,5,5,5]},{"arguments":{"data":[1,3,null,5,1]},"returns":[1,3,null,5,5]},{"arguments":{"data":[1,3,null,5,1],"ignore_nodata":false},"returns":[1,3,null,null,null]}]},{"id":"cummin","summary":"Cumulative minima","description":"Finds cumulative minima of an array of numbers. Every computed element is equal to the smaller one between the current element and the previously computed element. The returned array and the input array have always the same length.\\n\\nBy default, no-data values are skipped, but stay in the result. Setting the `ignore_nodata` flag to `true` makes that once a no-data value (`null`) is reached all following elements are set to `null` in the result.","categories":["math > cumulative"],"experimental":true,"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not and ignores them by default. Setting this flag to `false` considers no-data values so that `null` is set for all the following elements.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"An array with the computed cumulative minima.","schema":{"type":"array","items":{"type":["number","null"]}}},"examples":[{"arguments":{"data":[5,3,1,3,5]},"returns":[5,3,1,1,1]},{"arguments":{"data":[5,3,null,1,5]},"returns":[5,3,null,1,1]},{"arguments":{"data":[5,3,null,1,5],"ignore_nodata":false},"returns":[5,3,null,null,null]}]},{"id":"cumproduct","summary":"Cumulative products","description":"Computes cumulative products of an array of numbers. Every computed element is equal to the product of the current and all previous values. The returned array and the input array have always the same length.\\n\\nBy default, no-data values are skipped, but stay in the result. Setting the `ignore_nodata` flag to `true` makes that once a no-data value (`null`) is reached all following elements are set to `null` in the result.","categories":["math > cumulative"],"experimental":true,"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not and ignores them by default. Setting this flag to `false` considers no-data values so that `null` is set for all the following elements.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"An array with the computed cumulative products.","schema":{"type":"array","items":{"type":["number","null"]}}},"examples":[{"arguments":{"data":[1,3,5,3,1]},"returns":[1,3,15,45,45]},{"arguments":{"data":[1,2,3,null,3,1]},"returns":[1,2,6,null,18,18]},{"arguments":{"data":[1,2,3,null,3,1],"ignore_nodata":false},"returns":[1,2,6,null,null,null]}]},{"id":"cumsum","summary":"Cumulative sums","description":"Computes cumulative sums of an array of numbers. Every computed element is equal to the sum of current and all previous values. The returned array and the input array have always the same length.\\n\\nBy default, no-data values are skipped, but stay in the result. Setting the `ignore_nodata` flag to `true` makes that once a no-data value (`null`) is reached all following elements are set to `null` in the result.","categories":["math > cumulative"],"experimental":true,"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not and ignores them by default. Setting this flag to `false` considers no-data values so that `null` is set for all the following elements.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"An array with the computed cumulative sums.","schema":{"type":"array","items":{"type":["number","null"]}}},"examples":[{"arguments":{"data":[1,3,5,3,1]},"returns":[1,4,9,12,13]},{"arguments":{"data":[1,3,null,3,1]},"returns":[1,4,null,7,8]},{"arguments":{"data":[1,3,null,3,1],"ignore_nodata":false},"returns":[1,4,null,null,null]}]},{"id":"date_shift","summary":"Manipulates dates and times by addition or subtraction","description":"Based on a given date (and optionally time), calculates a new date (and time if given) by adding or subtracting a given temporal period.\\n\\nSome specifics about dates and times need to be taken into account:\\n\\n* This process doesn\'t have any effect on the time zone.\\n* It doesn\'t take daylight saving time (DST) into account as only dates and time in UTC (with potential numerical time zone modifier) are supported.\\n* Leap years are implemented in a way that computations handle them gracefully (see parameter `unit` for details).\\n* Leap seconds are mostly ignored in manipulations as they don\'t follow a regular pattern. Leap seconds can be passed to the process, but will never be returned.","categories":["date & time"],"experimental":true,"parameters":[{"name":"date","description":"The date (and optionally time) to manipulate.\\n\\nIf the given date doesn\'t include the time, the process assumes that the time component is `00:00:00Z` (i.e. midnight, in UTC). The millisecond part of the time is optional and defaults to `0` if not given.","schema":[{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"}]},{"name":"value","description":"The period of time in the unit given that is added (positive numbers) or subtracted (negative numbers). The value `0` doesn\'t have any effect.","schema":{"type":"integer"}},{"name":"unit","description":"The unit for the value given. The following pre-defined units are available:\\n\\n- millisecond: Milliseconds\\n- second: Seconds - leap seconds are ignored in computations.\\n- minute: Minutes\\n- hour: Hours\\n- day: Days - changes only the the day part of a date\\n- week: Weeks (equivalent to 7 days)\\n- month: Months\\n- year: Years\\n\\nManipulations with the unit `year`, `month`, `week` or `day` do never change the time. If any of the manipulations result in an invalid date or time, the corresponding part is rounded down to the next valid date or time respectively. For example, adding a month to `2020-01-31` would result in `2020-02-29`.","schema":{"type":"string","enum":["millisecond","second","minute","hour","day","week","month","year"]}}],"returns":{"description":"The manipulated date. If a time component was given in the parameter `date`, the time component is returned with the date.","schema":[{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"}]},"examples":[{"arguments":{"date":"2020-02-01T17:22:45Z","value":6,"unit":"month"},"returns":"2020-08-01T17:22:45Z"},{"arguments":{"date":"2021-03-31T00:00:00+02:00","value":-7,"unit":"day"},"returns":"2021-03-24T00:00:00+02:00"},{"description":"Adding a year to February 29th in a leap year will result in February 28th in the next (non-leap) year.","arguments":{"date":"2020-02-29T17:22:45Z","value":1,"unit":"year"},"returns":"2021-02-28T17:22:45Z"},{"description":"Adding a month to January 31th will result in February 29th in leap years.","arguments":{"date":"2020-01-31","value":1,"unit":"month"},"returns":"2020-02-29"},{"description":"The process skips over the leap second `2016-12-31T23:59:60Z`.","arguments":{"date":"2016-12-31T23:59:59Z","value":1,"unit":"second"},"returns":"2017-01-01T00:00:00Z"},{"description":"Milliseconds can be added or subtracted. If not given, the default value is `0`.","arguments":{"date":"2018-12-31T17:22:45Z","value":1150,"unit":"millisecond"},"returns":"2018-12-31T17:22:46.150Z"},{"arguments":{"date":"2018-01-01","value":25,"unit":"hour"},"returns":"2018-01-02"},{"arguments":{"date":"2018-01-01","value":-1,"unit":"hour"},"returns":"2017-12-31"}]},{"id":"debug","summary":"Publish debugging information","description":"Sends debugging information about the data to the log output. Passes the data through.","categories":["development"],"experimental":true,"parameters":[{"name":"data","description":"Data to publish.","schema":{"description":"Any data type is allowed."}},{"name":"code","description":"An identifier to help identify the log entry in a bunch of other log entries.","schema":{"type":"string"},"default":"","optional":true},{"name":"level","description":"The severity level of this message, defaults to `info`. Note that the level `error` forces the computation to be stopped!","schema":{"type":"string","enum":["error","warning","info","debug"]},"default":"info","optional":true},{"name":"message","description":"A message to send in addition to the data.","schema":{"type":"string"},"default":"","optional":true}],"returns":{"description":"The data as passed to the `data` parameter without any modification.","schema":{"description":"Any data type is allowed."}}},{"id":"filter_labels","summary":"Filter dimension labels based on a condition","description":"Filters the dimension labels in the data cube for the given dimension. Only the dimension labels that match the specified condition are preserved, all other labels with their corresponding data get removed.","categories":["cubes","filter"],"experimental":true,"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"condition","description":"A condition that is evaluated against each dimension label in the specified dimension. A dimension label and the corresponding data is preserved for the given dimension, if the condition returns `true`.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"value","description":"A single dimension label to compare against. The data type of the parameter depends on the dimension labels set for the dimension.","schema":[{"type":"number"},{"type":"string"}]},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"`true` if the dimension label should be kept in the data cube, otherwise `false`.","schema":{"type":"boolean"}}}},{"name":"dimension","description":"The name of the dimension to filter on. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.","schema":{"type":"string"}},{"name":"context","description":"Additional data to be passed to the condition.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the given dimension has less (or the same) dimension labels.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."}},"examples":[{"description":"Filters the data cube to only contain data from platform Sentinel-2A. This example assumes that the data cube has a dimension `platform` so that computations can distinguish between Sentinel-2A and Sentinel-2B data.","arguments":{"data":{"from_parameter":"sentinel2_data"},"condition":{"process_graph":{"eq":{"process_id":"eq","arguments":{"x":{"from_parameter":"value"},"y":"Sentinel-2A","case_sensitive":false},"result":true}}},"dimension":"platform"}}],"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#filter","rel":"about","title":"Filters explained in the openEO documentation"}]},{"id":"is_infinite","summary":"Value is an infinite number","description":"Checks whether the specified value `x` is an infinite number. The definition of infinite numbers follows the [IEEE Standard 754](https://ieeexplore.ieee.org/document/4610935). The special numerical value `NaN` (not a number) as defined by the [IEEE Standard 754](https://ieeexplore.ieee.org/document/4610935) is not an infinite number and must return `false`.","categories":["comparison"],"experimental":true,"parameters":[{"name":"x","description":"The data to check.","schema":{"description":"Any data type is allowed."}}],"returns":{"description":"`true` if the data is an infinite number, otherwise `false`.","schema":{"type":"boolean"}},"links":[{"rel":"about","href":"https://ieeexplore.ieee.org/document/4610935","title":"IEEE Standard 754-2008 for Floating-Point Arithmetic"}]},{"id":"load_result","summary":"Load batch job results","description":"Loads batch job results by job id from the server-side user workspace. The job must have been stored by the authenticated user on the back-end currently connected to.","categories":["cubes","import"],"experimental":true,"parameters":[{"name":"id","description":"The id of a batch job with results.","schema":{"type":"string","subtype":"job-id","pattern":"^[\\\\w\\\\-\\\\.~]+$"}}],"returns":{"description":"A data cube for further processing.","schema":{"type":"object","subtype":"raster-cube"}}},{"id":"load_uploaded_files","summary":"Load files from the user workspace","description":"Loads one or more user-uploaded files from the server-side workspace of the authenticated user and returns them as a single data cube. The files must have been stored by the authenticated user on the back-end currently connected to.","categories":["cubes","import"],"experimental":true,"parameters":[{"name":"paths","description":"The files to read. Folders can\'t be specified, specify all files instead. An exception is thrown if a file can\'t be read.","schema":{"type":"array","subtype":"file-paths","items":{"type":"string","subtype":"file-path","pattern":"^[^\\r\\n\\\\:\'\\"]+$"}}},{"name":"format","description":"The file format to read from. It must be one of the values that the server reports as supported input file formats, which usually correspond to the short GDAL/OGR codes. If the format is not suitable for loading the data, a `FormatUnsuitable` exception will be thrown. This parameter is *case insensitive*.","schema":{"type":"string","subtype":"input-format"}},{"name":"options","description":"The file format parameters to be used to read the files. Must correspond to the parameters that the server reports as supported parameters for the chosen `format`. The parameter names and valid values usually correspond to the GDAL/OGR format options.","schema":{"type":"object","subtype":"input-format-options"},"default":{},"optional":true}],"returns":{"description":"A data cube for further processing.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"FormatUnsuitable":{"message":"Data can\'t be loaded with the requested input format."}}},{"id":"nan","summary":"Not a Number (NaN)","description":"`NaN` (not a number) is a symbolic floating-point representation which is neither a signed infinity nor a finite number.","categories":["math > constants"],"experimental":true,"parameters":[],"returns":{"description":"Returns `NaN`.","schema":{"description":"JSON Schema can\'t represent `NaN` and thus a schema can\'t be specified."}},"links":[{"rel":"about","href":"https://ieeexplore.ieee.org/document/4610935","title":"IEEE Standard 754-2008 for Floating-Point Arithmetic"},{"rel":"about","href":"http://mathworld.wolfram.com/NaN.html","title":"NaN explained by Wolfram MathWorld"}]},{"id":"reduce_spatial","summary":"Reduce spatial dimensions \'x\' and \'y\'","description":"Applies a reducer to a data cube by collapsing all the pixel values along the horizontal spatial dimensions (i.e. axes `x` and `y`) into an output value computed by the reducer. The horizontal spatial dimensions are dropped.\\n\\nAn aggregation over certain spatial areas can be computed with the process ``aggregate_spatial()``.\\n\\nThis process passes a list of values to the reducer. The list of values has an undefined order, therefore processes such as ``last()`` and ``first()`` that depend on the order of the values will lead to unpredictable results.","categories":["aggregate & resample","cubes","reducer"],"experimental":true,"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"reducer","description":"A reducer to apply on the horizontal spatial dimensions. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category \'reducer\' for such processes.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"data","description":"An array with elements of any type.","schema":{"type":"array","items":{"description":"Any data type."}}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the new data cube.","schema":{"description":"Any data type."}}}},{"name":"context","description":"Additional data to be passed to the reducer.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A data cube with the newly computed values. It is missing the horizontal spatial dimensions, the number of dimensions decreases by two. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#reduce","rel":"about","title":"Reducers explained in the openEO documentation"}]},{"id":"resample_cube_temporal","summary":"Resample temporal dimensions to match a target data cube","description":"Resamples one or more given temporal dimensions from a source data cube to align with the corresponding dimensions of the given target data cube using the nearest neighbor method. Returns a new data cube with the resampled dimensions.\\n\\nBy default, this process simply takes the nearest neighbor independent of the value (including values such as no-data / `null`). Depending on the data cubes this may lead to values being assigned to two target timestamps. To only consider valid values in a specific range around the target timestamps, use the parameter `valid_within`.\\n\\nThe rare case of ties is resolved by choosing the earlier timestamps.","categories":["cubes","aggregate & resample"],"experimental":true,"parameters":[{"name":"data","description":"A data cube with one or more temporal dimensions.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"target","description":"A data cube that describes the temporal target resolution.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"dimension","description":"The name of the temporal dimension to resample, which must exist with this name in both data cubes. If the dimension is not set or is set to `null`, the process resamples all temporal dimensions that exist with the same names in both data cubes.\\n\\nThe following exceptions may occur:\\n\\n* A dimension is given, but it does not exist in any of the data cubes: `DimensionNotAvailable`\\n* A dimension is given, but one of them is not temporal: `DimensionMismatch`\\n\\n* No specific dimension name is given and there are no temporal dimensions with the same name in the data: `DimensionMismatch`","schema":{"type":["string","null"]},"default":null,"optional":true},{"name":"valid_within","description":"Setting this parameter to a numerical value enables that the process searches for valid values within the given period of days before and after the target timestamps. Valid values are determined based on the function ``is_valid()``. For example, the limit of `7` for the target timestamps `2020-01-15 12:00:00` looks for a nearest neighbor after `2020-01-08 12:00:00` and before `2020-01-22 12:00:00`. If no valid value is found within the given period, the value will be set to no-data (`null`).","schema":{"type":["number","null"]},"default":null,"optional":true}],"returns":{"description":"A raster data cube with the same dimensions and the same dimension properties (name, type, labels, reference system and resolution) for all non-temporal dimensions. For the temporal dimension, the name and type remain unchanged, but the dimension labels, resolution and reference system may change.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"DimensionMismatch":{"message":"The temporal dimensions for resampling don\'t match."},"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#resample","rel":"about","title":"Resampling explained in the openEO documentation"}]},{"id":"run_udf_externally","summary":"Run an externally hosted UDF container","description":"Runs a compatible UDF container that is either externally hosted by a service provider or running on a local machine of the user. The UDF container must follow the [openEO UDF specification](https://openeo.org/documentation/1.0/udfs.html).\\n\\nThe referenced UDF service can be executed in several processes such as ``aggregate_spatial()``, ``apply()``, ``apply_dimension()`` and ``reduce_dimension()``. In this case, an array is passed instead of a raster data cube. The user must ensure that the data is properly passed as an array so that the UDF can make sense of it.","categories":["cubes","import","udf"],"experimental":true,"parameters":[{"name":"data","description":"The data to be passed to the UDF as an array or raster data cube.","schema":[{"title":"Raster data cube","type":"object","subtype":"raster-cube"},{"title":"Array","type":"array","minItems":1,"items":{"description":"Any data type."}},{"title":"Single Value","description":"A single value of any data type."}]},{"name":"url","description":"Absolute URL to a remote UDF service.","schema":{"type":"string","format":"uri","subtype":"uri","pattern":"^(http|https)://"}},{"name":"context","description":"Additional data such as configuration options to be passed to the UDF.","schema":{"type":"object"},"default":{},"optional":true}],"returns":{"description":"The data processed by the UDF service.\\n\\n* Returns a raster data cube if a raster data cube is passed for `data`. Details on the dimensions and dimension properties (name, type, labels, reference system and resolution) depend on the UDF.\\n* If an array is passed for `data`, the returned value can be of any data type, but is exactly what the UDF returns.","schema":[{"title":"Raster data cube","type":"object","subtype":"raster-cube"},{"title":"Any","description":"Any data type."}]},"links":[{"rel":"about","href":"https://openeo.org/documentation/1.0/udfs.html","title":"openEO UDF specification"},{"rel":"about","href":"https://github.com/Open-EO/openeo-udf","title":"openEO UDF repository"}]},{"id":"sar_backscatter","summary":"Computes backscatter from SAR input","description":"Computes backscatter from SAR input.\\n\\nNote that backscatter computation may require instrument specific metadata that is tightly coupled to the original SAR products. As a result, this process may only work in combination with loading data from specific collections, not with general data cubes.\\n\\nThis process uses bilinear interpolation, both for resampling the DEM and the backscatter.","categories":["cubes","sar"],"experimental":true,"federation:backends":["vito","eodc"],"parameters":[{"name":"data","description":"The source data cube containing SAR input.","schema":{"subtype":"raster-cube","type":"object"}},{"name":"coefficient","description":"Select the radiometric correction coefficient. The following options are available:\\n\\n* `beta0`: radar brightness\\n* `sigma0-ellipsoid`: ground area computed with ellipsoid earth model\\n* `sigma0-terrain`: ground area computed with terrain earth model\\n* `gamma0-ellipsoid`: ground area computed with ellipsoid earth model in sensor line of sight\\n* `gamma0-terrain`: ground area computed with terrain earth model in sensor line of sight (default)\\n* `null`: non-normalized backscatter","optional":true,"default":"gamma0-terrain","schema":[{"type":"string","enum":["beta0","sigma0-ellipsoid","sigma0-terrain","gamma0-ellipsoid","gamma0-terrain"]},{"title":"Non-normalized backscatter","type":"null"}]},{"name":"elevation_model","description":"The digital elevation model to use. Set to `null` (the default) to allow the back-end to choose, which will improve portability, but reduce reproducibility.","optional":true,"default":null,"schema":[{"type":"string","subtype":"collection-id"},{"type":"null"}]},{"name":"mask","description":"If set to `true`, a data mask is added to the bands with the name `mask`. It indicates which values are valid (1), invalid (0) or contain no-data (null).","optional":true,"default":false,"schema":{"type":"boolean"}},{"name":"contributing_area","description":"If set to `true`, a DEM-based local contributing area band named `contributing_area` is added. The values are given in square meters.","optional":true,"default":false,"schema":{"type":"boolean"}},{"name":"local_incidence_angle","description":"If set to `true`, a DEM-based local incidence angle band named `local_incidence_angle` is added. The values are given in degrees.","optional":true,"default":false,"schema":{"type":"boolean"}},{"name":"ellipsoid_incidence_angle","description":"If set to `true`, an ellipsoidal incidence angle band named `ellipsoid_incidence_angle` is added. The values are given in degrees.","optional":true,"default":false,"schema":{"type":"boolean"}},{"name":"noise_removal","description":"If set to `false`, no noise removal is applied. Defaults to `true`, which removes noise.","optional":true,"default":true,"federation:backends":["vito"],"schema":{"type":"boolean"}}],"returns":{"description":"Backscatter values corresponding to the chosen parametrization. The values are given in linear scale.","schema":{"subtype":"raster-cube","type":"object"}},"exceptions":{"DigitalElevationModelInvalid":{"message":"The digital elevation model specified is either not a DEM or can\'t be used with the data cube given."}},"links":[{"rel":"about","href":"https://bok.eo4geo.eu/PP2-2-4-3","title":"Gamma nought (0) explained by EO4GEO body of knowledge."},{"rel":"about","href":"https://bok.eo4geo.eu/PP2-2-4-2","title":"Sigma nought (0) explained by EO4GEO body of knowledge."},{"rel":"about","href":"https://www.geo.uzh.ch/microsite/rsl-documents/research/publications/peer-reviewed-articles/201108-TGRS-Small-tcGamma-3809999360/201108-TGRS-Small-tcGamma.pdf","title":"Flattening Gamma: Radiometric Terrain Correction for SAR Imagery"},{"rel":"about","href":"https://doi.org/10.3390/data4030093","title":"Reasoning behind the choice of bilinear resampling"}]},{"id":"quantiles","summary":"Quantiles","description":"Calculates quantiles, which are cut points dividing the range of a probability distribution into either\\n\\n* intervals corresponding to the given `probabilities` or\\n* (nearly) equal-sized intervals (q-quantiles based on the parameter `q`).\\n\\nEither the parameter `probabilities` or `q` must be specified, otherwise the `QuantilesParameterMissing` exception is thrown. If both parameters are set the `QuantilesParameterConflict` exception is thrown.","categories":["math"],"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"probabilities","description":"A list of probabilities to calculate quantiles for. The probabilities must be between 0 and 1.","schema":{"type":"array","items":{"type":"number","minimum":0,"maximum":1}},"optional":true},{"name":"q","description":"Intervals to calculate quantiles for. Calculates q-quantiles with (nearly) equal-sized intervals.","schema":{"type":"integer","minimum":2},"optional":true},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that an array with `null` values is returned if any element is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"An array with the computed quantiles. The list has either\\n\\n* as many elements as the given list of `probabilities` had or\\n* *`q`-1* elements.\\n\\nIf the input array is empty the resulting array is filled with as many `null` values as required according to the list above. See the \'Empty array\' example for an example.","schema":{"type":"array","items":{"type":["number","null"]}}},"exceptions":{"QuantilesParameterMissing":{"message":"The process `quantiles` requires either the `probabilities` or `q` parameter to be set."},"QuantilesParameterConflict":{"message":"The process `quantiles` only allows that either the `probabilities` or the `q` parameter is set."}},"examples":[{"arguments":{"data":[2,4,4,4,5,5,7,9],"probabilities":[0.005,0.01,0.02,0.05,0.1,0.5]},"returns":[2.07,2.14,2.28,2.7,3.4,4.5]},{"arguments":{"data":[2,4,4,4,5,5,7,9],"q":4},"returns":[4,4.5,5.5]},{"arguments":{"data":[-1,-0.5,null,1],"q":2},"returns":[-0.5]},{"arguments":{"data":[-1,-0.5,null,1],"q":4,"ignore_nodata":false},"returns":[null,null,null]},{"title":"Empty array","arguments":{"data":[],"probabilities":[0.1,0.5]},"returns":[null,null]}],"links":[{"rel":"about","href":"https://en.wikipedia.org/wiki/Quantile","title":"Quantiles explained by Wikipedia"}]},{"id":"rearrange","summary":"Rearrange an array based on a permutation","description":"Rearranges an array based on a permutation, i.e. a ranked list of element positions in the original list. The positions must be zero-based.","categories":["arrays","sorting"],"parameters":[{"name":"data","description":"The array to rearrange.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},{"name":"order","description":"The permutation used for rearranging.","schema":{"type":"array","items":{"type":"integer","minimum":0}}}],"returns":{"description":"The rearranged array.","schema":{"type":"array","items":{"description":"Any data type is allowed."}}},"examples":[{"title":"Reverse a list","arguments":{"data":[5,4,3],"order":[2,1,0]},"returns":[3,4,5]},{"title":"Remove two elements","arguments":{"data":[5,4,3,2],"order":[1,3]},"returns":[4,2]},{"title":"Swap two elements","arguments":{"data":[5,4,3,2],"order":[0,2,1,3]},"returns":[5,3,4,2]}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Permutation.html","title":"Permutation explained by Wolfram MathWorld"}]},{"id":"reduce_dimension","summary":"Reduce dimensions","description":"Applies a reducer to a data cube dimension by collapsing all the pixel values along the specified dimension into an output value computed by the reducer.\\n\\nThe dimension is dropped. To avoid this, use ``apply_dimension()`` instead.","categories":["cubes","reducer"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"reducer","description":"A reducer to apply on the specified dimension. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category \'reducer\' for such processes.","schema":{"type":"object","subtype":"process-graph","parameters":[{"name":"data","description":"A labeled array with elements of any type.","schema":{"type":"array","subtype":"labeled-array","items":{"description":"Any data type."}}},{"name":"context","description":"Additional data passed by the user.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"The value to be set in the new data cube.","schema":{"description":"Any data type."}}}},{"name":"dimension","description":"The name of the dimension over which to reduce. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.","schema":{"type":"string"}},{"name":"context","description":"Additional data to be passed to the reducer.","schema":{"description":"Any data type."},"optional":true,"default":null}],"returns":{"description":"A data cube with the newly computed values. It is missing the given dimension, the number of dimensions decreases by one. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#reduce","rel":"about","title":"Reducers explained in the openEO documentation"}]},{"id":"rename_dimension","summary":"Rename a dimension","description":"Renames a dimension in the data cube while preserving all other properties.","categories":["cubes"],"parameters":[{"name":"data","description":"The data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"source","description":"The current name of the dimension. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.","schema":{"type":"string"}},{"name":"target","description":"A new Name for the dimension. Fails with a `DimensionExists` exception if a dimension with the specified name exists.","schema":{"type":"string"}}],"returns":{"description":"A data cube with the same dimensions, but the name of one of the dimensions changes. The old name can not be referred to any longer. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"DimensionNotAvailable":{"message":"A dimension with the specified name does not exist."},"DimensionExists":{"message":"A dimension with the specified name already exists."}}},{"id":"rename_labels","summary":"Rename dimension labels","description":"Renames the labels of the specified dimension in the data cube from `source` to `target`.\\n\\nIf the array for the source labels is empty (the default), the dimension labels are expected to be enumerated with zero-based numbering (0,1,2,3,...) so that the dimension labels directly map to the indices of the array specified for the parameter `target`. If the dimension labels are not enumerated and the `target` parameter is not specified, the `LabelsNotEnumerated` exception is thrown. The number of the source and target labels must be equal. Otherwise, the exception `LabelMismatch` is thrown.\\n\\nThis process doesn\'t change the order of the labels and their corresponding data.","categories":["cubes"],"parameters":[{"name":"data","description":"The data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"dimension","description":"The name of the dimension to rename the labels for.","schema":{"type":"string"}},{"name":"target","description":"The new names for the labels. The dimension labels in the data cube are expected to be enumerated if the parameter `target` is not specified. If a target dimension label already exists in the data cube, a `LabelExists` exception is thrown.","schema":{"type":"array","items":{"type":["number","string"]}}},{"name":"source","description":"The names of the labels as they are currently in the data cube. The array defines an unsorted and potentially incomplete list of labels that should be renamed to the names available in the corresponding array elements in the parameter `target`. If one of the source dimension labels doesn\'t exist, the `LabelNotAvailable` exception is thrown. By default, the array is empty so that the dimension labels in the data cube are expected to be enumerated.","schema":{"type":"array","items":{"type":["number","string"]}},"default":[],"optional":true}],"returns":{"description":"The data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that for the given dimension the labels change. The old labels can not be referred to any longer. The number of labels remains the same.","schema":{"type":"object","subtype":"raster-cube"}},"exceptions":{"LabelsNotEnumerated":{"message":"The dimension labels are not enumerated."},"LabelMismatch":{"message":"The number of labels in the parameters `source` and `target` don\'t match."},"LabelNotAvailable":{"message":"A label with the specified name does not exist."},"LabelExists":{"message":"A label with the specified name exists."}},"examples":[{"title":"Rename named labels","description":"Renaming the bands from `B1` to `red`, from `B2` to `green` and from `B3` to `blue`.","arguments":{"data":{"from_parameter":"data"},"dimension":"bands","source":["B1","B2","B3"],"target":["red","green","blue"]}}],"links":[{"rel":"example","type":"application/json","href":"https://processes.openeo.org/1.1.0/examples/rename-enumerated-labels.json","title":"Rename enumerated labels"}]},{"id":"resample_cube_spatial","summary":"Resample the spatial dimensions to match a target data cube","description":"Resamples the spatial dimensions (x,y) from a source data cube to align with the corresponding dimensions of the given target data cube. Returns a new data cube with the resampled dimensions.\\n\\nTo resample a data cube to a specific resolution or projection regardless of an existing target data cube, refer to ``resample_spatial()``.","categories":["cubes","aggregate & resample"],"parameters":[{"name":"data","description":"A data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"target","description":"A data cube that describes the spatial target resolution.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"method","description":"Resampling method to use. The following options are available and are meant to align with [`gdalwarp`](https://gdal.org/programs/gdalwarp.html#cmdoption-gdalwarp-r):\\n\\n* `average`: average (mean) resampling, computes the weighted average of all valid pixels\\n* `bilinear`: bilinear resampling\\n* `cubic`: cubic resampling\\n* `cubicspline`: cubic spline resampling\\n* `lanczos`: Lanczos windowed sinc resampling\\n* `max`: maximum resampling, selects the maximum value from all valid pixels\\n* `med`: median resampling, selects the median value of all valid pixels\\n* `min`: minimum resampling, selects the minimum value from all valid pixels\\n* `mode`: mode resampling, selects the value which appears most often of all the sampled points\\n* `near`: nearest neighbour resampling (default)\\n* `q1`: first quartile resampling, selects the first quartile value of all valid pixels\\n* `q3`: third quartile resampling, selects the third quartile value of all valid pixels\\n* `rms` root mean square (quadratic mean) of all valid pixels\\n* `sum`: compute the weighted sum of all valid pixels\\n\\nValid pixels are determined based on the function ``is_valid()``.","schema":{"type":"string","enum":["average","bilinear","cubic","cubicspline","lanczos","max","med","min","mode","near","q1","q3","rms","sum"]},"default":"near","optional":true}],"returns":{"description":"A data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the spatial dimensions.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#resample","rel":"about","title":"Resampling explained in the openEO documentation"}]},{"id":"resample_spatial","summary":"Resample and warp the spatial dimensions","description":"Resamples the spatial dimensions (x,y) of the data cube to a specified resolution and/or warps the data cube to the target projection. At least `resolution` or `projection` must be specified.\\n\\nRelated processes:\\n\\n* Use ``filter_bbox()`` to set the target spatial extent.\\n* To spatially align two data cubes with each other (e.g. for merging), better use the process ``resample_cube_spatial()``.","categories":["cubes","aggregate & resample"],"parameters":[{"name":"data","description":"A raster data cube.","schema":{"type":"object","subtype":"raster-cube"}},{"name":"resolution","description":"Resamples the data cube to the target resolution, which can be specified either as separate values for x and y or as a single value for both axes. Specified in the units of the target projection. Doesn\'t change the resolution by default (`0`).","schema":[{"description":"A single number used as the resolution for both x and y.","type":"number","minimum":0},{"description":"A two-element array to specify separate resolutions for x (first element) and y (second element).","type":"array","minItems":2,"maxItems":2,"items":{"type":"number","minimum":0}}],"default":0,"optional":true},{"name":"projection","description":"Warps the data cube to the target projection, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html), [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). By default (`null`), the projection is not changed.","schema":[{"title":"EPSG Code","type":"integer","subtype":"epsg-code","minimum":1000,"examples":[3857]},{"title":"WKT2","type":"string","subtype":"wkt2-definition"},{"title":"PROJ definition","type":"string","subtype":"proj-definition","deprecated":true},{"title":"Don\'t change projection","type":"null"}],"default":null,"optional":true},{"name":"method","description":"Resampling method to use. The following options are available and are meant to align with [`gdalwarp`](https://gdal.org/programs/gdalwarp.html#cmdoption-gdalwarp-r):\\n\\n* `average`: average (mean) resampling, computes the weighted average of all valid pixels\\n* `bilinear`: bilinear resampling\\n* `cubic`: cubic resampling\\n* `cubicspline`: cubic spline resampling\\n* `lanczos`: Lanczos windowed sinc resampling\\n* `max`: maximum resampling, selects the maximum value from all valid pixels\\n* `med`: median resampling, selects the median value of all valid pixels\\n* `min`: minimum resampling, selects the minimum value from all valid pixels\\n* `mode`: mode resampling, selects the value which appears most often of all the sampled points\\n* `near`: nearest neighbour resampling (default)\\n* `q1`: first quartile resampling, selects the first quartile value of all valid pixels\\n* `q3`: third quartile resampling, selects the third quartile value of all valid pixels\\n* `rms` root mean square (quadratic mean) of all valid pixels\\n* `sum`: compute the weighted sum of all valid pixels\\n\\nValid pixels are determined based on the function ``is_valid()``.","schema":{"type":"string","enum":["average","bilinear","cubic","cubicspline","lanczos","max","med","min","mode","near","q1","q3","rms","sum"]},"default":"near","optional":true},{"name":"align","description":"Specifies to which corner of the spatial extent the new resampled data is aligned to.","schema":{"type":"string","enum":["lower-left","upper-left","lower-right","upper-right"]},"default":"upper-left","optional":true}],"returns":{"description":"A raster data cube with values warped onto the new projection. It has the same dimensions and the same dimension properties (name, type, labels, reference system and resolution) for all non-spatial or vertical spatial dimensions. For the horizontal spatial dimensions the name and type remain unchanged, but reference system, labels and resolution may change depending on the given parameters.","schema":{"type":"object","subtype":"raster-cube"}},"links":[{"href":"https://openeo.org/documentation/1.0/datacubes.html#resample","rel":"about","title":"Resampling explained in the openEO documentation"},{"rel":"about","href":"https://proj.org/usage/projections.html","title":"PROJ parameters for cartographic projections"},{"rel":"about","href":"http://www.epsg-registry.org","title":"Official EPSG code registry"},{"rel":"about","href":"http://www.epsg.io","title":"Unofficial EPSG code database"},{"href":"https://gdal.org/programs/gdalwarp.html#cmdoption-gdalwarp-r","rel":"about","title":"gdalwarp resampling methods"}]},{"id":"round","summary":"Round to a specified precision","description":"Rounds a real number `x` to specified precision `p`.\\n\\nIf the fractional part of `x` is halfway between two integers, one of which is even and the other odd, then the even number is returned.\\nThis behavior follows [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229). This kind of rounding is also called \\"round to nearest (even)\\" or \\"banker\'s rounding\\". It minimizes rounding errors that result from consistently rounding a midpoint value in a single direction.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > rounding"],"parameters":[{"name":"x","description":"A number to round.","schema":{"type":["number","null"]}},{"name":"p","description":"A positive number specifies the number of digits after the decimal point to round to. A negative number means rounding to a power of ten, so for example *-2* rounds to the nearest hundred. Defaults to *0*.","schema":{"type":"integer"},"default":0,"optional":true}],"returns":{"description":"The rounded number.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":0},{"arguments":{"x":3.56,"p":1},"returns":3.6},{"arguments":{"x":-0.4444444,"p":2},"returns":-0.44},{"arguments":{"x":-2.5},"returns":-2},{"arguments":{"x":-3.5},"returns":-4},{"arguments":{"x":1234.5,"p":-2},"returns":1200}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/AbsoluteValue.html","title":"Absolute value explained by Wolfram MathWorld"},{"rel":"about","href":"https://ieeexplore.ieee.org/document/8766229","title":"IEEE Standard 754-2019 for Floating-Point Arithmetic"}]},{"id":"run_udf","summary":"Run a UDF","description":"Runs a UDF in one of the supported runtime environments.\\n\\nThe process can either:\\n\\n1. load and run a UDF stored in a file on the server-side workspace of the authenticated user. The path to the UDF file must be relative to the root directory of the user\'s workspace.\\n2. fetch and run a remotely stored and published UDF by absolute URI.\\n3. run the source code specified inline as string.\\n\\nThe loaded UDF can be executed in several processes such as ``aggregate_spatial()``, ``apply()``, ``apply_dimension()`` and ``reduce_dimension()``. In this case, an array is passed instead of a raster data cube. The user must ensure that the data is properly passed as an array so that the UDF can make sense of it.","categories":["cubes","import","udf"],"parameters":[{"name":"data","description":"The data to be passed to the UDF as an array or raster data cube.","schema":[{"title":"Raster data cube","type":"object","subtype":"raster-cube"},{"title":"Array","type":"array","minItems":1,"items":{"description":"Any data type."}},{"title":"Single Value","description":"A single value of any data type."}]},{"name":"udf","description":"Either source code, an absolute URL or a path to a UDF script.","schema":[{"description":"Absolute URL to a UDF","type":"string","format":"uri","subtype":"uri","pattern":"^(http|https)://"},{"description":"Path to a UDF uploaded to the server.","type":"string","subtype":"file-path","pattern":"^[^\\r\\n\\\\:\'\\"]+$"},{"description":"The multi-line source code of a UDF, must contain a newline/line-break.","type":"string","subtype":"udf-code","pattern":"(\\r\\n|\\r|\\n)"}]},{"name":"runtime","description":"A UDF runtime identifier available at the back-end.","schema":{"type":"string","subtype":"udf-runtime"}},{"name":"version","description":"An UDF runtime version. If set to `null`, the default runtime version specified for each runtime is used.","schema":[{"type":"string","subtype":"udf-runtime-version"},{"title":"Default runtime version","type":"null"}],"default":null,"optional":true},{"name":"context","description":"Additional data such as configuration options to be passed to the UDF.","schema":{"type":"object"},"default":{},"optional":true}],"exceptions":{"InvalidVersion":{"message":"The specified UDF runtime version is not supported."}},"returns":{"description":"The data processed by the UDF.\\n\\n* Returns a raster data cube, if a raster data cube is passed for `data`. Details on the dimensions and dimension properties (name, type, labels, reference system and resolution) depend on the UDF.\\n* If an array is passed for `data`, the returned value can be of any data type, but is exactly what the UDF returns.","schema":[{"title":"Raster data cube","type":"object","subtype":"raster-cube"},{"title":"Any","description":"Any data type."}]}},{"id":"save_result","summary":"Save processed data to storage","description":"Saves processed data to the server-side user workspace of the authenticated user. This process aims to be compatible with GDAL/OGR formats and options. STAC-compatible metadata should be stored with the processed data.\\n\\nCalling this process may be rejected by back-ends in the context of secondary web services.","categories":["cubes","export"],"parameters":[{"name":"data","description":"The data to save.","schema":[{"type":"object","subtype":"raster-cube"},{"type":"object","subtype":"vector-cube"}]},{"name":"format","description":"The file format to save to. It must be one of the values that the server reports as supported output file formats, which usually correspond to the short GDAL/OGR codes. If the format is not suitable for storing the underlying data structure, a `FormatUnsuitable` exception will be thrown. This parameter is *case insensitive*.","schema":{"type":"string","subtype":"output-format"}},{"name":"options","description":"The file format parameters to be used to create the file(s). Must correspond to the parameters that the server reports as supported parameters for the chosen `format`. The parameter names and valid values usually correspond to the GDAL/OGR format options.","schema":{"type":"object","subtype":"output-format-options"},"default":{},"optional":true}],"returns":{"description":"`false` if saving failed, `true` otherwise.","schema":{"type":"boolean"}},"exceptions":{"FormatUnsuitable":{"message":"Data can\'t be transformed into the requested output format."}},"links":[{"rel":"about","href":"https://www.gdal.org/formats_list.html","title":"GDAL Raster Formats"},{"rel":"about","href":"https://www.gdal.org/ogr_formats.html","title":"OGR Vector Formats"}]},{"id":"sd","summary":"Standard deviation","description":"Computes the sample standard deviation, which quantifies the amount of variation of an array of numbers. It is defined to be the square root of the corresponding variance (see ``variance()``).\\n\\nA low standard deviation indicates that the values tend to be close to the expected value, while a high standard deviation indicates that the values are spread out over a wider range.\\n\\nAn array without non-`null` elements resolves always with `null`.","categories":["math","reducer"],"parameters":[{"name":"data","description":"An array of numbers.","schema":{"type":"array","items":{"type":["number","null"]}}},{"name":"ignore_nodata","description":"Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.","schema":{"type":"boolean"},"default":true,"optional":true}],"returns":{"description":"The computed sample standard deviation.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"data":[-1,1,3,null]},"returns":2},{"arguments":{"data":[-1,1,3,null],"ignore_nodata":false},"returns":null},{"description":"The input array is empty: return `null`.","arguments":{"data":[]},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/StandardDeviation.html","title":"Standard deviation explained by Wolfram MathWorld"}],"process_graph":{"variance":{"process_id":"variance","arguments":{"data":{"from_parameter":"data"},"ignore_nodata":{"from_parameter":"ignore_nodata"}}},"power":{"process_id":"power","arguments":{"base":{"from_node":"variance"},"p":0.5},"result":true}}},{"id":"sgn","summary":"Signum","description":"The signum (also known as *sign*) of `x` is defined as:\\n\\n* *1* if *x > 0*\\n* *0* if *x = 0*\\n* *-1* if *x < 0*\\n\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math"],"parameters":[{"name":"x","description":"A number.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed signum value of `x`.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":-2},"returns":-1},{"arguments":{"x":3.5},"returns":1},{"arguments":{"x":0},"returns":0},{"arguments":{"x":null},"returns":null}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Sign.html","title":"Sign explained by Wolfram MathWorld"}],"process_graph":{"gt0":{"process_id":"gt","arguments":{"x":{"from_parameter":"x"},"y":0}},"lt0":{"process_id":"lt","arguments":{"x":{"from_parameter":"x"},"y":0}},"if_gt0":{"process_id":"if","arguments":{"value":{"from_node":"gt0"},"accept":1,"reject":{"from_parameter":"x"}}},"if_lt0":{"process_id":"if","arguments":{"value":{"from_node":"lt0"},"accept":-1,"reject":{"from_node":"if_gt0"}},"result":true}}},{"id":"sin","summary":"Sine","description":"Computes the sine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"An angle in radians.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed sine of `x`.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/Sine.html","title":"Sine explained by Wolfram MathWorld"}]},{"id":"sinh","summary":"Hyperbolic sine","description":"Computes the hyperbolic sine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.","categories":["math > trigonometric"],"parameters":[{"name":"x","description":"An angle in radians.","schema":{"type":["number","null"]}}],"returns":{"description":"The computed hyperbolic sine of `x`.","schema":{"type":["number","null"]}},"examples":[{"arguments":{"x":0},"returns":0}],"links":[{"rel":"about","href":"http://mathworld.wolfram.com/HyperbolicSine.html","title":"Hyperbolic sine explained by Wolfram MathWorld"}]},{"id":"sort","summary":"Sort data","description":"Sorts an array into ascending (default) or descending order.\\n\\n**Remarks:**\\n\\n* Ties will be left in their original ordering.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone/time-offset representations.","categories":["arrays","sorting"],"parameters":[{"name":"data","description":"An array with data to sort.","schema":{"type":"array","items":{"anyOf":[{"type":"number"},{"type":"null"},{"type":"string","format":"date-time","subtype":"date-time"},{"type":"string","format":"date","subtype":"date"},{"type":"string","format":"time","subtype":"time"}]}}},{"name":"asc","description":"The default sort order is ascending, with smallest values first. To sort in reverse (descending) order, set this parameter to `false`.","schema":{"type":"boolean"},"default":true,"optional":true},{"name":"nodata","description":"Controls the handling of no-data values (`null`). By default, they are removed. 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loading","webpack://@openeo/vue-components/webpack/startup"],"sourcesContent":["var render = function render(){var _vm=this,_c=_vm._self._c;return _c('section',{staticClass:\"vue-component billing-plans\"},[(_vm.heading)?_c(_vm.headingTag,{tag:\"component\"},[_vm._v(_vm._s(_vm.heading))]):_vm._e(),_c('strong',[_vm._v(\"Plans\")]),(_vm.currency !== null && _vm.plans.length)?_c('ul',_vm._l((_vm.plans),function(plan){return _c('li',{key:plan.name},[_c('div',{staticClass:\"plan-header\"},[_c('strong',{staticClass:\"plan-name\"},[(plan.url)?_c('a',{attrs:{\"href\":plan.url,\"target\":\"_blank\"}},[_vm._v(_vm._s(plan.name))]):[_vm._v(_vm._s(plan.name))]],2),_c('ul',{staticClass:\"badges small inline\"},[(_vm.defaultPlan == plan.name)?_c('li',{staticClass:\"badge default\"},[_vm._v(\"default\")]):_vm._e(),(plan.paid === true)?_c('li',{staticClass:\"badge red\"},[_vm._v(\"paid\")]):(plan.paid === false)?_c('li',{staticClass:\"badge green\"},[_vm._v(\"free\")]):_vm._e()])]),(plan.description)?_c('Description',{attrs:{\"description\":plan.description,\"compact\":true}}):_vm._e()],1)}),0):_c('p',[_vm._v(\"No plans available.\")]),_c('div',{staticClass:\"billing-currency\"},[(_vm.currency !== null)?[_c('strong',[_vm._v(\"Currency:\")]),_vm._v(\" \"+_vm._s(_vm.currency)+\" \")]:[_vm._v(\"No billing information provided.\")]],2)],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t{{ heading }} \n\t\tPlans \n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t \n\t\t\t \n\t\t \n\t\tNo plans available.
\n\t\t\n\t\t\t\n\t\t\t\tCurrency: {{ currency }}\n\t\t\t \n\t\t\tNo billing information provided. \n\t\t
\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./BillingPlans.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./BillingPlans.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./BillingPlans.vue?vue&type=template&id=f3e29044\"\nimport script from \"./BillingPlans.vue?vue&type=script&lang=js\"\nexport * from \"./BillingPlans.vue?vue&type=script&lang=js\"\nimport style0 from \"./BillingPlans.vue?vue&type=style&index=0&id=f3e29044&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('article',{staticClass:\"vue-component stac collection\"},[_vm._t(\"title\",function(){return [_c('a',{staticClass:\"anchor\",attrs:{\"name\":_vm.stac.id}}),_c('h2',[_vm._v(_vm._s(_vm.stac.id))])]},null,_vm.$props),(_vm.stac.title)?_c('summary',[_vm._v(_vm._s(_vm.stac.title))]):_vm._e(),(_vm.hasElements(_vm.stac.keywords))?_c('section',{staticClass:\"keywords\"},[_c('ul',{staticClass:\"badges\"},_vm._l((_vm.stac.keywords),function(keyword){return _c('li',{key:keyword,staticClass:\"badge\"},[_vm._v(_vm._s(keyword))])}),0)]):_vm._e(),_vm._t(\"before-description\",null,null,_vm.$props),(_vm.stac.description)?_c('section',{staticClass:\"description\"},[_c('h3',[_vm._v(\"Description\")]),_c('Description',{attrs:{\"description\":_vm.stac.description}}),(_vm.stac.deprecated)?_c('DeprecationNotice',{attrs:{\"entity\":\"collection\"}}):_vm._e(),(_vm.supportedBy)?_c('FederationNotice',{attrs:{\"backends\":_vm.supportedBy,\"federation\":_vm.federation,\"entity\":\"collection\"}}):_vm._e(),(_vm.stac['federation:missing'])?_c('FederationMissing',{attrs:{\"missing\":_vm.stac['federation:missing'],\"federation\":_vm.federation}}):_vm._e()],1):_vm._e(),_c('section',{staticClass:\"license\"},[_c('h3',[_vm._v(\"License\")]),_c('span',{domProps:{\"innerHTML\":_vm._s(_vm.license)}})]),_c('section',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.thumbnails.length),expression:\"thumbnails.length\"}],staticClass:\"preview\"},[_c('h3',[_vm._v(\"Previews\")]),_c('div',{staticClass:\"thumbnails\"},_vm._l((_vm.thumbnails),function(img){return _c('a',{key:img.href,attrs:{\"href\":img.href,\"target\":\"_blank\"}},[_c('img',{attrs:{\"src\":img.href,\"title\":img.title,\"alt\":img.title || 'Preview'}})])}),0)]),(_vm.temporalIntervals.length || _vm.boundingBoxes.length)?_c('section',{staticClass:\"extent\"},[(_vm.boundingBoxes.length)?_c('div',[_c('h3',[_vm._v(\"Spatial Extent\")]),_vm._t(\"spatial-extents\",function(){return [(_vm.worldwide)?_c('span',{staticClass:\"worldwide\"},[_c('i',{staticClass:\"fas fa-globe\"}),_vm._v(\" Worldwide\")]):_c('div',{ref:\"mapContainer\",staticClass:\"map\"},[(!_vm.map)?_vm._l((_vm.boundingBoxes),function(bbox,i){return _c('ul',{key:i},[_c('li',[_vm._v(\"Latitudes: \"+_vm._s(bbox[1])+\" / \"+_vm._s(bbox[3])+\", Longitudes: \"+_vm._s(bbox[0])+\" / \"+_vm._s(bbox[2]))])])}):_vm._e()],2)]},{\"extents\":_vm.boundingBoxes,\"mapOptions\":_vm.mapOptions,\"worldwide\":_vm.worldwide})],2):_vm._e(),(_vm.temporalIntervals.length)?_c('div',[_c('h3',[_vm._v(\"Temporal Extent\")]),_vm._t(\"temporal-extents\",function(){return [(_vm.temporalIntervals.length > 1)?_vm._l((_vm.temporalIntervals),function(interval,i){return _c('ul',{key:i},[(i !== 0)?_c('li',{domProps:{\"innerHTML\":_vm._s(_vm.formatters.formatTemporalExtent(interval))}}):_vm._e()])}):_c('span',{domProps:{\"innerHTML\":_vm._s(_vm.formatters.formatTemporalExtent(_vm.temporalIntervals[0]))}})]},{\"extents\":_vm.temporalIntervals.length > 0})],2):_vm._e()]):_vm._e(),(_vm.hasProviders)?_c('section',{staticClass:\"providers\"},[_c('h3',[_vm._v(\"Providers\")]),_c('ol',_vm._l((_vm.stac.providers),function(provider){return _c('li',{key:provider.name},[(provider.url)?_c('a',{attrs:{\"href\":provider.url,\"target\":\"_blank\"}},[_vm._v(_vm._s(provider.name))]):[_vm._v(_vm._s(provider.name))],(_vm.hasElements(provider.roles))?[_c('ul',{staticClass:\"badges small inline\"},_vm._l((provider.roles),function(role){return _c('li',{key:role,staticClass:\"badge provider-role\"},[_vm._v(_vm._s(role))])}),0)]:_vm._e(),(provider.description)?_c('Description',{attrs:{\"description\":provider.description,\"compact\":true}}):_vm._e()],2)}),0)]):_vm._e(),(_vm.hasDimensions)?_c('section',{staticClass:\"dimensions\"},[_c('h3',[_vm._v(\"Data Cube Dimensions\")]),_c('ul',_vm._l((_vm.stac['cube:dimensions']),function(dim,name){return _c('li',{key:name,staticClass:\"dimension\"},[_c('h4',[(dim.type === 'bands')?_c('a',{staticClass:\"name\",attrs:{\"href\":\"#summary_eo:bands\"},on:{\"click\":_vm.scrollToBands}},[_vm._v(_vm._s(name))]):_c('span',{staticClass:\"name\"},[_vm._v(_vm._s(name))]),_c('ul',{staticClass:\"type badges small inline\"},[_c('li',{staticClass:\"badge\"},[_vm._v(_vm._s(dim.type))])])]),(dim.description)?_c('Description',{attrs:{\"description\":dim.description}}):_vm._e(),(dim.axis)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Axis:\")]),_c('div',{staticClass:\"value\"},[_vm._v(_vm._s(dim.axis))])]):_vm._e(),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Labels:\")]),(dim.extent)?_c('div',{staticClass:\"value\"},[(dim.type === 'temporal')?_c('span',{domProps:{\"innerHTML\":_vm._s(_vm.formatters.formatTemporalExtent(dim.extent))}}):_c('span',{domProps:{\"innerHTML\":_vm._s(_vm.formatters.formatExtent(dim.extent))}})]):(Array.isArray(dim.values) && dim.values.length > 0)?_c('ul',{staticClass:\"value comma-separated-list\"},_vm._l((dim.values),function(value){return _c('li',{key:value},[_vm._v(_vm._s(value))])}),0):_c('div',{staticClass:\"value\"},[_c('i',[_vm._v(\"n/a\")])])]),((typeof dim.step !== 'undefined'))?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Steps:\")]),_c('div',{staticClass:\"value\"},[(dim.step === null)?[_vm._v(\"irregularly spaced\")]:(dim.type === 'temporal')?[_vm._v(_vm._s(_vm.formatDuration(dim.step)))]:[_vm._v(_vm._s(dim.step))]],2)]):_vm._e(),((typeof dim.reference_system !== 'undefined'))?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Reference System:\")]),_c('div',{staticClass:\"value\"},[((typeof dim.reference_system === 'number'))?_c('div',{staticClass:\"epsg\",domProps:{\"innerHTML\":_vm._s(_vm.formatters.formatEPSG(dim.reference_system))}}):((typeof dim.reference_system === 'string'))?_c('div',{staticClass:\"wkt2\",domProps:{\"innerHTML\":_vm._s(_vm.formatters.formatWKT2(dim.reference_system))}}):((typeof dim.reference_system === 'object'))?_c('ObjectTree',{staticClass:\"projjson\",attrs:{\"data\":dim.reference_system}}):[_vm._v(_vm._s(dim.reference_system))]],2)]):_vm._e()],1)}),0)]):_vm._e(),_c('StacFields',{staticClass:\"summaries\",attrs:{\"type\":\"Collection\",\"metadata\":_vm.stac,\"ignore\":_vm.ignoredFields}}),(_vm.hasAssets)?_c('section',{staticClass:\"assets\"},[_c('h3',[_vm._v(\"Assets\")]),_c('ul',{staticClass:\"list\"},_vm._l((_vm.stac.assets),function(asset,id){return _c('StacAsset',{key:id,attrs:{\"asset\":asset,\"id\":id,\"context\":_vm.stac}})}),1)]):_vm._e(),_c('section',{staticClass:\"links\"},[_c('LinkList',{attrs:{\"links\":_vm.stac.links,\"heading\":\"See Also\",\"headingTag\":\"h3\",\"ignoreRel\":['self', 'parent', 'root', 'license', 'cite-as']}})],1),_vm._t(\"end\",null,null,_vm.$props)],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\n\t\t\n\t\t\t \n\t\t\t{{ stac.id }} \n\t\t \n\n\t\t{{ stac.title }} \n\n\t\t\n\t\t\t\n\t\t\t\t{{ keyword }} \n\t\t\t \n\t\t \n\n\t\t \n\n\t\t\n\t\t\tDescription \n\n\t\t\t \n\t\t\t \n\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\t\t\tLicense \n\t\t\t \n\t\t \n\n\t\t\n\t\t\n\t\t\tPreviews \n\t\t\t\n\t\t \n\n\t\t\n\t\t\t\n\t\t\t\t
Spatial Extent \n\t\t\t\t
\n\t\t\t\t\t Worldwide \n\t\t\t\t\t\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tLatitudes: {{ bbox[1] }} / {{ bbox[3] }}, Longitudes: {{ bbox[0] }} / {{ bbox[2] }} \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t
\n\t\t\t\t \n\t\t\t
\n\n\t\t\t\n\t\t\t\t
Temporal Extent \n\t\t\t\t
0\">\n\t\t\t\t\t 1\">\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t
\n\t\t \n\n\t\t\n\t\t\tProviders \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ provider.name }} \n\t\t\t\t\t{{ provider.name }} \n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t{{ role }} \n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\t\t\tData Cube Dimensions \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t{{ name }} \n\t\t\t\t\t\t{{ name }} \n\t\t\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t\n\t\t\t\t\t\t
Axis: \n\t\t\t\t\t\t
{{ dim.axis }}
\n\t\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t
Labels: \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0\" class=\"value comma-separated-list\">\n\t\t\t\t\t\t\t{{ value }} \n\t\t\t\t\t\t \n\t\t\t\t\t\t
n/a
\n\t\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t
Steps: \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tirregularly spaced \n\t\t\t\t\t\t\t{{ formatDuration(dim.step) }} \n\t\t\t\t\t\t\t{{ dim.step }} \n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t
Reference System: \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
{{ dim.reference_system }} \n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t \n\t\t\t \n\t\t \n\n\t\t \n\n\t\t\n\t\t\tAssets \n\t\t\t\n\t\t \n\n\t\t\n\n\t\t \n\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Collection.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Collection.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Collection.vue?vue&type=template&id=e85270a2\"\nimport script from \"./Collection.vue?vue&type=script&lang=js\"\nexport * from \"./Collection.vue?vue&type=script&lang=js\"\nimport style0 from \"./Collection.vue?vue&type=style&index=0&id=e85270a2&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component message-block deprecation-notice\"},[_c('strong',[_vm._v(\"Deprecated\")]),_c('p',[_vm._v(\"Please note that this \"+_vm._s(_vm.entity)+\" is now deprecated with the potential to be removed in any of the next versions. It should be transitioned out of usage as soon as possible. Refrain from using it in new implementations.\")])])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
Deprecated \n\t\t
Please note that this {{entity}} is now deprecated with the potential to be removed in any of the next versions. It should be transitioned out of usage as soon as possible. Refrain from using it in new implementations.
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./DeprecationNotice.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./DeprecationNotice.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./DeprecationNotice.vue?vue&type=template&id=00a47f2c\"\nimport script from \"./DeprecationNotice.vue?vue&type=script&lang=js\"\nexport * from \"./DeprecationNotice.vue?vue&type=script&lang=js\"\nimport style0 from \"./DeprecationNotice.vue?vue&type=style&index=0&id=00a47f2c&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component styled-description\",class:{compact: _vm.compact},domProps:{\"innerHTML\":_vm._s(_vm.markup(_vm.description))}})\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t
\n \n\n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Description.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Description.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Description.vue?vue&type=template&id=d6a0afce\"\nimport script from \"./Description.vue?vue&type=script&lang=js\"\nexport * from \"./Description.vue?vue&type=script&lang=js\"\nimport style0 from \"./Description.vue?vue&type=style&index=0&id=d6a0afce&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component message-block experimental-notice\"},[_c('strong',[_vm._v(\"Experimental\")]),_c('p',[_vm._v(\"Please note that this \"+_vm._s(_vm.entity)+\" is experimental with the potential for major things to change. Feel encouraged to try it out and give feedback, but refrain from using it in production.\")])])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
Experimental \n\t\t
Please note that this {{entity}} is experimental with the potential for major things to change. Feel encouraged to try it out and give feedback, but refrain from using it in production.
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ExperimentalNotice.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ExperimentalNotice.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./ExperimentalNotice.vue?vue&type=template&id=8040f87e\"\nimport script from \"./ExperimentalNotice.vue?vue&type=script&lang=js\"\nexport * from \"./ExperimentalNotice.vue?vue&type=script&lang=js\"\nimport style0 from \"./ExperimentalNotice.vue?vue&type=style&index=0&id=8040f87e&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return (_vm.services)?_c('section',{staticClass:\"vue-component message-block federation federation-backends\"},[(_vm.retry)?_c('button',{staticClass:\"retry\",attrs:{\"type\":\"button\"},on:{\"click\":_vm.retry}},[_vm._t(\"button-text\",function(){return [_vm._v(\"Retry\")]})],2):_vm._e(),_c('strong',{staticClass:\"header\"},[_vm._v(\"Incomplete\")]),_c('p',[_vm._v(\" The following list is incomplete as at least one of the services in the federation is currently not available. The data for the following services is missing: \"+_vm._s(_vm.services.join(', '))+\" \")])]):_vm._e()\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t\n\t\t\tRetry \n\t\t \n\t\t\n\t\t\n\t\t\tThe following list is incomplete as at least one of the services in the federation is currently not available.\n\t\t\tThe data for the following services is missing: {{ services.join(', ') }}\n\t\t
\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./FederationMissingNotice.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./FederationMissingNotice.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./FederationMissingNotice.vue?vue&type=template&id=b38c1fb4\"\nimport script from \"./FederationMissingNotice.vue?vue&type=script&lang=js\"\nexport * from \"./FederationMissingNotice.vue?vue&type=script&lang=js\"\nimport style0 from \"./FederationMissingNotice.vue?vue&type=style&index=0&id=b38c1fb4&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('article',{staticClass:\"vue-component file-format\"},[_vm._t(\"title\",function(){return [_c('a',{staticClass:\"anchor\",attrs:{\"name\":_vm.id}}),_c('h2',[(_vm.format.title)?[_vm._v(\" \"+_vm._s(_vm.format.title)+\" (\"),_c('code',{staticClass:\"id\"},[_vm._v(_vm._s(_vm._f(\"abbrev\")(_vm.id)))]),_vm._v(\") \")]:_c('code',{staticClass:\"id\"},[_vm._v(_vm._s(_vm._f(\"abbrev\")(_vm.id)))])],2)]},null,_vm.$props),_vm._t(\"badges\",function(){return [_c('ul',{staticClass:\"badges\"},[(_vm.type === 'input')?_c('li',{staticClass:\"badge option1\"},[_vm._v(\"Import\")]):_vm._e(),(_vm.type === 'output')?_c('li',{staticClass:\"badge option2\"},[_vm._v(\"Export\")]):_vm._e(),(Array.isArray(_vm.format.gis_data_types))?_vm._l((_vm.format.gis_data_types),function(gisType){return _c('li',{key:gisType,staticClass:\"badge gis\"},[_vm._v(_vm._s(gisType))])}):_vm._e()],2)]},null,_vm.$props),_vm._t(\"before-description\",null,null,_vm.$props),(_vm.format.description)?_c('section',{staticClass:\"description\"},[_c('h3',[_vm._v(\"Description\")]),_c('Description',{attrs:{\"description\":_vm.format.description}}),(_vm.format.deprecated)?_c('DeprecationNotice',{attrs:{\"entity\":\"file format\"}}):_vm._e(),(_vm.format.experimental)?_c('ExperimentalNotice',{attrs:{\"entity\":\"file format\"}}):_vm._e(),(_vm.format['federation:backends'])?_c('FederationNotice',{attrs:{\"backends\":_vm.format['federation:backends'],\"federation\":_vm.federation,\"entity\":\"file format\"}}):_vm._e()],1):_vm._e(),_c('section',{staticClass:\"parameters\"},[_c('h3',[_vm._v(\"Parameters\")]),_vm._l((_vm.parameters),function(param){return _c('ProcessParameter',{key:param.name,attrs:{\"parameter\":param,\"federation\":_vm.federation}})}),(_vm.parameters.length === 0)?_c('p',[_vm._v(\"This file format has no parameters.\")]):_vm._e()],2),_c('section',{staticClass:\"links\"},[_c('LinkList',{attrs:{\"links\":_vm.format.links,\"heading\":\"See Also\",\"headingTag\":\"h3\"}})],1),_vm._t(\"end\",null,null,_vm.$props)],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\n\t\t\n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ format.title }}\n\t\t\t\t\t({{ id | abbrev }}
)\n\t\t\t\t \n\t\t\t\t{{ id | abbrev }}
\n\t\t\t \n\t\t \n\n\t\t\n\t\t\t\n\t\t\t\tImport \n\t\t\t\tExport \n\t\t\t\t\n\t\t\t\t\t{{ gisType }} \n\t\t\t\t \n\t\t\t \n\t\t \n\n\t\t \n\n\t\t\n\t\t\tDescription \n\t\t\t \n\t\t\t \n\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\t\t\tParameters \n\t\t\t \n\t\t\tThis file format has no parameters.
\n\t\t \n\n\t\t\n\n\t\t \n\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./FileFormat.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./FileFormat.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./FileFormat.vue?vue&type=template&id=a3db04b4\"\nimport script from \"./FileFormat.vue?vue&type=script&lang=js\"\nexport * from \"./FileFormat.vue?vue&type=script&lang=js\"\nimport style0 from \"./FileFormat.vue?vue&type=style&index=0&id=a3db04b4&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('article',{staticClass:\"vue-component stac item\"},[_vm._t(\"title\",function(){return [_c('a',{staticClass:\"anchor\",attrs:{\"name\":_vm.stac.id}}),_c('h2',[_vm._v(_vm._s(_vm.title))])]},null,_vm.$props),_vm._t(\"before-description\",null,null,_vm.$props),(_vm.properties.description)?_c('summary',{staticClass:\"description\"},[_c('Description',{attrs:{\"description\":_vm.properties.description}}),(_vm.properties.deprecated)?_c('DeprecationNotice',{attrs:{\"entity\":\"item\"}}):_vm._e(),(_vm.stac['federation:missing'])?_c('FederationMissingNotice',{attrs:{\"missing\":_vm.stac['federation:missing'],\"federation\":_vm.federation}}):_vm._e()],1):_vm._e(),(_vm.thumbnails.length)?_c('section',{staticClass:\"preview\"},[_c('h3',[_vm._v(\"Previews\")]),_c('div',{staticClass:\"thumbnails\"},_vm._l((_vm.thumbnails),function(img){return _c('a',{key:img.href,attrs:{\"href\":img.href,\"target\":\"_blank\"}},[_c('img',{attrs:{\"src\":img.href,\"title\":img.title,\"alt\":img.title || 'Preview'}})])}),0)]):_vm._e(),_c('section',{staticClass:\"metadata properties\"},[(_vm.thumbnails.length)?_c('h3',[_vm._v(\"Metadata\")]):_vm._e(),(_vm.stac.geometry || Array.isArray(_vm.stac.bbox))?_vm._t(\"location\",function(){return [_c('div',{staticClass:\"tabular wrap\"},[_c('label',[_vm._v(\"Location\")]),_c('div',{ref:\"mapContainer\",staticClass:\"value map\"},[(!_vm.map)?[_vm._v(\" Latitudes: \"+_vm._s(_vm.stac.bbox[1])+\" / \"+_vm._s(_vm.stac.bbox[3])+\", Longitudes: \"+_vm._s(_vm.stac.bbox[0])+\" / \"+_vm._s(_vm.stac.bbox[2])+\" \")]:_vm._e()],2)])]},{\"geometry\":_vm.stac.geometry,\"bbox\":_vm.stac.bbox,\"mapOptions\":_vm.mapOptions}):_vm._e(),_c('StacFields',{attrs:{\"type\":\"Item\",\"metadata\":_vm.stac,\"headingTag\":\"h4\",\"ignore\":_vm.ignoredFields}})],2),(_vm.hasAssets)?_c('section',{staticClass:\"assets\"},[_c('h3',[_vm._v(\"Assets\")]),_c('ul',{staticClass:\"list\"},_vm._l((_vm.stac.assets),function(asset,id){return _c('StacAsset',{key:id,attrs:{\"asset\":asset,\"id\":id,\"context\":_vm.stac}})}),1)]):_vm._e(),_c('section',{staticClass:\"links\"},[_c('LinkList',{attrs:{\"links\":_vm.stac.links,\"heading\":\"See Also\",\"headingTag\":\"h3\",\"ignoreRel\":['self', 'parent', 'root', 'license', 'cite-as']}})],1),_vm._t(\"end\",null,null,_vm.$props)],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\n\t\t\n\t\t\t \n\t\t\t{{ title }} \n\t\t \n\n\t\t \n\n\t\t\n\t\t\t \n\n\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\t\t\tPreviews \n\t\t\t\n\t\t \n\n\t\t\n\n\t\t\n\t\t\tAssets \n\t\t\t\n\t\t \n\n\t\t\n\n\t\t \n\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Item.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Item.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Item.vue?vue&type=template&id=363b6de2\"\nimport script from \"./Item.vue?vue&type=script&lang=js\"\nexport * from \"./Item.vue?vue&type=script&lang=js\"\nimport style0 from \"./Item.vue?vue&type=style&index=0&id=363b6de2&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return (_vm.showSchema)?_c('div',{staticClass:\"vue-component json-schema\",class:{composite: _vm.compositeTypes.length > 1}},[(_vm.visible)?[(_vm.isProcessGraph)?_c('div',{staticClass:\"schemaProcessGraph\"},[_c('div',{staticClass:\"process-graph-parameters\"},[_c('p',{staticClass:\"schema-attrs\"},[_vm._v(_vm._s(_vm.formatKey('type'))+\": \"),_c('span',{staticClass:\"data-type\"},[_vm._v(\"User-defined Process (process-graph:object)\")])]),_vm._m(0),(_vm.hasParameters)?_vm._l((_vm.schema.parameters),function(param,i){return _c('ProcessParameter',{key:i,attrs:{\"parameter\":param,\"processUrl\":_vm.processUrl}})}):_c('p',[_vm._v(\"No parameters defined.\")]),_vm._m(1),(_vm.hasReturns)?[(_vm.schema.returns.description)?_c('Description',{attrs:{\"description\":_vm.schema.returns.description,\"processUrl\":_vm.processUrl}}):_vm._e(),(_vm.schema.returns.schema)?_c('div',{staticClass:\"json-schema-container\"},[_c('openeo-json-schema',{attrs:{\"schema\":_vm.schema.returns.schema}})],1):_vm._e()]:_c('p',[_vm._v(\"No constraints defined.\")])],2)]):(_vm.showRow('object'))?_c('div',{staticClass:\"schemaObjectElement\"},[_c('div',{staticClass:\"inline-schema-attrs\"},[(_vm.filteredObjectSchema !== null)?_c('openeo-json-schema',{attrs:{\"schema\":_vm.filteredObjectSchema,\"nestingLevel\":_vm.nestingLevel+1}}):_vm._e(),_c('table',{staticClass:\"object-properties\"},[_vm._m(2),_vm._l((_vm.schema.properties),function(val,key){return _c('tr',{key:key},[_c('td',{staticClass:\"key\"},[_vm._v(\" \"+_vm._s(key)+\" \"),(_vm.schema.required && _vm.schema.required.indexOf(key) !== -1)?_c('strong',{staticClass:\"required\",attrs:{\"title\":\"required\"}},[_vm._v(\"*\")]):_vm._e()]),_c('td',{staticClass:\"value\"},[_c('openeo-json-schema',{attrs:{\"schema\":val,\"nestingLevel\":_vm.nestingLevel+1,\"processUrl\":_vm.processUrl}})],1)])})],2)],1)]):_c('table',{staticClass:\"schema-attrs\"},[(typeof _vm.schema.title == 'string')?_c('tr',[_c('td',{attrs:{\"colspan\":\"2\"}},[_c('strong',[_vm._v(_vm._s(_vm.schema.title))])])]):_vm._e(),(typeof _vm.schema.description == 'string')?_c('tr',[_c('td',{attrs:{\"colspan\":\"2\"}},[_c('Description',{attrs:{\"description\":_vm.schema.description,\"compact\":true}})],1)]):_vm._e(),(_vm.showAnyType)?_c('tr',[_c('td',{staticClass:\"key\"},[_vm._v(_vm._s(_vm.formatKey('type'))+\":\")]),_c('td',{staticClass:\"value data-type\"},[_vm._v(\"any\")])]):(_vm.compositeTypes.length > 1)?[_vm._m(3),_c('tr',[_c('td',{staticClass:\"schema-container data-types-container\",attrs:{\"colspan\":\"2\"}},_vm._l((_vm.compositeTypes),function(v,k){return _c('openeo-json-schema',{key:k,attrs:{\"schema\":v,\"nestingLevel\":_vm.nestingLevel+1,\"processUrl\":_vm.processUrl}})}),1)])]:_vm._e(),(!Array.isArray(this.schema))?[_vm._l((_vm.schema),function(val,key){return [(typeof val !== 'undefined' && _vm.showRow(key))?_c('tr',{key:key},[_c('td',{staticClass:\"key\"},[_vm._v(_vm._s(_vm.formatKey(key))+\":\")]),_c('td',{staticClass:\"value\"},[(key == 'type')?_c('span',{staticClass:\"data-type\"},[_vm._v(_vm._s(_vm.formatType()))]):(key == 'allOf' && Array.isArray(val))?_c('div',{staticClass:\"schema-container\"},_vm._l((val),function(v,k){return _c('openeo-json-schema',{key:k,attrs:{\"schema\":v,\"nestingLevel\":_vm.nestingLevel+1,\"processUrl\":_vm.processUrl}})}),1):(key != 'const' && key != 'default' && key != 'examples' && val === true)?_c('span',{attrs:{\"title\":\"true\"}},[_vm._v(\"✓ Yes\")]):(key != 'const' && key != 'default' && key != 'examples' && val === false)?_c('span',{attrs:{\"title\":\"false\"}},[_vm._v(\"✕ No\")]):(key != 'examples' && Array.isArray(val))?_c('ul',{staticClass:\"comma-separated-list\"},_vm._l((val),function(v,k){return _c('li',{key:k},[_vm._v(_vm._s(v))])}),0):(key == 'examples' && Array.isArray(val) && val.length > 1)?_c('ul',_vm._l((val),function(v,k){return _c('li',{key:k},[_c('code',[_vm._v(_vm._s(v))])])}),0):(key == 'examples' && Array.isArray(val) && val.length === 1)?_c('code',[_vm._v(_vm._s(val[0]))]):(key == 'description')?_c('Description',{attrs:{\"description\":val,\"compact\":true}}):(key == 'default' && val === '')?_c('em',[_vm._v(\"Empty string\")]):(key == 'default' && (typeof val === 'object' || typeof val === 'boolean'))?_c('code',[_vm._v(_vm._s(JSON.stringify(val)))]):(key == 'pattern' && typeof val === 'string')?_c('code',[_vm._v(_vm._s(_vm._f(\"regex\")(val)))]):(typeof val === 'object')?_c('openeo-json-schema',{attrs:{\"schema\":val,\"initShown\":_vm.nestingLevel < 3,\"nestingLevel\":_vm.nestingLevel+1,\"processUrl\":_vm.processUrl}}):_c('span',[_vm._v(_vm._s(val))])],1)]):_vm._e()]})]:_vm._e()],2)]:_c('div',{staticClass:\"schema-expand\"},[_c('a',{on:{\"click\":function($event){return _vm.show()}}},[_vm._v(\"> ...\")])])],2):_vm._e()\n}\nvar staticRenderFns = [function (){var _vm=this,_c=_vm._self._c;return _c('p',{staticClass:\"schema-attrs\",attrs:{\"title\":\"The parameters that can be used in the process.\"}},[_c('strong',[_vm._v(\"Parameters:\")])])\n},function (){var _vm=this,_c=_vm._self._c;return _c('p',{staticClass:\"schema-attrs\",attrs:{\"title\":\"Describes what must be returned by the process.\"}},[_c('strong',[_vm._v(\"Expected Return Value:\")])])\n},function (){var _vm=this,_c=_vm._self._c;return _c('tr',[_c('th',{staticClass:\"object-prop-heading\",attrs:{\"colspan\":\"2\"}},[_vm._v(\"Object Properties:\")])])\n},function (){var _vm=this,_c=_vm._self._c;return _c('tr',[_c('th',{staticClass:\"data-types-heading\",attrs:{\"colspan\":\"2\"}},[_vm._v(\"Data Types:\")])])\n}]\n\nexport { render, staticRenderFns }","\n\t 1}\" v-if=\"showSchema\">\n\t\t
\n\t\t\t\n\t\t\t\t
\n\t\t\t\t\t
{{ formatKey('type') }}: User-defined Process (process-graph:object)
\n\t\t\t\t\t
\n\t\t\t\t\t\tParameters: \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t
No parameters defined.
\n\t\t\t\t\t
\n\t\t\t\t\t\tExpected Return Value: \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t \n\t\t\t\t\t\t
\n\t\t\t\t\t \n\t\t\t\t\t
No constraints defined.
\n\t\t\t\t
\n\t\t\t
\n\t\t\t\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tObject Properties: \n\t\t\t\t\t\t \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{{ key }}\n\t\t\t\t\t\t\t\t* \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t
\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ schema.title }} \n\t\t\t\t \n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t \n\t\t\t\t\n\t\t\t\t\t{{ formatKey('type') }}: \n\t\t\t\t\tany \n\t\t\t\t \n\t\t\t\t 1\">\n\t\t\t\t\t\n\t\t\t\t\t\tData Types: \n\t\t\t\t\t \n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t{{ formatKey(key) }}: \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{{ formatType() }} \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t✓ Yes \n\t\t\t\t\t\t\t\t✕ No \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t{{ v }} \n\t\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t\t 1\">\n\t\t\t\t\t\t\t\t\t{{ v }}
\n\t\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t\t{{ val[0] }}
\n\t\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t\tEmpty string \n\t\t\t\t\t\t\t\t{{ JSON.stringify(val) }}
\n\t\t\t\t\t\t\t\t{{ val | regex }}
\n\t\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t\t{{ val }} \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t
\n\t\t \n\t\t
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./JsonSchema.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./JsonSchema.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./JsonSchema.vue?vue&type=template&id=55b843fc\"\nimport script from \"./JsonSchema.vue?vue&type=script&lang=js\"\nexport * from \"./JsonSchema.vue?vue&type=script&lang=js\"\nimport style0 from \"./JsonSchema.vue?vue&type=style&index=0&id=55b843fc&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return (_vm.friendlyLinks.length > 0)?_c('div',{staticClass:\"vue-component link-list\"},[(_vm.heading)?_c(_vm.headingTag,{tag:\"component\"},[_vm._v(_vm._s(_vm.heading))]):_vm._e(),_c('ul',_vm._l((_vm.friendlyLinks),function(link,i){return _c('li',{key:i},[_c('a',{attrs:{\"href\":link.href,\"target\":\"_blank\",\"rel\":link.rel}},[_vm._v(_vm._s(link.title))]),(_vm.showRel && link.rel)?_c('span',{staticClass:\"relation\"},[_vm._v(\" (\"+_vm._s(link.rel)+\")\")]):_vm._e()])}),0)],1):_vm._e()\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t 0\">\n\t\t
{{ heading }} \n\t\t
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./LinkList.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./LinkList.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./LinkList.vue?vue&type=template&id=1f1a4084\"\nimport script from \"./LinkList.vue?vue&type=script&lang=js\"\nexport * from \"./LinkList.vue?vue&type=script&lang=js\"\nimport style0 from \"./LinkList.vue?vue&type=style&index=0&id=1f1a4084&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{ref:\"div\",class:_vm.classes,style:(_vm.style),attrs:{\"id\":_vm.id,\"tabindex\":\"0\"},on:{\"mousemove\":_vm.onMouseMove,\"mousedown\":_vm.onMouseDown,\"wheel\":_vm.onMouseWheel,\"keydown\":_vm.onKeyDown,\"focus\":function($event){_vm.hasFocus = true},\"blur\":function($event){_vm.hasFocus = false}}},[_c('svg',{staticClass:\"canvas\",attrs:{\"xmlns\":\"http://www.w3.org/2000/svg\",\"version\":\"1.1\"}},[_vm._l((_vm.edges),function(edge){return _c('Edge',{key:edge.id,attrs:{\"id\":edge.id,\"parameter1\":edge.parameter1,\"parameter2\":edge.parameter2,\"selected\":edge.selected,\"inactive\":edge.inactive,\"issues\":edge.issues,\"state\":_vm.state},on:{\"mounted\":node => _vm.mount(edge, node),\"unmounted\":() => _vm.mount(edge),\"position\":function($event){return _vm.updateEdgePos(edge, arguments)}}})}),_vm._l((_vm.hiddenParameterRefEdges),function(edge){return _c('Edge',{key:edge.id,attrs:{\"id\":edge.id,\"parameter1\":edge.parameter1,\"parameter2\":edge.parameter2,\"inactive\":true,\"lineColor\":[200,200,200,1],\"lineWidth\":2,\"state\":_vm.state},on:{\"mounted\":node => _vm.mount(edge, node),\"unmounted\":() => _vm.mount(edge)}})}),(_vm.linkingLine)?_c('line',_vm._b({},'line',_vm.linkingLine,false)):_vm._e(),(_vm.selectRect)?_c('rect',_vm._b({},'rect',_vm.selectRect,false)):_vm._e()],2),_c('div',{staticClass:\"blocks\"},_vm._l((_vm.blocks),function(block){return _c('Block',{key:block.id,attrs:{\"id\":block.id,\"type\":block.type,\"spec\":block.spec,\"state\":_vm.state,\"selected\":block.selected,\"position\":block.position,\"origin\":block.origin,\"process_id\":block.process_id,\"namespace\":block.namespace,\"result\":block.result,\"args\":block.arguments,\"description\":block.description},on:{\"update\":(...args) => _vm.updateBlock(block, ...args),\"mounted\":node => _vm.mount(block, node),\"unmounted\":() => _vm.mount(block),\"move\":_vm.startDragBlock}})}),1),(_vm.state.scale < 0.7 || _vm.showZoomInfo)?_c('div',{staticClass:\"zoomInfo\"},[(_vm.state.scale < 0.7)?_c('div',[_vm._v(\" Zoom in for more details. \")]):_vm._e(),(_vm.showZoomInfo)?_c('div',[_vm._v(\" Zoom with \"),_c('kbd',[_vm._v(\"STRG\")]),_vm._v(\" or \"),_c('kbd',[_vm._v(\"Meta\")]),_vm._v(\" key and the mouse wheel. \")]):_vm._e()]):_vm._e(),(_vm.parameterViewer)?_c('ParameterViewer',_vm._b({on:{\"close\":function($event){_vm.parameterViewer = null}}},'ParameterViewer',_vm.parameterViewer,false)):_vm._e()],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{ref:\"div\",class:_vm.containerClasses,style:(_vm.styles),attrs:{\"id\":'block' + this.id},on:{\"mousedown\":function($event){if(!$event.type.indexOf('key')&&_vm._k($event.keyCode,\"left\",37,$event.key,[\"Left\",\"ArrowLeft\"]))return null;$event.preventDefault();$event.stopPropagation();if('button' in $event && $event.button !== 0)return null;return _vm.onMouseDown.apply(null, arguments)}}},[_c('div',{staticClass:\"blockTitle\",on:{\"mousedown\":function($event){if(!$event.type.indexOf('key')&&_vm._k($event.keyCode,\"left\",37,$event.key,[\"Left\",\"ArrowLeft\"]))return null;$event.preventDefault();$event.stopPropagation();if('button' in $event && $event.button !== 0)return null;return _vm.emitDrag.apply(null, arguments)}}},[_c('span',{staticClass:\"titleText\",attrs:{\"title\":_vm.plainTitle}},[_c('span',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.invalid),expression:\"invalid\"}],staticClass:\"invalid\",attrs:{\"title\":\"Process or Collection not supported!\"}},[_c('i',{staticClass:\"fas fa-exclamation-triangle\"})]),_vm._v(\" \"+_vm._s(_vm.name)+\" \"),(_vm.showId)?_c('span',{staticClass:\"blockId\"},[_vm._v(_vm._s(_vm.id))]):_vm._e()]),_c('div',{staticClass:\"blockicon\",on:{\"mousedown\":function($event){if(!$event.type.indexOf('key')&&_vm._k($event.keyCode,\"left\",37,$event.key,[\"Left\",\"ArrowLeft\"]))return null;$event.preventDefault();$event.stopPropagation();if('button' in $event && $event.button !== 0)return null;return _vm.focus()}}},[_c('span',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.allowsDescription && !_vm.showDescriptionField),expression:\"allowsDescription && !showDescriptionField\"}],staticClass:\"addDescription\",attrs:{\"title\":\"Add description\"},on:{\"click\":function($event){$event.stopPropagation();$event.preventDefault();return _vm.addDescription()}}},[_c('i',{staticClass:\"fas fa-comment-medical\"})]),_c('span',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.allowsDuplicate),expression:\"allowsDuplicate\"}],staticClass:\"duplicate\",attrs:{\"title\":\"Duplicate\"},on:{\"click\":function($event){$event.stopPropagation();$event.preventDefault();return _vm.duplicate.apply(null, arguments)}}},[_c('i',{staticClass:\"fas fa-clone\"})]),_c('span',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.allowsDelete),expression:\"allowsDelete\"}],staticClass:\"delete\",attrs:{\"title\":\"Remove (DEL)\"},on:{\"click\":function($event){$event.stopPropagation();$event.preventDefault();return _vm.remove()}}},[_c('i',{staticClass:\"fas fa-trash\"})]),_c('span',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.allowsInfo),expression:\"allowsInfo\"}],staticClass:\"info\",attrs:{\"title\":\"Details\"},on:{\"click\":function($event){$event.stopPropagation();$event.preventDefault();return _vm.showInfo()}}},[_c('i',{staticClass:\"fas fa-info\"})]),_c('span',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.allowsParameterChange),expression:\"allowsParameterChange\"}],staticClass:\"settings\",attrs:{\"title\":_vm.isParameter ? 'Edit parameter' : 'Change parameter values'},on:{\"click\":function($event){$event.stopPropagation();$event.preventDefault();return _vm.edit.apply(null, arguments)}}},[_c('i',{staticClass:\"fas fa-edit\"})])])]),_c('div',{staticClass:\"inout\"},[_c('div',{staticClass:\"inputs\"},_vm._l((_vm.parameters),function(param,i){return _c('BlockParameter',{key:i,ref:\"parameters\",refInFor:true,attrs:{\"state\":_vm.state,\"value\":_vm.args[param.name],\"name\":param.name,\"description\":param.description,\"optional\":param.optional,\"deprecated\":param.deprecated,\"experimental\":param.experimental,\"default\":param.default,\"schema\":param.schema},on:{\"input\":value => _vm.updateArgument(param.name, value),\"edgesChanged\":function($event){return _vm.edgesChanged(param, $event)}}})}),1),_c('div',{staticClass:\"outputs\"},[_c('BlockParameter',_vm._b({ref:\"output\",attrs:{\"state\":_vm.state,\"label\":_vm.outputLabel},on:{\"input\":_vm.updateResult}},'BlockParameter',_vm.output,false))],1)]),(_vm.showDescriptionField)?_c('textarea',{ref:\"descriptionField\",staticClass:\"editDescription\",attrs:{\"readonly\":!_vm.state.editable,\"placeholder\":\"Type description here...\"},domProps:{\"value\":_vm.description},on:{\"input\":_vm.updateDescription,\"blur\":_vm.updateDescription,\"mousedown\":function($event){$event.stopPropagation();}}}):_vm._e()])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{class:_vm.classes},[(!_vm.output)?_c('div',_vm._g({ref:\"circle\",class:_vm.circleClasses},_vm.circleListeners)):_vm._e(),_c('span',_vm._g({staticClass:\"text\"},_vm.textListeners),[_c('span',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.unspecified),expression:\"unspecified\"}],staticClass:\"unspecified\",attrs:{\"title\":\"Parameter is likely unsupported!\"}},[_c('i',{staticClass:\"fas fa-exclamation-triangle\"})]),_c('span',{staticClass:\"label\"},[_vm._v(_vm._s(_vm.displayLabel))]),(_vm.displayValue.length)?[_vm._v(\": \")]:_vm._e(),_c('span',{staticClass:\"value\",domProps:{\"innerHTML\":_vm._s(_vm.displayValue)}})],2),(_vm.output)?_c('div',_vm._g({ref:\"circle\",class:_vm.circleClasses,attrs:{\"title\":\"Output of the process\"}},_vm.circleListeners)):_vm._e()])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n \n
\n
\n \n \n \n {{ displayLabel }} : \n \n \n
\n
\n \n\n\n\n","import mod from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./BlockParameter.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./BlockParameter.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./BlockParameter.vue?vue&type=template&id=b7329df8\"\nimport script from \"./BlockParameter.vue?vue&type=script&lang=js\"\nexport * from \"./BlockParameter.vue?vue&type=script&lang=js\"\nimport style0 from \"./BlockParameter.vue?vue&type=style&index=0&id=b7329df8&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","export default {\n\tdefaultScale: 1.3,\n\tblockWidth: {\n\t\tcompactParams: 110,\n\t\tcompact: 60,\n\t\tnormalParams: 220,\n\t\tnormal: 110\n\t}\n};","\n \n
\n
\n \n \n \n {{ name }}\n {{ id }} \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n
\n
\n
\n updateArgument(param.name, value)\" @edgesChanged=\"edgesChanged(param, $event)\" :name=\"param.name\" :description=\"param.description\" :optional=\"param.optional\" :deprecated=\"param.deprecated\" :experimental=\"param.experimental\" :default=\"param.default\" :schema=\"param.schema\" />\n
\n
\n \n
\n
\n
\n
\n \n\n\n\n","import mod from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Block.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Block.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Block.vue?vue&type=template&id=738e9c36\"\nimport script from \"./Block.vue?vue&type=script&lang=js\"\nexport * from \"./Block.vue?vue&type=script&lang=js\"\nimport style0 from \"./Block.vue?vue&type=style&index=0&id=738e9c36&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('g',_vm._l((_vm.lines),function(line,i){return _c('line',_vm._b({key:i},'line',line,false))}),0)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","export default class Segment {\n\n constructor(x, y, dx, dy) {\n this.x = x;\n this.y = y;\n this.dx = dx;\n this.dy = dy;\n }\n\n /**\n * Distance\n */\n distance(point1, point2) {\n return Math.sqrt(Math.pow(point2.x-point1.x,2) + Math.pow(point2.y-point1.y,2));\n }\n\n /**\n * Distance with a point\n */\n distanceP(point) {\n var normal = this.normal();\n normal.x = point.x;\n normal.y = point.y;\n var intersection = this.intersection(normal);\n\n return [intersection[0], this.distance(normal.alpha(intersection[1]), point)];\n }\n\n /**\n * Normal\n */\n normal() {\n return new Segment(this.x, this.y, this.dy, -this.dx);\n }\n\n /**\n * Gets the intersection alpha with another \n */\n intersection(other) {\n var a = this.dx;\n var b = -other.dx;\n var c = this.dy;\n var d = -other.dy;\n var b0 = other.x-this.x;\n var b1 = other.y-this.y;\n var det = a*d - b*c;\n\n if (det == 0) {\n return null;\n }\n\n var r1 = (d*b0 - b*b1)/det;\n var r2 = (-c*b0 + a*b1)/det;\n\n return [r1, r2];\n }\n\n /**\n * Gets the alpha point\n */\n alpha(a) {\n var point = {};\n point.x = this.x+this.dx*a;\n point.y = this.y+this.dy*a;\n\n return point;\n }\n\n}","\n \n \n \n \n\n","import mod from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Edge.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Edge.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Edge.vue?vue&type=template&id=25fda632\"\nimport script from \"./Edge.vue?vue&type=script&lang=js\"\nexport * from \"./Edge.vue?vue&type=script&lang=js\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","\n \n \n
\n mount(edge, node)\" @unmounted=\"() => mount(edge)\"\n @position=\"updateEdgePos(edge, arguments)\" />\n mount(edge, node)\" @unmounted=\"() => mount(edge)\"\n :inactive=\"true\" :lineColor=\"[200,200,200,1]\" :lineWidth=\"2\" :state=\"state\" />\n \n \n \n
\n updateBlock(block, ...args)\"\n @mounted=\"node => mount(block, node)\" @unmounted=\"() => mount(block)\"\n @move=\"startDragBlock\" />\n
\n
\n
\n Zoom in for more details.\n
\n
\n Zoom with STRG or Meta key and the mouse wheel.\n
\n
\n
\n
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ModelBuilder.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ModelBuilder.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./ModelBuilder.vue?vue&type=template&id=cfd84820\"\nimport script from \"./ModelBuilder.vue?vue&type=script&lang=js\"\nexport * from \"./ModelBuilder.vue?vue&type=script&lang=js\"\nimport style0 from \"./ModelBuilder.vue?vue&type=style&index=0&id=cfd84820&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component object-tree\",class:{inline: _vm.size === 0}},[(_vm.size === 0)?_c('em',[_vm._v(_vm._s(_vm.format(_vm.data)))]):(Array.isArray(_vm.data))?[_c('ol',_vm._l((_vm.indicesShown),function(i){return _c('li',{key:i},[(_vm.isStructured(_vm.data[i]))?_c('openeo-object-tree',{attrs:{\"data\":_vm.data[i]}}):(_vm.isUrl(_vm.data[i]))?_c('a',{attrs:{\"href\":_vm.data[i],\"target\":\"_blank\"}},[_vm._v(_vm._s(_vm.data[i]))]):(_vm.format(_vm.data[i]))?_c('em',[_vm._v(_vm._s(_vm.format(_vm.data[i])))]):[_vm._v(_vm._s(_vm.data[i]))]],2)}),0),(_vm.size !== _vm.indicesShown.length)?_c('button',{attrs:{\"type\":\"button\"},on:{\"click\":_vm.show}},[_vm._v(\"Show all \"+_vm._s(_vm.data.length)+\" entries\")]):_vm._e()]:(typeof _vm.data === 'object')?_c('ul',_vm._l((_vm.data),function(value,key){return _c('li',{key:key},[_c('strong',[_vm._v(_vm._s(_vm.prettifyKey(key)))]),_vm._v(\": \"),(_vm.isStructured(value))?_c('openeo-object-tree',{attrs:{\"data\":value}}):(_vm.isUrl(value))?_c('a',{attrs:{\"href\":value,\"target\":\"_blank\"}},[_vm._v(_vm._s(value))]):(_vm.format(value))?_c('em',[_vm._v(_vm._s(_vm.format(value)))]):[_vm._v(_vm._s(value))]],2)}),0):[_vm._v(_vm._s(_vm.data))]],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
{{ format(data) }} \n\t\t
\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t\t{{ data[i] }} \n\t\t\t\t\t{{ format(data[i]) }} \n\t\t\t\t\t{{ data[i] }} \n\t\t\t\t \n\t\t\t \n\t\t\tShow all {{ data.length }} entries \n\t\t \n\t\t
\n\t\t\t\n\t\t\t\t{{ prettifyKey(key) }} : \n\t\t\t\t \n\t\t\t\t{{ value }} \n\t\t\t\t{{ format(value) }} \n\t\t\t\t{{ value }} \n\t\t\t \n\t\t \n\t\t
{{ data }} \n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ObjectTree.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ObjectTree.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./ObjectTree.vue?vue&type=template&id=28f58b3c\"\nimport script from \"./ObjectTree.vue?vue&type=script&lang=js\"\nexport * from \"./ObjectTree.vue?vue&type=script&lang=js\"\nimport style0 from \"./ObjectTree.vue?vue&type=style&index=0&id=28f58b3c&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('article',{staticClass:\"vue-component process\"},[(_vm.process.id)?_vm._t(\"title\",function(){return [_c('a',{staticClass:\"anchor\",attrs:{\"name\":_vm.process.id}}),_c('h2',[_vm._v(\" \"+_vm._s(_vm.process.id)+\" \"),(_vm.displayableNamespace)?_c('span',{staticClass:\"namespace\"},[_vm._v(\" — \"+_vm._s(_vm.displayableNamespace))]):_vm._e()])]},{\"vBind\":_vm.$props,\"displayableNamespace\":_vm.displayableNamespace}):_vm._e(),(_vm.process.summary || _vm.process.deprecated || _vm.process.experimental)?[_c('summary',[_vm._v(\" \"+_vm._s(_vm.process.summary)+\" \"),(_vm.process.deprecated || _vm.process.experimental)?[(_vm.process.summary)?[_vm._v(\" — \")]:_vm._e(),(_vm.process.deprecated)?_c('strong',{staticClass:\"deprecated\"},[_vm._v(\"deprecated\")]):_vm._e(),(_vm.process.experimental)?_c('strong',{staticClass:\"experimental\"},[_vm._v(\"experimental\")]):_vm._e()]:_vm._e()],2)]:_vm._e(),(_vm.provideDownload || _vm.hasElements(_vm.process.categories))?[_c('div',{staticClass:\"process-bar\"},[(_vm.hasElements(_vm.process.categories))?_c('ul',{staticClass:\"badges categories\"},_vm._l((_vm.process.categories),function(value){return _c('li',{key:value,staticClass:\"badge category\",domProps:{\"textContent\":_vm._s(_vm.formatCategory(value))}})}),0):_vm._e(),(_vm.provideDownload)?_c('ul',{staticClass:\"badges actions\"},[_c('li',{staticClass:\"badge action download\"},[_c('a',{staticClass:\"badge-fill\",on:{\"click\":_vm.download}},[_vm._v(\"Download JSON\")])])]):_vm._e()])]:_vm._e(),_vm._t(\"before-description\",null,{\"vBind\":_vm.$props}),(_vm.process.description)?_c('section',{staticClass:\"description\"},[_c('h3',[_vm._v(\"Description\")]),_c('code',{staticClass:\"signature\",domProps:{\"innerHTML\":_vm._s(_vm.signature)}}),_c('Description',{attrs:{\"description\":_vm.process.description,\"processUrl\":_vm.processUrl}}),(_vm.process.deprecated)?_c('DeprecationNotice',{attrs:{\"entity\":\"process\"}}):_vm._e(),(_vm.process.experimental)?_c('ExperimentalNotice',{attrs:{\"entity\":\"process\"}}):_vm._e(),(_vm.process['federation:backends'])?_c('FederationNotice',{attrs:{\"backends\":_vm.process['federation:backends'],\"federation\":_vm.federation,\"entity\":\"process\"}}):_vm._e()],1):_vm._e(),_c('section',{staticClass:\"parameters\"},[_c('h3',[_vm._v(\"Parameters\")]),_vm._l((_vm.parameters),function(param){return _c('ProcessParameter',{key:param.name,attrs:{\"parameter\":param,\"processUrl\":_vm.processUrl,\"federation\":_vm.federation}})}),(_vm.parameters.length === 0)?_c('p',[_vm._v(\"This process has no parameters.\")]):_vm._e()],2),_c('section',{staticClass:\"returns\"},[_c('h3',[_vm._v(\"Return Value\")]),(_vm.returns.description || _vm.returns.schema)?[(_vm.returns.description)?_c('Description',{attrs:{\"description\":_vm.returns.description,\"processUrl\":_vm.processUrl}}):_vm._e(),(_vm.returns.schema)?_c('div',{staticClass:\"json-schema-container\"},[_c('JsonSchema',{attrs:{\"schema\":_vm.returns.schema}})],1):_vm._e()]:_c('p',[_vm._v(\"The return value has not been defined.\")])],2),(_vm.hasElements(_vm.process.exceptions))?_c('section',{staticClass:\"exceptions\"},[_c('h3',[_vm._v(\"Errors/Exceptions\")]),_c('ul',_vm._l((_vm.process.exceptions),function(exception,name){return _c('li',{key:name,staticClass:\"exception\"},[_c('code',[_vm._v(_vm._s(name))]),(exception.http)?_c('span',{staticClass:\"http-code\"},[_vm._v(\" — HTTP \"+_vm._s(exception.http))]):_vm._e(),(exception.code)?_c('span',{staticClass:\"error-code\"},[_vm._v(\" — \"+_vm._s(exception.code))]):_vm._e(),(exception.description)?_c('Description',{attrs:{\"description\":exception.description,\"processUrl\":_vm.processUrl,\"compact\":true}}):_vm._e(),(exception.message)?_c('div',{staticClass:\"message\"},[_vm._v(\"Message: \"),_c('em',[_vm._v(_vm._s(exception.message))])]):_vm._e()],1)}),0)]):_vm._e(),(_vm.hasElements(_vm.process.examples))?_c('section',{staticClass:\"examples\"},[_c('h3',[_vm._v(\"Examples\")]),_vm._l((_vm.process.examples),function(example,key){return _c('ProcessExample',{key:key,attrs:{\"id\":key,\"example\":example,\"processId\":_vm.id,\"processParameters\":_vm.parameters,\"processUrl\":_vm.processUrl}})}),_c('LinkList',{attrs:{\"links\":_vm.exampleLinks,\"heading\":\"Processes\",\"headingTag\":\"h4\"}})],2):_vm._e(),_c('section',{staticClass:\"links\"},[_c('LinkList',{attrs:{\"links\":_vm.process.links,\"heading\":\"See Also\",\"headingTag\":\"h3\",\"ignoreRel\":['self', 'example']}})],1),(_vm.showGraph && _vm.process.process_graph)?_c('section',{staticClass:\"process-graph\"},[_c('h3',[_vm._v(\"Processing Instructions\")]),_c('div',{staticClass:\"graph\"},[_vm._t(\"process-graph\",function(){return [_c('ModelBuilder',{attrs:{\"id\":_vm.id,\"value\":_vm.process,\"explicitZoom\":true}})]},{\"vBind\":_vm.$props})],2)]):_vm._e(),_vm._t(\"end\",null,{\"vBind\":_vm.$props})],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component process-example\"},[_c('h4',[_vm._v(_vm._s(_vm.title))]),_c('div',{staticClass:\"content\"},[(_vm.example.description)?_c('Description',{attrs:{\"description\":_vm.example.description,\"processUrl\":_vm.processUrl,\"compact\":true}}):_vm._e(),(_vm.example.arguments)?_c('div',{staticClass:\"arguments\"},[_c('code',{domProps:{\"innerHTML\":_vm._s(_vm.renderedArguments)}})]):_vm._e()],1)])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
{{ title }} \n\n\t\t
\n\t\t\t
\n\n\t\t\t
\n\t\t\t\t
\n\t\t\t
\n\t\t
\n\t
\n \n\n\n\n","import mod from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ProcessExample.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ProcessExample.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./ProcessExample.vue?vue&type=template&id=ed09847c\"\nimport script from \"./ProcessExample.vue?vue&type=script&lang=js\"\nexport * from \"./ProcessExample.vue?vue&type=script&lang=js\"\nimport style0 from \"./ProcessExample.vue?vue&type=style&index=0&id=ed09847c&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","\n\t\n\n\t\t\n\t\t\t \n\t\t\t\n\t\t\t\t{{ process.id }}\n\t\t\t\t — {{ displayableNamespace }} \n\t\t\t \n\t\t \n\n\t\t\n\t\t\t\n\t\t\t\t{{ process.summary }}\n\t\t\t\t\n\t\t\t\t\t — \n\t\t\t\t\tdeprecated \n\t\t\t\t\texperimental \n\t\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\t\t\t\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t
\n\t\t \n\n\t\t \n\n\t\t\n\t\t\tDescription \n\t\t\t
\n\t\t\t \n\t\t\t \n\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\t\t\tParameters \n\t\t\t \n\t\t\tThis process has no parameters.
\n\t\t \n\n\t\t\n\t\t\tReturn Value \n\t\t\t\n\t\t\t\t \n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t
\n\t\t\t \n\t\t\tThe return value has not been defined.
\n\t\t \n\n\t\t\n\t\t\tErrors/Exceptions \n\t\t\t\n\t\t \n\n\t\t\n\t\t\tExamples \n\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\n\t\t\n\t\t\tProcessing Instructions \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t \n\t\t\t
\n\t\t \n\n\t\t \n\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Process.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Process.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Process.vue?vue&type=template&id=cbf63874\"\nimport script from \"./Process.vue?vue&type=script&lang=js\"\nexport * from \"./Process.vue?vue&type=script&lang=js\"\nimport style0 from \"./Process.vue?vue&type=style&index=0&id=cbf63874&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component searchable-list\",class:{expandable: _vm.collapsed !== null, expanded: _vm.showList, noResults: _vm.filteredCount === 0}},[_vm._t(\"heading\",function(){return [(_vm.heading)?_c('h2',{staticClass:\"heading\",on:{\"click\":function($event){return _vm.toggleHeading(null)}}},[_vm._v(\" \"+_vm._s(_vm.heading)+\" \"),(_vm.filteredCount !== null && _vm.filteredCount !== _vm.totalCount)?[_vm._v(\"(\"+_vm._s(_vm.filteredCount)+\"/\"+_vm._s(_vm.totalCount)+\")\")]:[_vm._v(\"(\"+_vm._s(_vm.totalCount)+\")\")]],2):_vm._e()]},{\"filteredCount\":_vm.filteredCount,\"totalCount\":_vm.totalCount}),(_vm.showList !== null)?_c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.showList === true),expression:\"showList === true\"}],staticClass:\"body\"},[_vm._t(\"content-start\"),(_vm.totalCount === 0)?[_c('p',[_vm._v(\"No data available.\")])]:[_c('section',{staticClass:\"action-bar\"},[(_vm.externalSearchTerm === null)?_c('SearchBox',{attrs:{\"placeholder\":_vm.searchPlaceholder,\"minLength\":_vm.searchMinLength},model:{value:(_vm.searchTerm),callback:function ($$v) {_vm.searchTerm=$$v},expression:\"searchTerm\"}}):_vm._e(),(_vm.deprecatedFilter)?_c('label',{staticClass:\"deprecated\",attrs:{\"title\":\"Show deprecated elements?\"}},[_c('input',{directives:[{name:\"model\",rawName:\"v-model\",value:(_vm.hideDeprecated),expression:\"hideDeprecated\"}],attrs:{\"type\":\"checkbox\",\"true-value\":false,\"false-value\":true},domProps:{\"checked\":Array.isArray(_vm.hideDeprecated)?_vm._i(_vm.hideDeprecated,null)>-1:_vm._q(_vm.hideDeprecated,false)},on:{\"change\":function($event){var $$a=_vm.hideDeprecated,$$el=$event.target,$$c=$$el.checked?(false):(true);if(Array.isArray($$a)){var $$v=null,$$i=_vm._i($$a,$$v);if($$el.checked){$$i<0&&(_vm.hideDeprecated=$$a.concat([$$v]))}else{$$i>-1&&(_vm.hideDeprecated=$$a.slice(0,$$i).concat($$a.slice($$i+1)))}}else{_vm.hideDeprecated=$$c}}}}),_vm._v(\" Show deprecated \")]):_vm._e()],1),_vm._t(\"after-search-box\",null,{\"filteredCount\":_vm.filteredCount,\"summaries\":_vm.summaries}),(_vm.filteredCount === 0)?_c('p',[_vm._v(\"No search results found.\")]):_c('ul',{staticClass:\"list\",class:{expandable: _vm.offerDetails}},_vm._l((_vm.summaries),function(summary,i){return _c('li',{directives:[{name:\"show\",rawName:\"v-show\",value:(summary.show),expression:\"summary.show\"}],key:summary.identifier,class:{expanded: _vm.showDetails[i]}},[_c('summary',{staticClass:\"summary\",class:{experimental: summary.experimental, deprecated: summary.deprecated},on:{\"click\":function($event){return _vm.toggleDetails(i)}}},[_vm._t(\"summary\",function(){return [_c('strong',[_vm._v(\" \"+_vm._s(summary.identifier)+\" \"),(_vm.allowCopy && _vm.canCopy)?_c('span',{staticClass:\"copy\",attrs:{\"title\":\"Copy identifier\"},on:{\"click\":function($event){$event.preventDefault();$event.stopPropagation();return _vm.copyIdentifier($event, summary)}}},[_vm._v(\"📋\")]):_vm._e()]),(summary.summary)?_c('small',{class:{hideOnExpand: !_vm.showSummaryOnExpand}},[_vm._v(_vm._s(summary.summary))]):_vm._e(),(_vm.showKeywords && summary.keywords.length > 0)?_c('ul',{staticClass:\"badges small block hideOnExpand\"},_vm._l((summary.keywords),function(keyword){return _c('li',{key:keyword,staticClass:\"badge\"},[_vm._v(_vm._s(keyword))])}),0):_vm._e()]},{\"summary\":summary,\"item\":summary.data})],2),(typeof _vm.showDetails[i] === 'boolean')?_c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.showDetails[i] === true),expression:\"showDetails[i] === true\"}],staticClass:\"details\"},[(!summary.loaded)?_c('Loading'):_vm._t(\"details\",function(){return [_vm._v(\" No details available! \")]},{\"summary\":summary,\"item\":summary.data})],2):_vm._e()])}),0)]],2):_vm._e()],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
\n\t\t\t\n\t\t\t\t{{ heading }}\n\t\t\t\t({{ filteredCount }}/{{ totalCount }}) \n\t\t\t\t({{ totalCount }}) \n\t\t\t \n\t\t \n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\tNo data available.
\n\t\t\t \n\t\t\t
\n\t\t\t\t\n\t\t\t\t \n\t\t\t\tNo search results found.
\n\t\t\t\t\n\t\t\t \n\t\t
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./SearchableList.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./SearchableList.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./SearchableList.vue?vue&type=template&id=79d9b1e3\"\nimport script from \"./SearchableList.vue?vue&type=script&lang=js\"\nexport * from \"./SearchableList.vue?vue&type=script&lang=js\"\nimport style0 from \"./SearchableList.vue?vue&type=style&index=0&id=79d9b1e3&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('article',{staticClass:\"vue-component service-type\"},[_vm._t(\"title\",function(){return [_c('a',{staticClass:\"anchor\",attrs:{\"name\":_vm.id}}),_c('h2',[(_vm.service.title)?[_vm._v(\" \"+_vm._s(_vm.service.title)+\" (\"),_c('code',{staticClass:\"id\"},[_vm._v(_vm._s(_vm._f(\"abbrev\")(_vm.id)))]),_vm._v(\") \")]:_c('code',{staticClass:\"id\"},[_vm._v(_vm._s(_vm._f(\"abbrev\")(_vm.id)))])],2)]},null,_vm.$props),_vm._t(\"before-description\",null,null,_vm.$props),(_vm.service.description)?_c('section',{staticClass:\"description\"},[_c('h3',[_vm._v(\"Description\")]),_c('Description',{attrs:{\"description\":_vm.service.description}}),(_vm.service.deprecated)?_c('DeprecationNotice',{attrs:{\"entity\":\"type of secondary web service\"}}):_vm._e(),(_vm.service.experimental)?_c('ExperimentalNotice',{attrs:{\"entity\":\"type of secondary web service\"}}):_vm._e(),(_vm.service['federation:backends'])?_c('FederationNotice',{attrs:{\"backends\":_vm.service['federation:backends'],\"federation\":_vm.federation,\"entity\":\"type of secondary web service\"}}):_vm._e()],1):_vm._e(),_c('section',{staticClass:\"configuration\"},[_c('h3',[_vm._v(\"Configuration Settings\")]),_vm._l((_vm.configuration),function(param){return _c('ProcessParameter',{key:param.name,attrs:{\"parameter\":param,\"federation\":_vm.federation}})}),(_vm.configuration.length === 0)?_c('p',[_vm._v(\"No configuration settings available.\")]):_vm._e()],2),_c('section',{staticClass:\"parameters\"},[_c('h3',[_vm._v(\"Process Parameters\")]),_vm._l((_vm.parameters),function(param){return _c('ProcessParameter',{key:param.name,attrs:{\"parameter\":param,\"federation\":_vm.federation}})}),(_vm.parameters.length === 0)?_c('p',[_vm._v(\"No process parameters available.\")]):_vm._e()],2),_c('section',{staticClass:\"links\"},[_c('LinkList',{attrs:{\"links\":_vm.service.links,\"heading\":\"See Also\",\"headingTag\":\"h3\"}})],1),_vm._t(\"end\",null,null,_vm.$props)],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\n\t\t\n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ service.title }}\n\t\t\t\t\t({{ id | abbrev }}
)\n\t\t\t\t \n\t\t\t\t{{ id | abbrev }}
\n\t\t\t \n\t\t \n\n\t\t \n\n\t\t\n\t\t\tDescription \n\t\t\t \n\t\t\t \n\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\t\t\tConfiguration Settings \n\t\t\t \n\t\t\tNo configuration settings available.
\n\t\t \n\n\t\t\n\t\t\tProcess Parameters \n\t\t\t \n\t\t\tNo process parameters available.
\n\t\t \n\n\t\t\n\n\t\t \n\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ServiceType.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ServiceType.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./ServiceType.vue?vue&type=template&id=9ad25d1c\"\nimport script from \"./ServiceType.vue?vue&type=script&lang=js\"\nexport * from \"./ServiceType.vue?vue&type=script&lang=js\"\nimport style0 from \"./ServiceType.vue?vue&type=style&index=0&id=9ad25d1c&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component features\"},[(_vm.heading)?_c(_vm.headingTag,{tag:\"component\"},[_vm._v(\" \"+_vm._s(_vm.heading)+\" (\"+_vm._s(_vm.supportedFeatureCount)+\"/\"+_vm._s(_vm.totalCount)+\") \")]):_vm._e(),_c('ul',_vm._l((_vm.supportedFeatures),function(status,feature){return _c('li',{key:feature},[_c('span',{class:status.className,attrs:{\"title\":status.tooltip},domProps:{\"textContent\":_vm._s(status.icon)}}),_vm._v(\" \"+_vm._s(feature)+\" \")])}),0)],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n \n\t\t
\n {{ heading }}\n ({{ supportedFeatureCount }}/{{ totalCount }})\n \n
\n
\n \n\n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./SupportedFeatures.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./SupportedFeatures.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./SupportedFeatures.vue?vue&type=template&id=52a7f7d6\"\nimport script from \"./SupportedFeatures.vue?vue&type=script&lang=js\"\nexport * from \"./SupportedFeatures.vue?vue&type=script&lang=js\"\nimport style0 from \"./SupportedFeatures.vue?vue&type=style&index=0&id=52a7f7d6&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.enabled && _vm.active),expression:\"enabled && active\"}],staticClass:\"tabContent\"},[_vm._t(\"default\",null,{\"tab\":this})],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t \n\t
\n \n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Tab.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Tab.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Tab.vue?vue&type=template&id=000afa5e\"\nimport script from \"./Tab.vue?vue&type=script&lang=js\"\nexport * from \"./Tab.vue?vue&type=script&lang=js\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{class:_vm.classes,attrs:{\"id\":_vm.id}},[(!_vm.hasEnabledTabs)?_c('div',{staticClass:\"tabsEmpty\"},[_vm._t(\"empty\")],2):_vm._e(),_c('div',{ref:\"tabsHeader\",staticClass:\"tabsHeader\"},_vm._l((_vm.tabs),function(tab){return _c('button',{directives:[{name:\"show\",rawName:\"v-show\",value:(tab.enabled),expression:\"tab.enabled\"}],key:tab.id,class:{tabItem: true, tabActive: tab.active, tabHasIcon: !!tab.icon, [tab.id]: true },attrs:{\"type\":\"button\",\"title\":tab.name},on:{\"click\":function($event){if(!$event.type.indexOf('key')&&_vm._k($event.keyCode,\"left\",37,$event.key,[\"Left\",\"ArrowLeft\"]))return null;if('button' in $event && $event.button !== 0)return null;return _vm.selectTab(tab)},\"mouseup\":function($event){if('button' in $event && $event.button !== 1)return null;return _vm.closeTab(tab)}}},[(tab.icon)?_c('i',{class:['tabIcon', 'fas', tab.icon]}):_vm._e(),_c('span',{staticClass:\"tabName\",on:{\"dblclick\":function($event){return _vm.editTabName(tab)}}},[(_vm.tabEditId == tab.id)?[_c('input',{directives:[{name:\"model\",rawName:\"v-model\",value:(_vm.tabEditName),expression:\"tabEditName\"}],attrs:{\"type\":\"text\"},domProps:{\"value\":(_vm.tabEditName)},on:{\"blur\":_vm.saveTabName,\"keyup\":[function($event){if(!$event.type.indexOf('key')&&_vm._k($event.keyCode,\"enter\",13,$event.key,\"Enter\"))return null;return _vm.saveTabName.apply(null, arguments)},function($event){if(!$event.type.indexOf('key')&&_vm._k($event.keyCode,\"esc\",27,$event.key,[\"Esc\",\"Escape\"]))return null;return _vm.resetTabName.apply(null, arguments)}],\"input\":function($event){if($event.target.composing)return;_vm.tabEditName=$event.target.value}}})]:[_vm._t(\"tabName\",function(){return [_vm._v(_vm._s(tab.name))]},{\"tab\":tab})]],2),(tab.closable)?_c('svg',{staticClass:\"tabClose\",attrs:{\"xmlns\":\"http://www.w3.org/2000/svg\",\"title\":\"Close\",\"viewBox\":\"0 0 24 24\"},on:{\"click\":function($event){$event.preventDefault();$event.stopPropagation();return _vm.closeTab(tab)}}},[_c('circle',{attrs:{\"cx\":\"12\",\"cy\":\"12\",\"r\":\"11\",\"stroke\":\"black\",\"stroke-width\":\"2\",\"fill\":\"none\"}}),_c('path',{attrs:{\"stroke\":\"black\",\"stroke-width\":\"2\",\"fill\":\"none\",\"d\":\"M7,7,17,17\"}}),_c('path',{attrs:{\"stroke\":\"black\",\"stroke-width\":\"2\",\"fill\":\"none\",\"d\":\"M7,17,17,7\"}})]):_vm._e()])}),0),_c('div',{staticClass:\"tabsBody\"},[_vm._t(\"default\",null,{\"tabs\":this}),_vm._l((this.dynamicTabs),function(tab){return _c('Tab',{key:tab.id,attrs:{\"id\":tab.id,\"name\":tab.name,\"icon\":tab.icon,\"selected\":tab.selected,\"data\":tab.data,\"enabled\":tab.enabled,\"closable\":tab.closable,\"allowShow\":() => _vm.onDynamicAllowShow(tab)},on:{\"close\":function($event){return _vm.onDynamic(tab, 'close')},\"hide\":function($event){return _vm.onDynamic(tab, 'hide')},\"show\":function($event){return _vm.onDynamic(tab, 'show')}}},[_vm._t(tab.id,function(){return [_vm._t(\"dynamic\",null,{\"tab\":tab})]},{\"tab\":tab})],2)})],2)])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
\n\t\t\n\t\t
\n\t\t\t \n\t\t\t onDynamicAllowShow(tab)\" :key=\"tab.id\">\n\t\t\t\t \n\t\t\t \n\t\t
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Tabs.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Tabs.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Tabs.vue?vue&type=template&id=75b0d5be\"\nimport script from \"./Tabs.vue?vue&type=script&lang=js\"\nexport * from \"./Tabs.vue?vue&type=script&lang=js\"\nimport style0 from \"./Tabs.vue?vue&type=style&index=0&id=75b0d5be&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('article',{staticClass:\"vue-component udf-runtime\"},[_vm._t(\"title\",function(){return [_c('a',{staticClass:\"anchor\",attrs:{\"name\":_vm.id}}),_c('h2',[(_vm.runtime.title)?[_vm._v(\" \"+_vm._s(_vm.runtime.title)+\" (\"),_c('code',{staticClass:\"id\"},[_vm._v(_vm._s(_vm.id))]),_vm._v(\") \")]:_c('code',{staticClass:\"id\"},[_vm._v(_vm._s(_vm.id))])],2)]},null,_vm.$props),_vm._t(\"badges\",function(){return [_c('ul',{staticClass:\"badges\"},[(_vm.isDocker)?_c('li',{staticClass:\"badge docker\"},[_vm._v(\"Docker: \"+_vm._s(_vm.runtime.docker))]):_c('li',{staticClass:\"badge\"},[_vm._v(\"Programming Language\")])])]},null,_vm.$props),_vm._t(\"before-description\",null,null,_vm.$props),(_vm.runtime.description)?_c('section',{staticClass:\"description\"},[_c('Description',{attrs:{\"description\":_vm.runtime.description}}),(_vm.runtime.deprecated)?_c('DeprecationNotice',{attrs:{\"entity\":\"UDF runtime\"}}):_vm._e(),(_vm.runtime.experimental)?_c('ExperimentalNotice',{attrs:{\"entity\":\"UDF runtime\"}}):_vm._e(),(_vm.runtime['federation:backends'])?_c('FederationNotice',{attrs:{\"backends\":_vm.runtime['federation:backends'],\"federation\":_vm.federation,\"entity\":\"UDF runtime\"}}):_vm._e()],1):_vm._e(),_c('section',{staticClass:\"links\"},[_c('LinkList',{attrs:{\"links\":_vm.runtime.links,\"heading\":\"See Also\",\"headingTag\":\"h3\"}})],1),(_vm.isDocker)?[_c('h3',[_vm._v(\"Tags\")]),_c('ul',_vm._l((_vm.runtime.tags),function(tag){return _c('li',{key:tag},[_vm._v(\" \"+_vm._s(tag)+\" \"),(tag === _vm.runtime.default)?_c('ul',{staticClass:\"badges small\"},[_c('li',{staticClass:\"badge default\"},[_vm._v(\"default\")])]):_vm._e()])}),0)]:[_c('h3',[_vm._v(\"Versions\")]),_c('Tabs',{ref:\"tabs\",attrs:{\"id\":\"userContent\"}},_vm._l((_vm.runtime.versions),function(env,version){return _c('Tab',{key:version,attrs:{\"id\":`${_vm.id}-${version}`,\"name\":version,\"selected\":version === _vm.selectVersion}},[(version === _vm.runtime.default)?_c('ul',{staticClass:\"badges\"},[_c('li',{staticClass:\"badge default\"},[_vm._v(\"default\")])]):_vm._e(),(env.deprecated)?_c('DeprecationNotice',{attrs:{\"entity\":\"UDF runtime version\"}}):_vm._e(),(env.experimental)?_c('ExperimentalNotice',{attrs:{\"entity\":\"UDF runtime version\"}}):_vm._e(),_c('p',[_vm._v(\"This runtime includes support for:\")]),_c('ul',_vm._l((env.libraries),function(library,name){return _c('li',{key:name,staticClass:\"library\"},[_vm._v(\" \"+_vm._s(name)+\" \"),_c('ul',{staticClass:\"badges small\"},[_c('span',{staticClass:\"badge\"},[_vm._v(_vm._s(library.version))]),(library.experimental)?_c('span',{staticClass:\"badge experimental\"},[_vm._v(\"experimental\")]):_vm._e(),(library.deprecated)?_c('span',{staticClass:\"badge deprecated\"},[_vm._v(\"deprecated\")]):_vm._e()]),_c('LinkList',{attrs:{\"links\":library.links}})],1)}),0)],1)}),1)]],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\n\t\t\n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ runtime.title }}\n\t\t\t\t\t({{ id }}
)\n\t\t\t\t \n\t\t\t\t{{ id }}
\n\t\t\t \n\t\t \n\n\t\t\n\t\t\t\n\t\t\t\tDocker: {{ runtime.docker }} \n\t\t\t\tProgramming Language \n\t\t\t \n\t\t \n\n\t\t \n\n\t\t\n\t\t\t \n\t\t\t \n\t\t\t \n\t\t\t \n\t\t \n\n\t\t\n\n\t\t\n\t\t\tTags \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ tag }}\n\t\t\t\t\t\n\t\t\t\t \n\t\t\t \n\t\t \n\t\t\n\t\t\tVersions \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tdefault \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\tThis runtime includes support for:
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t{{ name }}\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{{ library.version }} \n\t\t\t\t\t\t\t\texperimental \n\t\t\t\t\t\t\t\tdeprecated \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t \n\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./UdfRuntime.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./UdfRuntime.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./UdfRuntime.vue?vue&type=template&id=5034d179\"\nimport script from \"./UdfRuntime.vue?vue&type=script&lang=js\"\nexport * from \"./UdfRuntime.vue?vue&type=script&lang=js\"\nimport style0 from \"./UdfRuntime.vue?vue&type=style&index=0&id=5034d179&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _vm._m(0)\n}\nvar staticRenderFns = [function (){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component loading-notice\"},[_c('span',{staticClass:\"loading\"},[_vm._v(\"Loading\")])])\n}]\n\nexport { render, staticRenderFns }","\n\t\n\t\tLoading \n\t
\n \n\n\n\n","import mod from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Loading.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Loading.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Loading.vue?vue&type=template&id=17097841\"\nimport script from \"./Loading.vue?vue&type=script&lang=js\"\nexport * from \"./Loading.vue?vue&type=script&lang=js\"\nimport style0 from \"./Loading.vue?vue&type=style&index=0&id=17097841&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component process-parameter\"},[_c('h4',[_c('code',[_vm._v(_vm._s(_vm.parameter.name))]),(!_vm.parameter.optional)?_c('strong',{staticClass:\"required\",attrs:{\"title\":\"required\"}},[_vm._v(\"*\")]):_vm._e(),(_vm.hasDefault)?_c('code',{staticClass:\"default\"},[_vm._v(\" = \"+_vm._s(_vm.defaultValue))]):_vm._e()]),_c('div',{staticClass:\"details\"},[(_vm.parameter.description)?_c('Description',{attrs:{\"description\":_vm.parameter.description,\"processUrl\":_vm.processUrl}}):_vm._e(),(_vm.parameter.deprecated === true)?_c('DeprecationNotice',{attrs:{\"entity\":\"parameter\"}}):_vm._e(),(_vm.parameter.experimental === true)?_c('ExperimentalNotice',{attrs:{\"entity\":\"parameter\"}}):_vm._e(),(_vm.parameter['federation:backends'])?_c('FederationNotice',{attrs:{\"backends\":_vm.parameter['federation:backends'],\"federation\":_vm.federation,\"entity\":\"parameter\"}}):_vm._e(),(_vm.parameter.schema)?_c('div',{staticClass:\"json-schema-container\"},[_c('JsonSchema',{attrs:{\"schema\":_vm.parameter.schema,\"processUrl\":_vm.processUrl}})],1):_vm._e()],1)])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
\n\t\t\t{{ parameter.name }}
\n\t\t\t* \n\t\t\t = {{ defaultValue }}
\n\t\t \n\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\t \n\t\t\t
\n\t\t
\n\t
\n \n\n\n\n","import mod from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ProcessParameter.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ProcessParameter.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./ProcessParameter.vue?vue&type=template&id=65c7b990\"\nimport script from \"./ProcessParameter.vue?vue&type=script&lang=js\"\nexport * from \"./ProcessParameter.vue?vue&type=script&lang=js\"\nimport style0 from \"./ProcessParameter.vue?vue&type=style&index=0&id=65c7b990&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","export default {\n data() {\n return {\n canCopy: false\n }\n },\n\tmounted() {\n\t\tthis.canCopy = navigator && navigator.clipboard && typeof navigator.clipboard.writeText === 'function';\n\t},\n methods: {\n copyText(text, onSuccess = null, onError = null) {\n if (this.canCopy) {\n const promise = navigator.clipboard.writeText(text)\n if (onSuccess) {\n promise.then(onSuccess);\n }\n if (onError) {\n promise.catch(onError);\n }\n }\n },\n\t\ttoggleIcon(elem, newIcon) {\n\t\t\tif (elem) {\n\t\t\t\tlet oldIcon = elem.innerText;\n\t\t\t\telem.innerText = newIcon;\n\t\t\t\tsetTimeout(() => elem.innerText = oldIcon, 2000);\n\t\t\t}\n\t\t}\n }\n}","export default {\n\tcomponents: {\n\t\tFederationNotice: () => import('./FederationNotice.vue'),\n\t\tFederationMissingNotice: () => import('../FederationMissingNotice.vue')\n\t},\n\tprops: {\n\t\tfederation: {\n\t\t\ttype: Object,\n\t\t\tdefault: () => ({})\n\t\t}\n\t}\n};","import Utils from '../../utils';\nimport { Versions } from '@openeo/js-commons';\nimport StacMigrate from '@radiantearth/stac-migrate';\n\nconst IMAGE_MEDIA_TYPES = ['image/apng', 'image/gif', 'image/png', 'image/jpeg', 'image/webp'];\nconst IMAGE_EXTENSIONS = ['gif', 'png', 'jpg', 'jpeg', 'webp'];\n\nexport default {\n\tcomponents: {\n\t\tDescription: () => import('../Description.vue'),\n\t\tDeprecationNotice: () => import('../DeprecationNotice.vue'),\n\t\tLinkList: () => import('../LinkList.vue'),\n\t\tStacAsset: () => import('../internal/StacAsset.vue'),\n\t\tStacFields: () => import('../internal/StacFields.vue')\n\t},\n\tprops: {\n\t\tdata: {\n\t\t\ttype: Object,\n\t\t\tdefault: () => ({})\n\t\t},\n\t\tmapOptions: {\n\t\t\t// It's not possible to specify defaults for the individual properties, therefore this prop is only accessed through a computed property which adds them in.\n\t\t\ttype: Object,\n\t\t\t// Default values will be set in computed.leafletOptions\n\t\t\tdefault: () => ({}),\n\t\t\tvalidator: function(value) {\n\t\t\t\tconst allowedTypes = { // keep in sync with Readme\n\t\t\t\t\theight: \"string\",\n\t\t\t\t\twidth: \"string\",\n\t\t\t\t\tbasemap: \"string\",\n\t\t\t\t\tattribution: \"string\",\n\t\t\t\t\twrapAroundAntimeridian: \"boolean\",\n\t\t\t\t\tscrollWheelZoom: \"boolean\"\n\t\t\t\t};\n\t\t\t\tconst allowedKeys = Object.keys(allowedTypes);\n\t\t\t\treturn typeof value == 'object' && Object.keys(value).every(key =>\n\t\t\t\t\tallowedKeys.indexOf(key) != -1 && typeof value[key] == allowedTypes[key]\n\t\t\t\t);\n\t\t\t}\n\t\t}\n\t},\n\tdata() {\n\t\treturn {\n\t\t\tmap: null\n\t\t};\n\t},\n\tcomputed: {\n\t\tstac() {\n\t\t\tlet cloned = Utils.deepClone(this.data);\n\t\t\treturn StacMigrate.stac(cloned);\n\t\t},\n\t\tleafletOptions() {\n\t\t\treturn { // keep in sync with Readme\n\t\t\t\theight: this.mapOptions.height || '250px',\n\t\t\t\twidth: this.mapOptions.width || 'auto',\n\t\t\t\tbasemap: this.mapOptions.basemap || 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png',\n\t\t\t\tattribution: this.mapOptions.attribution || 'Map data © OpenStreetMap ',\n\t\t\t\tnoWrap: this.mapOptions.wrapAroundAntimeridian === undefined ? true : !this.mapOptions.wrapAroundAntimeridian, // negate!\n\t\t\t\tscrollWheelZoom: this.mapOptions.scrollWheelZoom === undefined ? false : this.mapOptions.scrollWheelZoom\n\t\t\t}\n\t\t},\n\t\tthumbnails() {\n\t\t\tif (!Utils.isObject(this.stac.assets)) {\n\t\t\t\treturn [];\n\t\t\t}\n\t\t\treturn Object.values(this.stac.assets).filter(this.assetIsImage);\n\t\t},\n\t\thasAssets() {\n\t\t\treturn Utils.size(this.stac.assets) > 0;\n\t\t}\n\t},\n\twatch: {\n\t\tdata() {\n\t\t\tthis.initMap();\n\t\t},\n\t\tshowMap(val) {\n\t\t\tif (val) {\n\t\t\t\tthis.initMap();\n\t\t\t}\n\t\t}\n\t},\n\tbeforeCreate() {\n\t\tUtils.enableHtmlProps(this);\n\t},\n\tmounted() {\n\t\tthis.initMap();\n\t},\n\tmethods: {\n\t\tassetIsImage(asset) {\n\t\t\tlet ext = typeof asset.href === 'string' ? asset.href.split('.').pop() : '';\n\t\t\treturn Array.isArray(asset.roles) && \n\t\t\t\t(asset.roles.includes('thumbnail') || asset.roles.includes('overview')) && \n\t\t\t\t(IMAGE_MEDIA_TYPES.includes(asset.type) || IMAGE_EXTENSIONS.includes(ext));\n\t\t},\n\t\tinitLeafletPlugins() {\n\t\t\t// To be implemented in Collection/Item, if required.\n\t\t},\n\t\tasync initMap() {\n\t\t\tif (!this.$refs.mapContainer) {\n\t\t\t\tawait this.$nextTick();\n\t\t\t}\n\t\t\tif (!this.$refs.mapContainer || this.map !== null || !this.showMap) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t\t// Only use the L for leaflet temporarily and release it later again\n\t\t\tlet oldL = window.L;\n\t\t\ttry {\n\t\t\t\tthis.map = {\n\t\t\t\t\tleaflet: null,\n\t\t\t\t\tinstance: null,\n\t\t\t\t\tgeometries: null\n\t\t\t\t};\n\t\t\t\t// Leaflet no conflict fix: Try to re-use an already available instance of Leaflet to avoid\n\t\t\t\t// conflicts with other libraries such as ipyleaflet. Also, use L variable as long as initMap\n\t\t\t\t// is running. For all other cases store Leaflet in this.map.leaflet, which can be used in other places.\n\t\t\t\tlet hasLeaflet = () => Utils.isObject(window.L) && Versions.validate(window.L.version) && Versions.compare(window.L.version, \"1.x.x\", \"=\");\n\t\t\t\tif (!hasLeaflet()) {\n\t\t\t\t\twindow.L = await import('leaflet');\n\t\t\t\t}\n\t\t\t\tif (!hasLeaflet()) {\n\t\t\t\t\tconsole.warn(\"Leaflet is not available\");\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\tthis.map.leaflet = window.L;\n\n\t\t\t\tlet css = await import('leaflet/dist/leaflet.css');\n\t\t\t\t// In Web Component mode inject the CSS into the shadowroot\n\t\t\t\tif (this.$root && this.$root.$options.shadowRoot && css.__inject__) {\n\t\t\t\t\tcss.__inject__(this.$root.$options.shadowRoot);\n\t\t\t\t}\n\n\t\t\t\tvar map = this.map.leaflet.map(this.$refs.mapContainer, {scrollWheelZoom: this.leafletOptions.scrollWheelZoom});\n\t\t\t\tthis.map.instance = map;\n\t\t\t\tvar osm = this.map.leaflet.tileLayer(this.leafletOptions.basemap, {\n\t\t\t\t\tname: 'Basemap',\n\t\t\t\t\tattribution: this.leafletOptions.attribution,\n\t\t\t\t\tnoWrap: this.leafletOptions.noWrap\n\t\t\t\t});\n\t\t\t\tosm.addTo(map);\n\n\t\t\t\tif (typeof this.addFeatures === 'function') {\n\t\t\t\t\tthis.map.geometries = this.addFeatures();\n\t\t\t\t\tif (this.map.geometries) {\n\t\t\t\t\t\tthis.map.geometries.addTo(map);\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Update map container in DOM\n\t\t\t\tthis.$refs.mapContainer.style.width = this.leafletOptions.width;\n\t\t\t\tthis.$refs.mapContainer.style.height = this.leafletOptions.height;\n\t\t\t\tmap.invalidateSize(false);\n\t\t\t\tthis.updateMapView();\n\n\t\t\t\tif (typeof this.mapOptions.onAfterMapInit === 'function') {\n\t\t\t\t\ttypeof this.mapOptions.onAfterMapInit(map, this.map.geometries);\n\t\t\t\t}\n\t\t\t\t// Leaflet no conflict fix: Release L global variable again\n\t\t\t\twindow.L = oldL;\n\t\t\t\treturn true;\n\t\t\t} catch (e) {\n\t\t\t\tconsole.error(e);\n\t\t\t\t// Leaflet no conflict fix: Release L global variable again\n\t\t\t\twindow.L = oldL;\n\t\t\t\treturn false;\n\t\t\t}\n\t\t},\n\t\tupdateMapView() {\n\t\t\tif (!this.map || !this.map.geometries) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// Compute somewhat smart map extent and zoom level around bbox\n\t\t\tvar bounds = this.map.geometries.getBounds();\n\t\t\tvar zoom = this.map.instance.getBoundsZoom(bounds);\n\t\t\tvar newZoom = Math.min(zoom, 11); // Never zoom closer than 8\n\t\t\tif (zoom > 8) {\n\t\t\t\tnewZoom = newZoom - 3; // Zoom out three levels\n\t\t\t}\n\t\t\telse if (zoom > 5) {\n\t\t\t\tnewZoom = newZoom - 2; // Zoom out two levels\n\t\t\t}\n\t\t\telse if (zoom > 2) {\n\t\t\t\tnewZoom--; // Zoom out one level\n\t\t\t}\n\t\t\tthis.map.instance.fitBounds(bounds);\n\t\t\tthis.map.instance.setZoom(newZoom);\n\n\t\t\tthis.map.instance.once('moveend zoomend', () => this.map.instance.invalidateSize(false));\n\t\t}\n\t}\n};","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"container\"},[_c('h1',[_vm._v(\"Examples\")]),_vm._l((_vm.data),function(examples,component){return _c('div',{key:component,staticClass:\"container\"},[_c('h2',[_vm._v(_vm._s(component))]),_vm._l((examples),function(props,id){return _c('div',{key:id,staticClass:\"example\"},[_c('h3',[_vm._v(\" ➥ \"+_vm._s(id)+\" \"),_c('button',{attrs:{\"type\":\"button\"},on:{\"click\":function($event){return _vm.toggle(component, id)}}},[(_vm.show[component + id])?[_vm._v(\"Remove Component\")]:[_vm._v(\"Load Component\")]],2)]),(_vm.show[component + id])?_c('div',{staticClass:\"component\"},[_c(component,_vm._b({tag:\"component\"},'component',props,false))],1):_vm._e()])})],2)})],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component capabilities\"},[_c('h2',[_vm._v(_vm._s(_vm.title))]),_c('section',{staticClass:\"base-data\"},[(_vm.url2)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"URL:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.url2))])]):_vm._e(),(_vm.capabilities.api_version)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"openEO-Version:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.capabilities.api_version))])]):_vm._e(),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Production:\")]),_c('span',{staticClass:\"value\"},[(_vm.capabilities.production)?[_vm._v(\"✔️\")]:[_vm._v(\"❌\")]],2)])]),(_vm.capabilities.description)?_c('Description',{attrs:{\"description\":_vm.capabilities.description}}):_vm._e(),_c('SupportedFeatures',{attrs:{\"endpoints\":_vm.capabilities.endpoints,\"headingTag\":\"h3\"}}),(_vm.capabilities.billing)?_c('BillingPlans',{attrs:{\"billing\":_vm.capabilities.billing,\"headingTag\":\"h3\"}}):_vm._e(),(_vm.federated)?_c('div',{staticClass:\"federation\"},[_c('h3',[_vm._v(\"Federation\")]),_c('p',[_vm._v(\"This service is a federation of multiple services, which are all listed below:\")]),_c('ul',_vm._l((_vm.capabilities.federation),function(service,id){return _c('li',{key:id},[_c('div',{staticClass:\"fed-header\"},[_c('strong',{staticClass:\"fed-title\"},[_vm._v(_vm._s(service.title || id))]),_c('ul',{staticClass:\"badges small inline\"},[(service.status === 'offline')?_c('li',{staticClass:\"badge red\",attrs:{\"title\":_vm.offlineTitle(service)}},[_vm._v(\"offline\")]):_c('li',{staticClass:\"badge green\"},[_vm._v(\"online\")])])]),_c('small',[_vm._v(\"URL: \"+_vm._s(service.url))]),(service.description)?_c('Description',{attrs:{\"description0\":service.description,\"compact\":true}}):_vm._e(),(service.last_status_check)?_c('small',[_vm._v(\"Last check: \"+_vm._s(_vm._f(\"timestamp\")(service.last_status_check)))]):_vm._e()],1)}),0)]):_vm._e(),_c('LinkList',{attrs:{\"links\":_vm.capabilities.links,\"heading\":\"More information\",\"headingTag\":\"h3\"}})],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
{{ title }} \n\t\t
\n\t\t\tURL: {{ url2 }}
\n\t\t\topenEO-Version: {{ capabilities.api_version }}
\n\t\t\tProduction: \n\t\t\t\t✔️ \n\t\t\t\t❌ \n\t\t\t
\n\t\t \n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
Federation \n\t\t\t
This service is a federation of multiple services, which are all listed below:
\n\t\t\t
\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tURL: {{ service.url }} \n\t\t\t\t\t \n\t\t\t\t\tLast check: {{ service.last_status_check | timestamp }} \n\t\t\t\t \n\t\t\t \n\t\t
\n\t\t
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Capabilities.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Capabilities.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Capabilities.vue?vue&type=template&id=4dbaca8c\"\nimport script from \"./Capabilities.vue?vue&type=script&lang=js\"\nexport * from \"./Capabilities.vue?vue&type=script&lang=js\"\nimport style0 from \"./Capabilities.vue?vue&type=style&index=0&id=4dbaca8c&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component collections\"},[_c('SearchableList',{attrs:{\"data\":_vm.collections,\"identifierKey\":\"id\",\"summaryKey\":\"title\",\"keywordsKey\":\"keywords\",\"showKeywords\":_vm.showKeywords,\"showSummaryOnExpand\":false,\"externalSearchTerm\":_vm.searchTerm,\"externalHideDeprecated\":_vm.hideDeprecated,\"deprecatedFilter\":_vm.deprecatedFilter,\"sort\":_vm.sort,\"offerDetails\":_vm.offerDetails,\"heading\":_vm.heading,\"collapsed\":_vm.collapsed,\"loadAdditionalData\":_vm.loadAdditionalData,\"allowCopy\":\"\"},on:{\"detailsToggled\":_vm.detailsToggled},scopedSlots:_vm._u([{key:\"heading\",fn:function(scope){return [_vm._t(\"heading\",null,null,scope)]}},(_vm.missing)?{key:\"content-start\",fn:function(){return [_c('FederationMissingNotice',{attrs:{\"missing\":_vm.missing,\"federation\":_vm.federation}})]},proxy:true}:null,{key:\"summary\",fn:function(scope){return [_vm._t(\"summary\",null,null,scope)]}},{key:\"details\",fn:function(slot){return [_c('Collection',{attrs:{\"data\":slot.item,\"mapOptions\":_vm.mapOptions,\"federation\":_vm.federation},scopedSlots:_vm._u([{key:\"title\",fn:function(){return [_c('span',{staticClass:\"hidden\"})]},proxy:true},{key:\"before-description\",fn:function(scope){return [_vm._t(\"collection-before-description\",null,null,scope)]}},{key:\"end\",fn:function(scope){return [_vm._t(\"collection-end\",null,null,scope)]}},{key:\"spatial-extents\",fn:function(scope){return [_vm._t(\"collection-spatial-extents\",null,null,scope)]}},{key:\"temporal-extents\",fn:function(scope){return [_vm._t(\"collection-temporal-extents\",null,null,scope)]}}],null,true)})]}}],null,true)})],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t\n\t\t\t \n\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t \n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Collections.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Collections.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Collections.vue?vue&type=template&id=cc7cbcee\"\nimport script from \"./Collections.vue?vue&type=script&lang=js\"\nexport * from \"./Collections.vue?vue&type=script&lang=js\"\nimport style0 from \"./Collections.vue?vue&type=style&index=0&id=cc7cbcee&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component data-table\"},[_c('div',{staticClass:\"menu\"},[_c('div',{staticClass:\"toolbar\"},[_vm._t(\"toolbar\")],2),(_vm.hasData)?_c('div',{staticClass:\"filter\"},[_c('SearchBox',{attrs:{\"compact\":true},model:{value:(_vm.filterValue),callback:function ($$v) {_vm.filterValue=$$v},expression:\"filterValue\"}})],1):_vm._e()]),(_vm.hasData)?_c('table',[_c('thead',[_c('tr',_vm._l((_vm.columns),function(col,id){return _c('th',{directives:[{name:\"show\",rawName:\"v-show\",value:(!col.hide),expression:\"!col.hide\"}],key:col.name,class:_vm.thClasses(id),attrs:{\"title\":_vm.thTitle(id)},on:{\"click\":function($event){return _vm.enableSort(id)}}},[_vm._v(_vm._s(col.name))])}),0)]),_c('tbody',[_vm._l((_vm.view),function(row,i){return _c('tr',{key:i},_vm._l((_vm.columns),function(col,id){return _c('td',{directives:[{name:\"show\",rawName:\"v-show\",value:(!col.hide),expression:\"!col.hide\"}],key:`${col.name}_${i}`,class:[id, {'edit': _vm.canEdit(col)}],attrs:{\"title\":_vm.canEdit(col) ? 'Double-click to change the value' : false,\"data-value\":col.stylable ? _vm.value(row, col, id) : false},on:{\"dblclick\":function($event){return _vm.onDblClick($event, row, col, id)}}},[_vm._t(id,function(){return [(_vm.showEditField(row, col, id))?[_c('form',{on:{\"submit\":function($event){$event.preventDefault();$event.stopPropagation();return _vm.saveEditField($event, row, col, id)}}},[_c('input',{ref:\"editField\",refInFor:true,attrs:{\"type\":\"text\"},domProps:{\"value\":_vm.value(row, col, id)},on:{\"blur\":function($event){return _vm.saveEditField($event, row, col, id)},\"keyup\":function($event){return _vm.resetEditFieldEsc($event, row, col, id)}}})])]:_c('span',{domProps:{\"innerHTML\":_vm._s(_vm.formattedValue(row, col, id))}})]},{\"row\":row,\"col\":col,\"id\":id})],2)}),0)}),(_vm.hasData && _vm.view.length == 0)?_c('tr',{staticClass:\"no-results\"},[_c('td',{attrs:{\"colspan\":_vm.columnCount}},[_vm._v(\"No element matches your search criteria.\")])]):_vm._e()],2)]):_c('div',{staticClass:\"no-data\"},[_vm._v(_vm._s(_vm.noDataMessage))])])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t\n\t\t
\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ col.name }} \n\t\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t\t\n\t\t\t\t\tNo element matches your search criteria. \n\t\t\t\t \n\t\t\t \n\t\t
\n\t\t
{{ noDataMessage }}
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./DataTable.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./DataTable.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./DataTable.vue?vue&type=template&id=39699eb8\"\nimport script from \"./DataTable.vue?vue&type=script&lang=js\"\nexport * from \"./DataTable.vue?vue&type=script&lang=js\"\nimport style0 from \"./DataTable.vue?vue&type=style&index=0&id=39699eb8&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component file-formats\"},[_c('SearchableList',{attrs:{\"data\":_vm.fileFormats,\"summaryKey\":\"title\",\"keywordsKey\":\"gis_data_types\",\"showSummaryOnExpand\":false,\"externalSearchTerm\":_vm.searchTerm,\"externalHideDeprecated\":_vm.hideDeprecated,\"deprecatedFilter\":_vm.deprecatedFilter,\"sort\":_vm.sort,\"offerDetails\":_vm.offerDetails,\"heading\":_vm.heading,\"collapsed\":_vm.collapsed},on:{\"detailsToggled\":_vm.detailsToggled},scopedSlots:_vm._u([{key:\"heading\",fn:function(scope){return [_vm._t(\"heading\",null,null,scope)]}},(_vm.missing)?{key:\"content-start\",fn:function(){return [_c('FederationMissingNotice',{attrs:{\"missing\":_vm.missing,\"federation\":_vm.federation}})]},proxy:true}:null,{key:\"summary\",fn:function(slot){return [_vm._t(\"summary\",function(){return [_c('strong',{staticClass:\"inline\"},[_vm._v(_vm._s(slot.item.name))]),_c('ul',{staticClass:\"badges small inline\"},[(_vm.showAll && slot.item.type === 'input')?_c('li',{staticClass:\"badge option1\"},[_vm._v(\"Import\")]):_vm._e(),(_vm.showAll && slot.item.type === 'output')?_c('li',{staticClass:\"badge option2\"},[_vm._v(\"Export\")]):_vm._e(),(Array.isArray(slot.item.gis_data_types))?_vm._l((slot.item.gis_data_types),function(type){return _c('li',{key:type,staticClass:\"badge gis\"},[_vm._v(_vm._s(type))])}):_vm._e()],2),_c('br'),_c('small',[_vm._v(_vm._s(slot.summary.summary))])]},null,slot)]}},{key:\"details\",fn:function(slot){return [_c('FileFormat',{attrs:{\"id\":slot.summary.identifier,\"format\":slot.item,\"type\":slot.item.type,\"federation\":_vm.federation},scopedSlots:_vm._u([{key:\"title\",fn:function(){return [_c('span',{staticClass:\"hidden\"})]},proxy:true},{key:\"badges\",fn:function(){return [_c('span',{staticClass:\"hidden\"})]},proxy:true},{key:\"before-description\",fn:function(scope){return [_vm._t(\"file-format-before-description\",null,null,scope)]}},{key:\"end\",fn:function(scope){return [_vm._t(\"file-format-end\",null,null,scope)]}}],null,true)})]}}],null,true)})],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
\n\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ slot.item.name }} \n\t\t\t\t\t\n\t\t\t\t\t\tImport \n\t\t\t\t\t\tExport \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t{{ type }} \n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t{{ slot.summary.summary }} \n\t\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t \n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./FileFormats.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./FileFormats.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./FileFormats.vue?vue&type=template&id=0632d382\"\nimport script from \"./FileFormats.vue?vue&type=script&lang=js\"\nexport * from \"./FileFormats.vue?vue&type=script&lang=js\"\nimport style0 from \"./FileFormats.vue?vue&type=style&index=0&id=0632d382&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component items\"},[_c('SearchableList',{ref:\"list\",attrs:{\"data\":_vm.data,\"summaryKey\":\"title\",\"showSummaryOnExpand\":false,\"externalSearchTerm\":_vm.searchTerm,\"externalHideDeprecated\":_vm.hideDeprecated,\"deprecatedFilter\":_vm.deprecatedFilter,\"sort\":_vm.sort,\"offerDetails\":_vm.offerDetails,\"heading\":_vm.heading,\"collapsed\":_vm.collapsed,\"loadAdditionalData\":_vm.loadAdditionalData},on:{\"summaries\":_vm.updateFeatures,\"detailsToggled\":_vm.detailsToggled},scopedSlots:_vm._u([{key:\"heading\",fn:function(scope){return [_vm._t(\"heading\",null,null,scope)]}},{key:\"after-search-box\",fn:function(){return [(_vm.showMap)?_vm._t(\"map\",function(){return [_c('div',{ref:\"mapContainer\",staticClass:\"map overview\"})]},{\"geojson\":_vm.geojson,\"mapOptions\":_vm.mapOptions}):_vm._e()]},proxy:true},{key:\"summary\",fn:function(scope){return [_vm._t(\"summary\",null,null,scope)]}},{key:\"details\",fn:function(slot){return [_c('Item',{attrs:{\"data\":slot.item,\"mapOptions\":_vm.mapOptions},scopedSlots:_vm._u([{key:\"title\",fn:function(){return [_c('span',{staticClass:\"hidden\"})]},proxy:true},{key:\"before-description\",fn:function(scope){return [_vm._t(\"item-before-description\",null,null,scope)]}},{key:\"end\",fn:function(scope){return [_vm._t(\"item-end\",null,null,scope)]}},{key:\"location\",fn:function(scope){return [_vm._t(\"item-location\",null,null,scope)]}}],null,true)})]}}],null,true)})],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
\n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t
\n\t\t\t\t \n\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\t- \n\t\t\t\t\t
\n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t \n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Items.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Items.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Items.vue?vue&type=template&id=b90455ce\"\nimport script from \"./Items.vue?vue&type=script&lang=js\"\nexport * from \"./Items.vue?vue&type=script&lang=js\"\nimport style0 from \"./Items.vue?vue&type=style&index=0&id=b90455ce&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('article',{staticClass:\"vue-component job\"},[_vm._t(\"title\",function(){return [_c('a',{staticClass:\"anchor\",attrs:{\"name\":_vm.job.id}}),_c('h2',[_vm._v(_vm._s(_vm.job.title || _vm.job.id))])]},null,_vm.$props),_c('section',{staticClass:\"progress\"},[_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"ID:\")]),_c('code',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.job.id))])]),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Submitted:\")]),_c('span',{staticClass:\"value\",domProps:{\"innerHTML\":_vm._s(_vm.created)}})]),(_vm.updated)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Updated:\")]),_c('span',{staticClass:\"value\",domProps:{\"innerHTML\":_vm._s(_vm.updated)}})]):_vm._e(),(_vm.job.status)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Status:\")]),_c('span',{staticClass:\"value status\",attrs:{\"data-value\":_vm.job.status}},[_vm._v(_vm._s(_vm.job.status))])]):_vm._e(),(typeof _vm.job.progress === 'number')?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Progress:\")]),_c('div',{staticClass:\"value\"},[_c('div',{staticClass:\"progressBar\",class:{error: !!_vm.job.error}},[_c('div',{staticClass:\"completed\",style:('width: ' + _vm.progress)},[(_vm.job.progress > 50)?_c('span',{staticClass:\"number\"},[_vm._v(_vm._s(_vm.progress))]):_vm._e(),_vm._v(\" \")]),(_vm.job.progress <= 50)?_c('span',{staticClass:\"number\"},[_vm._v(_vm._s(_vm.progress))]):_vm._e()])])]):_vm._e(),(_vm.job.log_level)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Minimum severity for logs:\")]),_c('span',{staticClass:\"value level\",attrs:{\"data-value\":_vm.job.log_level}},[_vm._v(_vm._s(_vm.job.log_level))])]):_vm._e()]),_vm._t(\"before-description\",null,null,_vm.$props),(_vm.job.description)?_c('summary',{staticClass:\"description\"},[_c('h3',[_vm._v(\"Description\")]),_c('Description',{attrs:{\"description\":_vm.job.description}}),(_vm.job['federation:missing'])?_c('FederationMissingNotice',{attrs:{\"missing\":_vm.job['federation:missing'],\"federation\":_vm.federation}}):_vm._e()],1):_vm._e(),(_vm.hasLinks)?_c('section',{staticClass:\"links\"},[_c('LinkList',{attrs:{\"links\":_vm.job.links,\"heading\":\"Additional Resources\",\"headingTag\":\"h3\"}})],1):_vm._e(),(_vm.job.plan || _vm.costs || _vm.budget)?_c('section',{staticClass:\"billing\"},[_c('h3',[_vm._v(\"Billing\")]),(_vm.job.plan)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Billing plan:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.job.plan))])]):_vm._e(),(_vm.costs)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Incurred Costs:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.costs))])]):_vm._e(),(_vm.budget)?_c('div',{staticClass:\"tabular\"},[_c('label',{attrs:{\"title\":\"The allowed maximum costs, specified by the user.\"}},[_vm._v(\"Budget limit:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.budget))])]):_vm._e()]):_vm._e(),(_vm.hasUsageMetrics)?_c('section',{staticClass:\"usage\"},[_c('h3',[_vm._v(\"Usage Metrics\")]),_vm._l((_vm.usage),function(metric,key){return _c('div',{key:key,staticClass:\"tabular\"},[_c('label',{staticClass:\"metric\"},[_vm._v(_vm._s(_vm._f(\"usageLabel\")(key)))]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm._f(\"usageValue\")(metric.value))+\" \"),_c('span',{staticClass:\"unit\"},[_vm._v(_vm._s(metric.unit))])])])})],2):_vm._e(),_c('section',{staticClass:\"process\"},[_c('h3',[_vm._v(\"Process\")]),_c('Process',{staticClass:\"inline\",attrs:{\"process\":_vm.job.process,\"provideDownload\":false,\"showGraph\":true},scopedSlots:_vm._u([{key:\"process-graph\",fn:function(p){return [_vm._t(\"process-graph\",null,{\"vBind\":p})]}}],null,true)})],1),_vm._t(\"end\",null,null,_vm.$props)],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","import Utils from '../../utils';\n\nconst usageLabels = {\n\tcpu: \"CPU usage\",\n\tmemory: \"Memory usage\",\n\tduration: \"Wall time\",\n\tnetwork: \"Network Transfer IO\",\n\tdisk: \"Storage IO\",\n\tstorage: \"Storage space\"\n};\n\nexport default {\n\tcomputed: {\n\t\thasUsageMetrics() {\n\t\t\treturn Utils.size(this.usage) > 0;\n\t\t}\n\t},\n\tfilters: {\n\t\tusageLabel(key) {\n\t\t\tif (usageLabels[key]) {\n\t\t\t\treturn usageLabels[key];\n\t\t\t}\n\t\t\telse {\n\t\t\t\treturn Utils.prettifyString(key);\n\t\t\t}\n\t\t},\n\t\tusageValue(num) {\n\t\t\tif (typeof num === 'number') {\n\t\t\t\treturn num.toLocaleString();\n\t\t\t}\n\t\t\treturn num;\n\t\t}\n\t}\n};","\n\t\n\n\t\t\n\t\t\t \n\t\t\t{{ job.title || job.id }} \n\t\t \n\n\t\t\n\t\t\t\n\t\t\t\tID: \n\t\t\t\t{{ job.id }}
\n\t\t\t
\n\n\t\t\t\n\t\t\t\tSubmitted: \n\t\t\t\t \n\t\t\t
\n\n\t\t\t\n\t\t\t\tUpdated: \n\t\t\t\t \n\t\t\t
\n\n\t\t\t\n\t\t\t\tStatus: \n\t\t\t\t{{ job.status }} \n\t\t\t
\n\n\t\t\t\n\t\t\t\t
Progress: \n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t 50\">{{ progress }} \n\t\t\t\t\t\t
\n\t\t\t\t\t\t
{{ progress }} \n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t
\n\n\t\t\t\n\t\t\t\tMinimum severity for logs: \n\t\t\t\t{{ job.log_level }} \n\t\t\t
\n\t\t \n\n\t\t \n\n\t\t\n\t\t\tDescription \n\t\t\t \n\n\t\t\t \n\t\t \n\n\t\t\n\n\t\t\n\t\t\tBilling \n\t\t\t\n\t\t\t\tBilling plan: \n\t\t\t\t{{ job.plan }} \n\t\t\t
\n\n\t\t\t\n\t\t\t\tIncurred Costs: \n\t\t\t\t{{ costs }} \n\t\t\t
\n\n\t\t\t\n\t\t\t\tBudget limit: \n\t\t\t\t{{ budget }} \n\t\t\t
\n\t\t \n\n\t\t\n\t\t\tUsage Metrics \n\t\t\t\n\t\t\t\t{{ key | usageLabel }} \n\t\t\t\t{{ metric.value | usageValue }} {{ metric.unit }} \n\t\t\t
\n\t\t \n\n\t\t\n\t\t\tProcess \n\t\t\t\n\t\t\t\t \n\t\t\t \n\t\t \n\n\t\t \n\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Job.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Job.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Job.vue?vue&type=template&id=ca96e6cc\"\nimport script from \"./Job.vue?vue&type=script&lang=js\"\nexport * from \"./Job.vue?vue&type=script&lang=js\"\nimport style0 from \"./Job.vue?vue&type=style&index=0&id=ca96e6cc&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component estimate\"},[_c('h2',[_vm._v(\"Estimate\")]),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Costs:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.costs))])]),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Runtime:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.duration))])]),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"File size:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.size))])]),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Downloads included:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.downloadsIncluded))])]),(_vm.expires)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Valid until:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.expires))])]):_vm._e()])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
Estimate \n\n\t\t
\n\t\t\tCosts: \n\t\t\t{{ costs }} \n\t\t
\n\t\t
\n\t\t\tRuntime: \n\t\t\t{{ duration }} \n\t\t
\n\t\t
\n\t\t\tFile size: \n\t\t\t{{ size }} \n\t\t
\n\t\t
\n\t\t\tDownloads included: \n\t\t\t{{ downloadsIncluded }} \n\t\t
\n\t\t
\n\t\t\tValid until: \n\t\t\t{{ expires }} \n\t\t
\n\t
\n \n\n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./JobEstimate.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./JobEstimate.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./JobEstimate.vue?vue&type=template&id=ca1f47d6\"\nimport script from \"./JobEstimate.vue?vue&type=script&lang=js\"\nexport * from \"./JobEstimate.vue?vue&type=script&lang=js\"\nimport style0 from \"./JobEstimate.vue?vue&type=style&index=0&id=ca1f47d6&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component logs\"},[(_vm.hasLogs)?_c('div',{staticClass:\"log-container\"},[_c('div',{staticClass:\"log-header\"},[_c('div',{staticClass:\"log-search\"},[(_vm.externalSearchTerm === null)?_c('SearchBox',{attrs:{\"placeholder\":\"Search in Logs\",\"minLength\":2},model:{value:(_vm.searchTerm),callback:function ($$v) {_vm.searchTerm=$$v},expression:\"searchTerm\"}}):_vm._e(),_c('MultiSelect',{staticClass:\"log-fields\",attrs:{\"options\":_vm.availableFields,\"trackBy\":\"id\",\"label\":\"label\",\"multiple\":true,\"searchable\":false,\"allowEmpty\":false,\"closeOnSelect\":false,\"limit\":3,\"limitText\":count => `+ ${count}`,\"selectedLabel\":\"✓\",\"deselectLabel\":\"✗\",\"selectLabel\":\"+\",\"title\":\"Select the fields to search through\"},model:{value:(_vm.fields),callback:function ($$v) {_vm.fields=$$v},expression:\"fields\"}})],1),_c('MultiSelect',{staticClass:\"log-levels\",attrs:{\"options\":_vm.levels,\"multiple\":true,\"searchable\":false,\"allowEmpty\":false,\"closeOnSelect\":false,\"selectedLabel\":\"✓\",\"deselectLabel\":\"✗\",\"selectLabel\":\"+\",\"title\":\"Select the log levels shown in the list of logs\"},scopedSlots:_vm._u([{key:\"tag\",fn:function(props){return [_c('span',{key:props.index,staticClass:\"multiselect__tag\",class:props.option},[_c('span',{domProps:{\"textContent\":_vm._s(props.option)}}),_c('i',{staticClass:\"multiselect__tag-icon\",attrs:{\"tabindex\":\"1\"},on:{\"keypress\":function($event){if(!$event.type.indexOf('key')&&_vm._k($event.keyCode,\"enter\",13,$event.key,\"Enter\"))return null;$event.preventDefault();return props.remove(props.option)},\"mousedown\":function($event){$event.preventDefault();return props.remove(props.option)}}})])]}}],null,false,1224176521),model:{value:(_vm.levelsShown),callback:function ($$v) {_vm.levelsShown=$$v},expression:\"levelsShown\"}})],1),_c('ul',{staticClass:\"log-body\"},_vm._l((_vm.logs),function(log,i){return _c('Log',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.shown[i]),expression:\"shown[i]\"}],key:log.id,attrs:{\"log\":log,\"startTime\":_vm.startTime}})}),1)]):_c('div',{staticClass:\"log-empty\"},[_vm._v(\"No logs available.\")])])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('li',{staticClass:\"vue-component log-entry\",class:{[_vm.log.level]: true, expanded: _vm.expanded}},[_c('summary',[_c('span',{staticClass:\"toggle\",on:{\"click\":function($event){return _vm.toggle()}}},[_vm._v(\"▸\")]),_c('span',{staticClass:\"log-message\",on:{\"click\":function($event){!_vm.expanded && _vm.toggle()}}},[_vm._v(_vm._s(_vm.log.message))]),(_vm.log.level)?_c('ul',{staticClass:\"badges small inline\"},[(_vm.relativeTime)?_c('li',{staticClass:\"badge time\",attrs:{\"title\":_vm.formattedTime}},[_vm._v(_vm._s(_vm.relativeTime))]):_vm._e(),_c('li',{staticClass:\"badge\",class:_vm.log.level},[_vm._v(_vm._s(_vm.log.level))])]):_vm._e()]),(_vm.expanded)?_c('ul',{staticClass:\"details\"},[(_vm.log.code)?_c('li',[_vm._v(\"Code: \"+_vm._s(_vm.log.code))]):_vm._e(),(Array.isArray(_vm.log.path) && _vm.log.path.length)?_c('li',[_vm._v(\" Path: \"),_c('ol',{staticClass:\"path\"},_vm._l((_vm.path),function(path,i){return _c('li',{key:i},[_c('strong',[_c('code',[_vm._v(\"#\"+_vm._s(path.node_id))])]),(path.process_id)?[_c('br'),_vm._v(\"Process: \"),_c('code',[_vm._v(_vm._s(path.process_id))]),(path.namespace)?[_vm._v(\" in namespace \"),_c('code',[_vm._v(_vm._s(path.namespace))])]:_vm._e(),(path.parameter)?[_c('br'),_vm._v(\"Parameter: \"),_c('code',[_vm._v(_vm._s(path.parameter))])]:_vm._e()]:_vm._e()],2)}),0)]):_vm._e(),(_vm.hasData)?_c('li',[_vm._v(\" Data: \"),_c('ObjectTree',{attrs:{\"data\":_vm.log.data}})],1):_vm._e(),(_vm.hasUsageMetrics)?_c('li',[_vm._v(\" Usage metrics: \"),_c('ul',{staticClass:\"usage\"},_vm._l((_vm.usage),function(metric,key){return _c('li',{key:key},[_c('strong',{staticClass:\"metric\"},[_vm._v(_vm._s(_vm._f(\"usageLabel\")(key)))]),_vm._v(\": \"+_vm._s(_vm._f(\"usageValue\")(metric.value))+\" \"),_c('span',{staticClass:\"unit\"},[_vm._v(_vm._s(metric.unit))])])}),0)]):_vm._e(),_c('li',[_vm._v(\"ID: \"+_vm._s(_vm.log.id))]),(Array.isArray(_vm.log.links) && _vm.log.links.length)?_c('li',[_vm._v(\" Related Resources: \"),_c('LinkList',{attrs:{\"links\":_vm.log.links}})],1):_vm._e()]):_vm._e()])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t\n\t\t\t▸ \n\t\t\t{{ log.message }} \n\t\t\t\n\t\t\t\t{{ relativeTime }} \n\t\t\t\t{{ log.level }} \n\t\t\t \n\t\t \n\t\t\n\t\t\tCode: {{ log.code }} \n\t\t\t\n\t\t\t\tPath:\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t#{{ path.node_id }}
\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t Process: {{ path.process_id }}
\n\t\t\t\t\t\t\t in namespace {{ path.namespace }}
\n\t\t\t\t\t\t\t Parameter: {{ path.parameter }}
\n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\tData:\n\t\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\tUsage metrics:\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t{{ key | usageLabel }} :\n\t\t\t\t\t\t{{ metric.value | usageValue }} {{ metric.unit }} \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t\tID: {{ log.id }} \n\t\t\t\n\t\t\t\tRelated Resources:\n\t\t\t\t \n\t\t\t \n\t\t \n\t \n \n\n\n\n","import mod from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Log.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Log.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Log.vue?vue&type=template&id=55464863\"\nimport script from \"./Log.vue?vue&type=script&lang=js\"\nexport * from \"./Log.vue?vue&type=script&lang=js\"\nimport style0 from \"./Log.vue?vue&type=style&index=0&id=55464863&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","\n\t\n\t\t
\n\t\t
No logs available.
\n\t
\n \n\n\n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Logs.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Logs.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Logs.vue?vue&type=template&id=9ffd447e\"\nimport script from \"./Logs.vue?vue&type=script&lang=js\"\nexport * from \"./Logs.vue?vue&type=script&lang=js\"\nimport style0 from \"vue-multiselect/dist/vue-multiselect.min.css?vue&type=style&index=0&prod&lang=css&external\"\nimport style1 from \"./Logs.vue?vue&type=style&index=1&id=9ffd447e&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component processes\"},[_c('SearchableList',{attrs:{\"data\":_vm.processes,\"keywordsKey\":\"categories\",\"showKeywords\":_vm.showCategories,\"showSummaryOnExpand\":false,\"externalSearchTerm\":_vm.searchTerm,\"externalHideDeprecated\":_vm.hideDeprecated,\"deprecatedFilter\":_vm.deprecatedFilter,\"sort\":_vm.sort,\"offerDetails\":_vm.offerDetails,\"heading\":_vm.heading,\"collapsed\":_vm.collapsed,\"loadAdditionalData\":_vm.loadAdditionalData,\"allowCopy\":\"\"},on:{\"detailsToggled\":_vm.detailsToggled},scopedSlots:_vm._u([{key:\"heading\",fn:function(scope){return [_vm._t(\"heading\",null,null,scope)]}},(_vm.missing)?{key:\"content-start\",fn:function(){return [_c('FederationMissingNotice',{attrs:{\"missing\":_vm.missing,\"federation\":_vm.federation}})]},proxy:true}:null,{key:\"summary\",fn:function(scope){return [_vm._t(\"summary\",null,null,scope)]}},{key:\"details\",fn:function(slot){return [_c('Process',{attrs:{\"process\":slot.item,\"provideDownload\":_vm.provideDownload,\"processUrl\":_vm.processUrl,\"showGraph\":_vm.showGraph,\"federation\":_vm.federation},scopedSlots:_vm._u([{key:\"title\",fn:function(){return [_c('span',{staticClass:\"hidden\"})]},proxy:true},{key:\"before-description\",fn:function(scope){return [_vm._t(\"process-before-description\",null,null,scope)]}},{key:\"end\",fn:function(scope){return [_vm._t(\"process-end\",null,null,scope)]}}],null,true)})]}}],null,true)})],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
\n\t\t\t \n\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t \n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Processes.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Processes.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Processes.vue?vue&type=template&id=4f6baa31\"\nimport script from \"./Processes.vue?vue&type=script&lang=js\"\nexport * from \"./Processes.vue?vue&type=script&lang=js\"\nimport style0 from \"./Processes.vue?vue&type=style&index=0&id=4f6baa31&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('article',{staticClass:\"vue-component service\"},[_vm._t(\"title\",function(){return [_c('a',{staticClass:\"anchor\",attrs:{\"name\":_vm.service.id}}),_c('h2',[_vm._v(_vm._s(_vm.service.title || _vm.service.id))])]},null,_vm.$props),_c('section',{staticClass:\"basedata\"},[_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"ID:\")]),_c('code',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.service.id))])]),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Type:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.type))])]),(_vm.service.url)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"URL:\")]),_c('code',{staticClass:\"value\"},[_c('a',{attrs:{\"href\":_vm.service.url,\"target\":\"_blank\"}},[_vm._v(_vm._s(_vm.service.url))])])]):_vm._e(),_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Enabled:\")]),_c('span',{staticClass:\"value boolean\"},[(_vm.service.enabled === true)?_c('span',[_vm._v(\"✔️\")]):(_vm.service.enabled === false)?_c('span',[_vm._v(\"❌\")]):_c('span',[_vm._v(\"❓\")])])]),(_vm.service.created)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Created:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.created))])]):_vm._e()]),_vm._t(\"before-description\",null,null,_vm.$props),(_vm.service.description)?_c('summary',{staticClass:\"description\"},[_c('h3',[_vm._v(\"Description\")]),_c('Description',{attrs:{\"description\":_vm.service.description}})],1):_vm._e(),(_vm.hasAttributes)?_c('section',{staticClass:\"attributes\"},[_c('h3',[_vm._v(\"Exposed Capabilities from \"+_vm._s(_vm.type))]),_vm._l((_vm.service.attributes),function(value,key){return _c('div',{key:key,staticClass:\"tabular\"},[_c('label',[_vm._v(_vm._s(_vm._f(\"prettifyKey\")(key))+\":\")]),_c('ObjectTree',{staticClass:\"value\",attrs:{\"data\":value}})],1)})],2):_vm._e(),(_vm.hasConfig)?_c('section',{staticClass:\"parameters\"},[_c('h3',[_vm._v(\"Custom Settings for \"+_vm._s(_vm.type))]),_vm._l((_vm.service.configuration),function(value,key){return _c('div',{key:key,staticClass:\"tabular\"},[_c('label',[_vm._v(_vm._s(_vm._f(\"prettifyKey\")(key))+\":\")]),_c('ObjectTree',{staticClass:\"value\",attrs:{\"data\":value}})],1)})],2):_vm._e(),(_vm.service.plan || _vm.costs || _vm.budget)?_c('section',{staticClass:\"billing\"},[_c('h3',[_vm._v(\"Billing\")]),(_vm.service.plan)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Billing plan:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.service.plan))])]):_vm._e(),(_vm.costs)?_c('div',{staticClass:\"tabular\"},[_c('label',[_vm._v(\"Incurred Costs:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.costs))])]):_vm._e(),(_vm.budget)?_c('div',{staticClass:\"tabular\"},[_c('label',{attrs:{\"title\":\"The allowed maximum costs, specified by the user.\"}},[_vm._v(\"Budget limit:\")]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm.budget))])]):_vm._e()]):_vm._e(),(_vm.hasUsageMetrics)?_c('section',{staticClass:\"usage\"},[_c('h3',[_vm._v(\"Usage Metrics\")]),_vm._l((_vm.usage),function(metric,key){return _c('div',{key:key,staticClass:\"tabular\"},[_c('label',{staticClass:\"metric\"},[_vm._v(_vm._s(_vm._f(\"usageLabel\")(key)))]),_c('span',{staticClass:\"value\"},[_vm._v(_vm._s(_vm._f(\"usageValue\")(metric.value))+\" \"),_c('span',{staticClass:\"unit\"},[_vm._v(_vm._s(metric.unit))])])])})],2):_vm._e(),_c('section',{staticClass:\"process\"},[_c('h3',[_vm._v(\"Process\")]),_c('Process',{staticClass:\"inline\",attrs:{\"process\":_vm.service.process,\"provideDownload\":false,\"showGraph\":true},scopedSlots:_vm._u([{key:\"process-graph\",fn:function(p){return [_vm._t(\"process-graph\",null,{\"vBind\":p})]}}],null,true)})],1),_vm._t(\"end\",null,null,_vm.$props)],2)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\n\t\t\n\t\t\t \n\t\t\t{{ service.title || service.id }} \n\t\t \n\n\t\t\n\t\t\t\n\t\t\t\tID: \n\t\t\t\t{{ service.id }}
\n\t\t\t
\n\n\t\t\t\n\t\t\t\tType: \n\t\t\t\t{{ type }} \n\t\t\t
\n\n\t\t\t\n\n\t\t\t\n\t\t\t\tEnabled: \n\t\t\t\t\n\t\t\t\t\t✔️ \n\t\t\t\t\t❌ \n\t\t\t\t\t❓ \n\t\t\t\t \n\t\t\t
\n\n\t\t\t\n\t\t\t\tCreated: \n\t\t\t\t{{ created }} \n\t\t\t
\n\t\t \n\n\t\t \n\n\t\t\n\t\t\tDescription \n\t\t\t \n\t\t \n\n\t\t\n\t\t\tExposed Capabilities from {{ type }} \n\t\t\t\n\t\t\t\t{{ key | prettifyKey }}: \n\t\t\t\t \n\t\t\t
\n\t\t \n\n\t\t\n\t\t\tCustom Settings for {{ type }} \n\t\t\t\n\t\t\t\t{{ key | prettifyKey }}: \n\t\t\t\t \n\t\t\t
\n\t\t \n\n\t\t\n\t\t\tBilling \n\t\t\t\n\t\t\t\tBilling plan: \n\t\t\t\t{{ service.plan }} \n\t\t\t
\n\n\t\t\t\n\t\t\t\tIncurred Costs: \n\t\t\t\t{{ costs }} \n\t\t\t
\n\n\t\t\t\n\t\t\t\tBudget limit: \n\t\t\t\t{{ budget }} \n\t\t\t
\n\t\t \n\n\t\t\n\t\t\tUsage Metrics \n\t\t\t\n\t\t\t\t{{ key | usageLabel }} \n\t\t\t\t{{ metric.value | usageValue }} {{ metric.unit }} \n\t\t\t
\n\t\t \n\n\t\t\n\t\t\tProcess \n\t\t\t\n\t\t\t\t \n\t\t\t \n\t\t \n\n\t\t \n\n\t \n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Service.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Service.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Service.vue?vue&type=template&id=1f0087bc\"\nimport script from \"./Service.vue?vue&type=script&lang=js\"\nexport * from \"./Service.vue?vue&type=script&lang=js\"\nimport style0 from \"./Service.vue?vue&type=style&index=0&id=1f0087bc&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component service-types\"},[_c('SearchableList',{attrs:{\"data\":_vm.services,\"summaryKey\":\"title\",\"externalSearchTerm\":_vm.searchTerm,\"externalHideDeprecated\":_vm.hideDeprecated,\"deprecatedFilter\":_vm.deprecatedFilter,\"sort\":_vm.sort,\"offerDetails\":_vm.offerDetails,\"heading\":_vm.heading,\"collapsed\":_vm.collapsed},on:{\"detailsToggled\":_vm.detailsToggled},scopedSlots:_vm._u([{key:\"heading\",fn:function(scope){return [_vm._t(\"heading\",null,null,scope)]}},{key:\"summary\",fn:function(scope){return [_vm._t(\"summary\",null,null,scope)]}},{key:\"details\",fn:function(slot){return [_c('ServiceType',{attrs:{\"id\":slot.summary.identifier,\"service\":slot.item,\"federation\":_vm.federation},scopedSlots:_vm._u([{key:\"title\",fn:function(){return [_c('span',{staticClass:\"hidden\"})]},proxy:true},{key:\"before-description\",fn:function(scope){return [_vm._t(\"service-type-before-description\",null,null,scope)]}},{key:\"end\",fn:function(scope){return [_vm._t(\"service-type-end\",null,null,scope)]}}],null,true)})]}}],null,true)})],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t\n\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t \n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ServiceTypes.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./ServiceTypes.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./ServiceTypes.vue?vue&type=template&id=93421ce8\"\nimport script from \"./ServiceTypes.vue?vue&type=script&lang=js\"\nexport * from \"./ServiceTypes.vue?vue&type=script&lang=js\"\nimport style0 from \"./ServiceTypes.vue?vue&type=style&index=0&id=93421ce8&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"vue-component udf-runtimes\"},[_c('SearchableList',{attrs:{\"data\":_vm.runtimes,\"summaryKey\":\"title\",\"showSummaryOnExpand\":false,\"externalSearchTerm\":_vm.searchTerm,\"externalHideDeprecated\":_vm.hideDeprecated,\"deprecatedFilter\":_vm.deprecatedFilter,\"sort\":_vm.sort,\"offerDetails\":_vm.offerDetails,\"heading\":_vm.heading,\"collapsed\":_vm.collapsed},on:{\"detailsToggled\":_vm.detailsToggled},scopedSlots:_vm._u([{key:\"heading\",fn:function(scope){return [_vm._t(\"heading\",null,null,scope)]}},{key:\"summary\",fn:function(slot){return [_vm._t(\"summary\",function(){return [_c('strong',{staticClass:\"inline\"},[_vm._v(_vm._s(slot.summary.identifier))]),_c('ul',{staticClass:\"badges small inline\"},[(slot.item.type === 'docker' || (slot.item.docker && slot.item.tags))?[_c('li',{staticClass:\"badge docker\"},[_vm._v(\"Docker\")]),_vm._l((slot.item.tags),function(tag){return _c('li',{key:tag,staticClass:\"badge version\",class:{default: tag === slot.item.default}},[_vm._v(_vm._s(tag))])})]:_vm._l((slot.item.versions),function(lang,version){return _c('li',{key:version,staticClass:\"badge version\",class:{default: version === slot.item.default}},[_vm._v(_vm._s(version))])})],2),_c('br'),_c('small',[_vm._v(_vm._s(slot.summary.summary))])]},null,slot)]}},{key:\"details\",fn:function(slot){return [_c('UdfRuntime',{attrs:{\"id\":slot.summary.identifier,\"runtime\":slot.item,\"federation\":_vm.federation},scopedSlots:_vm._u([{key:\"title\",fn:function(){return [_c('span',{staticClass:\"hidden\"})]},proxy:true},{key:\"badges\",fn:function(scope){return [_vm._t(\"udf-runtime-badges\",null,null,scope)]}},{key:\"before-description\",fn:function(scope){return [_vm._t(\"udf-runtime-before-description\",null,null,scope)]}}],null,true)})]}}],null,true)})],1)\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\t
\n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t{{ slot.summary.identifier }} \n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDocker \n\t\t\t\t\t\t\t{{ tag }} \n\t\t\t\t\t\t \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t{{ version }} \n\t\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t{{ slot.summary.summary }} \n\t\t\t\t \n\t\t\t \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t \n\t\t \n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./UdfRuntimes.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./UdfRuntimes.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./UdfRuntimes.vue?vue&type=template&id=4329a079\"\nimport script from \"./UdfRuntimes.vue?vue&type=script&lang=js\"\nexport * from \"./UdfRuntimes.vue?vue&type=script&lang=js\"\nimport style0 from \"./UdfRuntimes.vue?vue&type=style&index=0&id=4329a079&prod&lang=scss\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","\n\t\n\t\t
Examples \n\t\t
\n\t\t\t
{{ component }} \n\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t➥ {{ id }}\n\t\t\t\t\t\n\t\t\t\t\t\tRemove Component \n\t\t\t\t\t\tLoad Component \n\t\t\t\t\t \n\t\t\t\t \n\t\t\t\t
\n\t\t\t\t\t \n\t\t\t\t
\n\t\t\t
\n\t\t
\n\t
\n \n\n\n\n","import mod from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Examples.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../node_modules/thread-loader/dist/cjs.js!../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Examples.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Examples.vue?vue&type=template&id=7c59aac6\"\nimport script from \"./Examples.vue?vue&type=script&lang=js\"\nexport * from \"./Examples.vue?vue&type=script&lang=js\"\nimport style0 from \"./Examples.vue?vue&type=style&index=0&id=7c59aac6&prod&lang=css\"\n\n\n/* normalize component */\nimport normalizer from \"!../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","import Vue from 'vue';\nimport Examples from './Examples.vue';\n\nVue.config.productionTip = false;\n\nnew Vue({\n render: h => h(Examples),\n}).$mount('#app');","let collectionGee = require('./examples/collection-gee-s2.json');\nlet collectionEurac = require('./examples/collection-eurac.json');\nlet collections = require('./examples/collections-gee.json');\nlet item = require('./examples/item.json');\nlet itemCeos = require('./examples/item-ceos.json');\nlet items = require('./examples/items-ard-eodc.json');\nlet job = require('./examples/job.json');\nlet logs = require('./examples/logs.json');\nlet processes = require('./examples/processes.json');\nlet udp = require('./examples/process-array-find-nodata.json');\nlet runtimes = require('./examples/udf-runtimes-api.json');\nlet capabilities = require('./examples/capabilities-api.json');\nlet fileFormatsApi = require('./examples/file-formats-api.json');\nlet fileFormatsVito = require('./examples/file-formats-vito.json');\nlet serviceTypes = require('./examples/service-types-api.json');\nlet service = require('./examples/service.json');\nlet reduceApi = require('./examples/reduce-api-new.json');\nlet longArray = [...Array(1000).keys()];\n\nlet mbSample = require('./examples/model-builder/sample.json');\nlet maskScl = require('./examples/model-builder/mask_scl_dilation.json');\nlet clip = require('./examples/model-builder/clip.json');\nlet ardSR = require('./examples/model-builder/ard_surface_reflectance.json');\nlet slow = require('./examples/model-builder/slow.json');\nlet externalProc = require('./examples/model-builder/external_process.json');\n\nmodule.exports = {\n\t\"billing-plans\": {\n\t\t\"api\": {\n\t\t\t\"billing\": capabilities.billing\n\t\t}\n\t},\n\t\"capabilities\": {\n\t\t\"api\": {\n\t\t\t\"capabilities\": capabilities,\n\t\t\t\"url\": 'https://cool-eo-cloud.com'\n\t\t}\n\t},\n\t\"collection\": {\n\t\t\"gee\": {\n\t\t\t\"data\": collectionGee\n\t\t},\n\t\t\"eurac-mapbox\": {\n\t\t\t\"data\": collectionEurac,\n\t\t\t\"mapOptions\": {\n\t\t\t\t\"basemap\": \"https://api.mapbox.com/styles/v1/mapbox/light-v10/tiles/{z}/{x}/{y}?access_token=pk.eyJ1Ijoib3BlbmVvIiwiYSI6ImNreDRzdjNibDE5a2cyd3BoOXFyamd2ZTcifQ._epD9HcfAnGndfgwXmz_3A\",\n\t\t\t\t\"attribution\": '© Mapbox © OpenStreetMap Improve this map '\n\t\t\t},\n\t\t\t\"federation\": capabilities.federation\n\t\t}\n\t},\n\t\"collections\": {\n\t\t\"gee\": {\n\t\t\t\"collections\": collections,\n\t\t\t\"federation\": capabilities.federation,\n\t\t\t\"deprecated-filter\": true,\n\t\t\t\"hide-deprecated\": true\n\t\t},\n\t\t\"collapsed\": {\n\t\t\t\"collections\": collections,\n\t\t\t\"collapsed\": true\n\t\t},\n\t\t\"expanded-but-collapsible-with-keywords\": {\n\t\t\t\"collections\": collections,\n\t\t\t\"collapsed\": false,\n\t\t\t\"show-keywords\": true,\n\t\t\t\"missing\": [\"wwu\"],\n\t\t\t\"federation\": capabilities.federation\n\t\t}\n\t},\n\t\"data-table\": {\n\t\t\"jobs\": {\n\t\t\t\"data\": [\n\t\t\t\t{\"id\":\"mrzFnnApHC0rwxJu\",\"title\":\"Another Job\",\"description\":null,\"status\":\"finished\",\"created\":\"2020-11-17T12:42:54Z\",\"updated\":\"2020-11-17T14:22:24Z\",\"plan\":\"free\",\"costs\":0,\"budget\":null},\n\t\t\t\t{\"id\":\"1324657890\",\"title\":\"Second Job\",\"description\":\"A test!\",\"status\":\"submitted\",\"created\":\"2020-11-18T14:00:12+01:00\",\"updated\":\"2020-11-18T14:00:12+01:00\"}\n\t\t\t],\n\t\t\t\"columns\": {\n\t\t\t\tid: {\n\t\t\t\t\tname: 'ID',\n\t\t\t\t\tprimaryKey: true\n\t\t\t\t},\n\t\t\t\ttitle: {\n\t\t\t\t\tname: 'Title'\n\t\t\t\t},\n\t\t\t\tstatus: {\n\t\t\t\t\tname: 'Status',\n\t\t\t\t\tstylable: true\n\t\t\t\t},\n\t\t\t\tcreated: {\n\t\t\t\t\tname: 'Submitted',\n\t\t\t\t\tformat: 'Timestamp',\n\t\t\t\t\tsort: 'desc'\n\t\t\t\t},\n\t\t\t\tupdated: {\n\t\t\t\t\tname: 'Last update',\n\t\t\t\t\tformat: 'Timestamp'\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\t\"deprecation-notice\": {\n\t\t\"process\": {\n\t\t\t\"entity\": \"process\"\n\t\t}\n\t},\n\t\"description\": {\n\t\t\"general\": {\n\t\t\t\"description\": `\n# H1\n\nNormal text and \\`code\\`\n\n## H2\n\n**Bold**\n\nNo HTML:\n\n \n\n### H3\n\n*Italic*\n\n> Blockquote\n\n#### H4\n\n\\`\\`\\`js\nif (a == b) console.log(\"Hello World\");\n\\`\\`\\`\n\n##### H5 \n\n[Link](http://a.com)\n\n###### H6\n\n![Image](https://commonmark.org/help/images/favicon.png)\n\n* Unordered List\n* Foo\n* Bar\n\n---\n\n1. Ordered List\n2. X\n3. Y\n`\n\t\t}\n\t},\n\t\"experimental-notice\": {\n\t\t\"process\": {\n\t\t\t\"entity\": \"process\"\n\t\t}\n\t},\n\t\"federation-missing-notice\": {\n\t\t\"example\": {\n\t\t\t\"missing\": [\"wwu\", \"eodc\"],\n\t\t\t\"federation\": capabilities.federation,\n\t\t\t\"retry\": () => alert(\"Clicked refresh button\")\n\t\t}\n\t},\n\t\"file-format\": {\n\t\t\"output-gtiff\": {\n\t\t\t\"id\": \"GTIFF\",\n\t\t\t\"format\": fileFormatsApi.output.GTiff,\n\t\t\t\"type\": \"output\"\n\t\t},\n\t\t\"output-gpkg\": {\n\t\t\t\"id\": \"GPKG\",\n\t\t\t\"format\": fileFormatsApi.output.GPKG,\n\t\t\t\"type\": \"output\"\n\t\t},\n\t\t\"input-gpkg\": {\n\t\t\t\"id\": \"GPKG\",\n\t\t\t\"format\": fileFormatsApi.input.GPKG,\n\t\t\t\"type\": \"input\"\n\t\t}\n\t},\n\t\"file-formats\": {\n\t\t\"api-all\": {\n\t\t\t\"formats\": fileFormatsApi\n\t\t},\n\t\t\"api-input\": {\n\t\t\t\"formats\": fileFormatsApi,\n\t\t\t\"show-output\": false\n\t\t},\n\t\t\"api-output\": {\n\t\t\t\"formats\": fileFormatsApi,\n\t\t\t\"show-input\": false\n\t\t},\n\t\t\"vito\": {\n\t\t\t\"formats\": fileFormatsVito,\n\t\t\t\"federation\": capabilities.federation\n\t\t},\n\t},\n\t\"item\": {\n\t\t\"batch-job\": {\n\t\t\t\"data\": item\n\t\t},\n\t\t\"ceos\": {\n\t\t\t\"data\": itemCeos\n\t\t}\n\t},\n\t\"items\": {\n\t\t\"ard-eodc\": {\n\t\t\t\"items\": items,\n\t\t\t\"show-map\": true\n\t\t}\n\t},\n\t\"job\": {\n\t\t\"example\": {\n\t\t\t\"job\": job,\n\t\t\t\"currency\": \"EUR\"\n\t\t}\n\t},\n\t\"job-estimate\": {\n\t\t\"minimal\": {\n\t\t\testimate: {},\n\t\t\tcurrency: \"EUR\"\n\t\t},\n\t\t\"full\": {\n\t\t\testimate: {\n\t\t\t\tcosts: 12.98,\n\t\t\t\tduration: \"P1Y2M10DT2H30M\",\n\t\t\t\tsize: 15000000,\n\t\t\t\tdownloads_included: 5,\n\t\t\t\texpires: \"2020-11-01T00:00:00Z\"\n\t\t\t},\n\t\t\tcurrency: \"DKK\"\n\t\t}\n\t},\n\t\"json-schema\": {\n\t\t\"callback\": {\n\t\t\t\"schema\": reduceApi.parameters[1].schema\n\t\t}\n\t},\n\t\"link-list\": {\n\t\t\"gee-capabilities-links\": {\n\t\t\t\"links\": capabilities.links,\n\t\t\t\"heading\": \"Google Earth Engine Links\",\n\t\t\t\"heading-tag\": \"h1\",\n\t\t\t\"ignore-rel\": ['self', 'conformance']\n\t\t}\n\t},\n\t\"logs\": {\n\t\t\"empty\": {\n\t\t\t\"logs\": []\n\t\t},\n\t\t\"example\": {\n\t\t\t\"logs\": logs.logs\n\t\t}\n\t},\n\t\"model-builder\": {\n\t\t\"empty\": {\n\t\t\t\"id\": \"empty\",\n\t\t\t\"height\": \"100px\"\n\t\t},\n\t\t\"external-process\": {\n\t\t\t\"id\": \"namespace-is-url\",\n\t\t\t\"value\": externalProc\n\t\t},\n\t\t\"sample-viewer-without-metadata\": {\n\t\t\t\"id\": \"sample\",\n\t\t\t\"value\": mbSample\n\t\t},\n\t\t\"sample-editable-without-metadata\": {\n\t\t\t\"id\": \"sample\",\n\t\t\t\"editable\": true,\n\t\t\t\"value\": mbSample\n\t\t},\n\t\t\"sample-editable-with-metadata\": {\n\t\t\t\"id\": \"sample\",\n\t\t\t\"editable\": true,\n\t\t\t\"value\": mbSample,\n\t\t\t\"collections\": collections,\n\t\t\t\"processes\": processes\n\t\t},\n\t\t\"mask-scl-dilation\": {\n\t\t\t\"id\": \"mask\",\n\t\t\t\"editable\": true,\n\t\t\t\"value\": maskScl,\n\t\t\t\"explicit-zoom\": true\n\t\t},\n\t\t\"slow\": {\n\t\t\t\"id\": \"slow\",\n\t\t\t\"editable\": true,\n\t\t\t\"value\": slow,\n\t\t\t\"explicit-zoom\": true,\n\t\t\t\"processes\": processes\n\t\t},\n\t\t\"clip\": {\n\t\t\t\"id\": \"clip\",\n\t\t\t\"value\": clip\n\t\t},\n\t\t\"ard-sr-parameter-used-in-children-editable\": {\n\t\t\t\"id\": \"ard\",\n\t\t\t\"value\": ardSR,\n\t\t\t\"processes\": processes,\n\t\t\t\"editable\": true\n\t\t}\n\t},\n\t\"object-tree\": {\n\t\t\"array\": {\n\t\t\t\"data\": collections\n\t\t},\n\t\t\"object\": {\n\t\t\t\"data\": capabilities\n\t\t},\n\t\t\"scalar\": {\n\t\t\t\"data\": 1000\n\t\t},\n\t\t\"md-array\": {\n\t\t\t\"data\": [longArray,longArray,longArray,longArray,longArray,longArray,longArray]\n\t\t}\n\t},\n\t\"process\": {\n\t\t\"load-collection\": {\n\t\t\t\"process\": processes.find(p => p.id === 'load_collection')\n\t\t},\n\t\t\"extrema\": {\n\t\t\t\"process\": processes.find(p => p.id === 'extrema')\n\t\t},\n\t\t\"api-reduce-new\": {\n\t\t\t\"process\": reduceApi\n\t\t},\n\t\t\"udp-array-find-nodata\": {\n\t\t\t\"process\": udp,\n\t\t\t\"show-graph\": true,\n\t\t\t\"namespace\": \"user\"\n\t\t},\n\t\t\"udp-mask-scl-dilation\": {\n\t\t\t\"process\": maskScl,\n\t\t\t\"show-graph\": true,\n\t\t\t\"namespace\": \"vito\",\n\t\t\t\"federation\": capabilities.federation\n\t\t}\n\t},\n\t\"processes\": {\n\t\t\"1.1.0\": {\n\t\t\t\"processes\": processes,\n\t\t\t\"federation\": capabilities.federation\n\t\t},\n\t\t\"1.1.0-with-categories\": {\n\t\t\t\"processes\": processes,\n\t\t\t\"show-categories\": true\n\t\t}\n\t},\n\t// ToDo: SearchableList - see other implementing components for examples, e.g. Collections or Processes\n\t\"searchable-list\": {\n\t\t\"empty\": {}\n\t},\n\t\"service\": {\n\t\t\"empty\": {\n\t\t\t\"service\": {},\n\t\t\t\"currency\": \"EUR\"\n\t\t},\n\t\t\"example\": {\n\t\t\t\"service\": service,\n\t\t\t\"currency\": \"EUR\"\n\t\t}\n\t},\n\t\"service-type\": {\n\t\t\"wms\": {\n\t\t\t\"id\": \"WMS\",\n\t\t\t\"service\": serviceTypes.WMS,\n\t\t\t\"federation\": capabilities.federation\n\t\t},\n\t\t\"ogcapi-features\": {\n\t\t\t\"id\": \"OGCAPI-FEATURES\",\n\t\t\t\"service\": serviceTypes['OGCAPI-FEATURES']\n\t\t}\n\t},\n\t\"service-types\": {\n\t\t\"api\": {\n\t\t\t\"services\": serviceTypes,\n\t\t\t\"federation\": capabilities.federation\n\t\t}\n\t},\n\t\"supported-features\": {\n\t\t\"api\": {\n\t\t\t\"endpoints\": capabilities.endpoints\n\t\t}\n\t},\n\t// ToDo: Tab, Tabs - see UdfRuntime component for an example\n\t\"udf-runtime\": {\n\t\t\"api\": {\n\t\t\t\"id\": \"R\",\n\t\t\t\"runtime\": runtimes[\"R\"],\n\t\t\t\"federation\": capabilities.federation\n\t\t}\n\t},\n\t\"udf-runtimes\": {\n\t\t\"api\": {\n\t\t\t\"runtimes\": runtimes,\n\t\t\t\"federation\": capabilities.federation\n\t\t}\n\t}\n}","var FeatureList = {\n\n\t// Manual assignment of the endpoints above to individual features.\n\t// A functionality is considered supported when ALL of the corresponding endpoints are supported.\n\tfeatures: {\n\t\t'Basic functionality': {\n\t\t\t'get /collections': 'Data discovery',\n\t\t\t'get /collections/{collection_id}': 'Data discovery details',\n\t\t\t'get /processes': 'Process discovery',\n\t\t\t'get /file_formats': 'File format discovery',\n\t//\t\t'get /conformance': 'OGC conformance',\t\t\t\t// Only needed for OGC compliance\n\t//\t\t'get /credentials/basic': 'Basic authentication',\t// Should only be used for dev purposes\n\t\t\t'get /credentials/oidc': 'OpenID Connect authentication',\n\t\t\t'get /me': 'User profiles'\n\t\t},\n\t\t'Synchronous processing': {\n\t\t\t'post /result': 'Synchronous processing'\n\t\t},\n\t\t'Batch processing': {\n\t\t\t'get /jobs': 'Overview',\n\t\t\t'post /jobs': 'Creating',\n\t\t\t'get /jobs/{job_id}': 'Status',\n\t//\t\t'patch /jobs/{job_id}': 'Updating',\t\t\t\t\t// not necessarily needed (can be achieved by deleting and re-creating)\n\t\t\t'delete /jobs/{job_id}': 'Deleting',\n\t\t\t'get /jobs/{job_id}/logs': 'Logs',\n\t\t\t'get /jobs/{job_id}/results': 'Downloading results',\n\t\t\t'post /jobs/{job_id}/results': 'Queuing',\n\t// \t\t'delete /jobs/{job_id}/results': 'Deleting results'\t// not necessarily needed (can be deleted by deleting the entire job)\n\t\t},\n\t\t'Estimate processing costs': {\n\t\t\t'get /jobs/{job_id}/estimate': 'Estimate processing costs'\n\t\t},\n\t\t'Additional web services': {\n\t\t\t'get /service_types': 'Discovery',\n\t\t\t'get /services': 'Overview',\n\t\t\t'post /services': 'Creating',\n\t\t\t'get /services/{service_id}': 'Status',\n\t//\t\t'patch /services/{service_id}': 'Updating',\t\t\t// not necessarily needed (can be achieved by deleting and re-creating)\n\t\t\t'delete /services/{service_id}': 'Deleting',\n\t\t\t'get /services/{service_id}/logs': 'Logs'\n\t\t},\n\t\t'File storage': {\n\t\t\t'get /files': 'Overview',\n\t\t\t'get /files/{path}': 'Downloading',\n\t\t\t'put /files/{path}': 'Uploading',\n\t\t\t'delete /files/{path}': 'Deleting'\n\t\t},\n\t\t'User-defined processes': {\n//\t\t\t'post /validation': 'Validating',\t\t\t\t\t// not necessarily needed (can be achieved by sending a process to one of the other endpoints)\n\t\t\t'get /process_graphs': 'Overview',\n\t\t\t'get /process_graphs/{process_graph_id}': 'Status',\n\t \t\t'put /process_graphs/{process_graph_id}': 'Creating',\n\t\t\t'delete /process_graphs/{process_graph_id}': 'Deleting'\n\t\t},\n\t\t'User-defined functions (UDF)': {\n\t\t\t'get /udf_runtimes': 'User-defined functions (UDF)'\n\t\t}\n\t}\n\n};\n\nmodule.exports = FeatureList;","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('p',{staticClass:\"vue-component component-error-notice\"},[_vm._v(\" Sorry, could not load user interface from server. Please try again. \")])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\t\n\t\tSorry, could not load user interface from server. Please try again.\n\t
\n \n\n","import mod from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Errored.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[1]!../../node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./Errored.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Errored.vue?vue&type=template&id=d2682b08\"\nimport script from \"./Errored.vue?vue&type=script&lang=js\"\nexport * from \"./Errored.vue?vue&type=script&lang=js\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","import { Utils as CommonUtils, ProcessSchema } from '@openeo/js-commons';\nimport Loading from './components/internal/Loading.vue';\nimport Errored from './components/internal/Errored.vue';\n\nconst SEARCH_SPLITCHARS = /[\\s\\.,;:\"'!&\\(\\{\\[\\)\\}\\]]+/g;\nconst SEARCH_SERIALIZER = (_, value) => {\n\tif (value === null) {\n\t\treturn \"\";\n\t}\n\telse if (typeof value === 'string') {\n\t\treturn value.replace(/\\s+/g, ' ');\n\t}\n\treturn value;\n};\n\nclass Utils extends CommonUtils {\n\n\tstatic kebabToCamelCase(str) {\n\t\treturn str.replace(/-(\\w)/g, (_, c) => c ? c.toUpperCase() : '');\n\t}\n\n\tstatic enableHtmlProps(vm) {\n\t\t// Don't execute if not in web-component mode (i.e. check for the shadow root)\n\t\tif (!Utils.isObject(vm.$root) || !vm.$root.$options.shadowRoot) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Read the HTML props once the page is completely loaded and all props are completely available\n\t\tif(document.readyState === 'complete') {\n\t\t\tUtils.readHtmlProps(vm);\n\t\t}\n\t\telse {\n\t\t\tdocument.addEventListener('readystatechange', () => Utils.enableHtmlProps(vm), {once: true});\n\t\t}\n\t}\n\n\tstatic readHtmlProps(vm) {\n\t\tif (!Utils.isObject(vm) || !Utils.isObject(vm.$slots) || !Array.isArray(vm.$slots.default)) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Read script tags\n\t\tlet slots = vm.$slots.default.filter(slot => typeof slot.tag === 'string' && slot.tag.toUpperCase() === 'SCRIPT' && typeof slot.data.attrs.type === 'string' && slot.data.attrs.type.includes('application/json'));\n\t\t// We are using `includes` here as for some strange reasons in Jupyter Notebooks subsequent re-renders of the cell\n\t\t// result in the attribute value being prefixed by a \"true/\", i.e.the value in slot.data.attrs.type is \"true/application/json\".\n\t\tfor(let slot of slots) {\n\t\t\tlet prop = null;\n\t\t\ttry {\n\t\t\t\tif (typeof slot.data.attrs.prop === 'string' && slot.data.attrs.prop.length > 0) {\n\t\t\t\t\tprop = Utils.kebabToCamelCase(slot.data.attrs.prop);\n\t\t\t\t}\n\t\t\t\tlet value = JSON.parse(slot.data.domProps.innerHTML);\n\t\t\t\tif (prop) {\n\t\t\t\t\tUtils.setProp(vm, prop, value); // Set a single prop\n\t\t\t\t}\n\t\t\t\telse if (Utils.isObject(value)) {\n\t\t\t\t\tfor(let key in value) { // Set all props\n\t\t\t\t\t\tUtils.setProp(vm, key, value[key]);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tconsole.error(`Props passed via script tag must be contained in an object.`);\n\t\t\t\t}\n\t\t\t}\n\t\t\tcatch (error) {\n\t\t\t\tif (prop) {\n\t\t\t\t\tconsole.error(`Prop '${prop}' passed via script tag is invalid: ${error.message}`);\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tconsole.error(`Props passed via script tag are invalid: ${error.message}`);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tstatic loadFontAwesome(vm) {\n\t\tlet webComponent = Utils.isObject(vm.$root) && vm.$root.$options.shadowRoot;\n\n\t\tlet stylesheets = Array.from((webComponent || document).styleSheets);\n\t\tfor(let css of stylesheets) {\n\t\t\tif (typeof css.href === 'string' && css.href.includes('fontawesome')) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t}\n\n\t\t// Don't execute if not in web-component mode (browsers don't support loading fonts in shadowroot)\n\t\tif (!webComponent) {\n\t\t\tlet font = document.createElement('link');\n\t\t\tfont.as = \"font\";\n\t\t\tfont.type = \"font/woff2\";\n\t\t\tfont.crossOrigin = true;\n\t\t\tfont.href = \"https://use.fontawesome.com/releases/v5.13.0/webfonts/fa-solid-900.woff2\";\n\t\t\tdocument.head.appendChild(font);\n\t\t}\n\n\t\tlet css = document.createElement('link');\n\t\tcss.rel = \"stylesheet\";\n\t\tcss.type = \"text/css\";\n\t\tcss.media = \"all\";\n\t\tcss.href = \"https://use.fontawesome.com/releases/v5.13.0/css/all.css\";\n\t\t(webComponent || document.head).appendChild(css);\n\t}\n\n\tstatic setProp(vm, prop, value) {\n\t\t// Depending on when during the page load this is executed, we\n\t\t// need either to populate propsData (initially available) or\n\t\t// $props (available after propsData has been read).\n\t\tlet propsRef = Utils.isObject(vm.$props) ? vm.$props : vm.$options.propsData;\n\t\tvm.$set(propsRef, Utils.kebabToCamelCase(prop), value);\n\t}\n\n\tstatic loadAsyncComponent(importer) {\n\t\treturn {\n\t\t\tcomponent: importer,\n\t\t\tloading: Loading,\n\t\t\terror: Errored,\n\t\t\tdelay: 0,\n\t\t\ttimeout: 10000\n\t\t};\n\t}\n\n\tstatic dataType(schema, signature = false, similarAllowed = 2, level = 0) {\n\t\tlet schemaObj = new ProcessSchema(schema);\n\t\tvar types = new Set();\n\t\tfor(let i in schemaObj.schemas) {\n\t\t\tlet dt = schemaObj.schemas[i];\n\t\t\tlet native = dt.nativeDataType();\n\t\t\tlet type = dt.dataType();\n\t\t\t// Make the data types that we can submit via JSON (e.g. raster-cube, labeled-array) native\n\t\t\tif (!dt.isEditable()) {\n\t\t\t\tnative = type;\n\t\t\t}\n\t\t\telse if (signature && schemaObj.schemas.filter(other => other.nativeDataType() === native).length > similarAllowed) {\n\t\t\t\t// For signatures only: Check whether another similar type is available, then show only native type\n\t\t\t\ttypes.add(native);\n\t\t\t\tcontinue;\n\t\t\t}\n\t\t\tlet formatted = native === type ? type : `${type}:${native}`;\n\t\t\tif (native === 'array' && Utils.isObject(dt.schema.items)) {\n\t\t\t\tlet arrayItems = Utils.dataType(dt.schema.items, signature, similarAllowed, level + 1);\n\t\t\t\tif (arrayItems !== 'any') {\n\t\t\t\t\tformatted += `<${arrayItems}>`;\n\t\t\t\t}\n\t\t\t}\n\t\t\ttypes.add(formatted);\n\t\t}\n\t\tif (types.has('any')) {\n\t\t\treturn 'any';\n\t\t}\n\t\treturn Array.from(types).join(signature || level > 0 ? '|' : ', ');\n\t}\n\n\tstatic htmlentities_decode(str) {\n\t\tif (typeof str !== 'string') {\n\t\t\tstr = String(str);\n\t\t}\n\t\treturn str.replace(/</g, \"<\").replace(/>/g, \">\").replace(/"/g, '\"').replace(/'/g, \"'\");\n\t}\n\n\tstatic htmlentities(str) {\n\t\tif (typeof str !== 'string') {\n\t\t\tstr = String(str);\n\t\t}\n\t\treturn str.replace(//g, \">\").replace(/\"/g, \""\").replace(/'/g, ''');\n\t}\n\t\n\tstatic countObjectKeys(data) {\n\t\tvar count = {};\n\t\tfor(var i in data) {\n\t\t\tvar obj = data[i];\n\t\t\tif (typeof obj !== 'object' || obj === null || Array.isArray(obj)) {\n\t\t\t\treturn null;\n\t\t\t}\n\t\t\tfor(var key in obj) {\n\t\t\t\tif (typeof count[key] === 'undefined') {\n\t\t\t\t\tcount[key] = 1;\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tcount[key]++;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn count;\n\t}\n\n\tstatic isTableLike(data, force = false) {\n\t\tif (typeof data !== 'object' || data === null) {\n\t\t\treturn [];\n\t\t}\n\n\t\tvar countedKeys = Utils.countObjectKeys(data);\n\t\tif (countedKeys !== null) {\n\t\t\tif (force === true) {\n\t\t\t\treturn Object.keys(countedKeys);\n\t\t\t}\n\t\t\tvar num = 0;\n\t\t\tvar sum = 0;\n\t\t\tfor (var i in countedKeys) {\n\t\t\t\tnum++;\n\t\t\t\tsum += countedKeys[i];\n\t\t\t}\n\n\t\t\tvar avg = sum / num;\n\t\t\tvar dataSize = Array.isArray(dataSize) ? data.length : Object.keys(data).length;\n\t\t\tif (avg > dataSize/2) {\n\t\t\t\treturn Object.keys(countedKeys);\n\t\t\t}\n\t\t}\n\n\t\treturn [];\n\t}\n\n\tstatic isUrl(value, onlyHttp = true) {\n\t\tif (!Utils.hasText(value)) {\n\t\t\treturn false;\n\t\t}\n\t\ttry {\n\t\t\tlet url = new URL(value);\n\t\t\tif (onlyHttp && !url.protocol.match(/^https?:$/i)) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t\treturn true;\n\t\t} catch (error) {\n\t\t\treturn false;\n\t\t}\n\t}\n\n\tstatic prettifyAbbreviation(str) {\n\t\tif (typeof str === 'string' && str.match(/[A-Z]+/) === null) {\n\t\t\treturn str.toUpperCase();\n\t\t}\n\t\treturn str;\n\t}\n\t\n\tstatic ensurePoint(pt, fallback = null) {\n\t\tif (typeof fallback !== 'function') {\n\t\t\tfallback = () => [0,0];\n\t\t}\n\t\tif (!Array.isArray(pt)) {\n\t\t\treturn fallback();\n\t\t}\n\t\tif (typeof pt[0] !== 'number') {\n\t\t\tpt[0] = fallback()[0] || 0;\n\t\t}\n\t\tif (typeof pt[1] !== 'number') {\n\t\t\tpt[1] = fallback()[1] || 0;\n\t\t}\n\t\treturn pt;\n\t}\n\t\n\tstatic formatRef(value) {\n\t\tif (this.isRef(value)) {\n\t\t\tif (value.from_node) {\n\t\t\t\treturn \"#\" + value.from_node;\n\t\t\t}\n\t\t\telse if (value.from_parameter) {\n\t\t\t\treturn \"$\" + value.from_parameter;\n\t\t\t}\n\t\t}\n\t\treturn value;\n\t}\n\n\tstatic isRef(obj) {\n\t\treturn (Utils.isObject(obj) && (obj.from_parameter || obj.from_node));\n\t}\n\n\tstatic isRefEqual(ref1, ref2) {\n\t\tif (!Utils.isRef(ref1) || !Utils.isRef(ref2)) {\n\t\t\treturn false;\n\t\t}\n\t\telse if (ref1.from_parameter && ref1.from_parameter === ref2.from_parameter) {\n\t\t\treturn true;\n\t\t}\n\t\telse if (ref1.from_node && ref1.from_node === ref2.from_node) {\n\t\t\treturn true;\n\t\t}\n\t\treturn false;\n\t}\n\n\t// A very rough GeoJSON detection, if no GeoJSON schema is available.\n\tstatic detectGeoJson(data) {\n\t\tif (!Utils.isObject(data)) {\n\t\t\treturn false;\n\t\t}\n\t\telse if (typeof data.type !== 'string') {\n\t\t\treturn false;\n\t\t}\n\n\t\tswitch(data.type) {\n\t\t\tcase \"Point\":\n\t\t\tcase \"MultiPoint\":\n\t\t\tcase \"LineString\":\n\t\t\tcase \"MultiLineString\":\n\t\t\tcase \"Polygon\":\n\t\t\tcase \"MultiPolygon\":\n\t\t\t\tif (!Array.isArray(data.coordinates)) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\treturn true;\n\t\t\tcase \"GeometryCollection\":\n\t\t\t\tif (!Array.isArray(data.geometries)) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\treturn true;\n\t\t\tcase \"Feature\":\n\t\t\t\tif (data.geometry !== null && !Utils.isObject(data.geometry)) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\tif (data.properties !== null && !Utils.isObject(data.properties)) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\treturn true;\n\t\t\tcase \"FeatureCollection\":\n\t\t\t\tif (!Array.isArray(data.features)) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\treturn true;\n\t\t\tdefault:\n\t\t\t\treturn false;\n\t\t}\n\t}\n\t\n\tstatic toProcessParameters(parameters) {\n\t\tif (Utils.isObject(parameters)) {\n\t\t\tlet processParameters = [];\n\t\t\tfor(let name in parameters) {\n\t\t\t\tlet param = parameters[name];\n\t\t\t\tlet schema = Utils.omitFromObject(param, ['description', 'required', 'default', 'federation:backends']);\n\t\t\t\tprocessParameters.push({\n\t\t\t\t\tname,\n\t\t\t\t\tdescription: param.description,\n\t\t\t\t\toptional: !param.required,\n\t\t\t\t\tdefault: param.default,\n\t\t\t\t\t'federation:backends': param['federation:backends'],\n\t\t\t\t\tschema\n\t\t\t\t});\n\t\t\t}\n\t\t\treturn processParameters.sort((a,b) => Utils.compareStringCaseInsensitive(a.name, b.name));\n\t\t}\n\t\telse {\n\t\t\treturn [];\n\t\t}\n\t}\n\t\n\tstatic formatCurrency(amount, currency, fallback = '') {\n\t\tif (typeof amount !== 'number') {\n\t\t\treturn fallback;\n\t\t}\n\n\t\ttry {\n\t\t\treturn amount.toLocaleString(undefined, { style: 'currency', currency: currency });\n\t\t} catch(error) {\n\t\t\tlet str = amount.toLocaleString(undefined, {maximumFractionDigits: 2});\n\t\t\tif (typeof currency === 'string') {\n\t\t\t\tstr += ' ';\n\t\t\t\tstr += currency;\n\t\t\t}\n\t\t\treturn str.trim();\n\t\t}\n\t}\n\n\tstatic formatBudget(budget, currency, unlimited = \"Unlimited\") {\n\t\tif (budget === null) {\n\t\t\treturn unlimited;\n\t\t}\n\t\telse {\n\t\t\treturn Utils.formatCurrency(budget, currency);\n\t\t}\n\t}\n\n\tstatic formatTimestamp(value, fallback = 'n/a') {\n\t\tif (typeof value === 'string') {\n\t\t\ttry {\n\t\t\t\treturn new Date(value).toLocaleString(undefined, {\n\t\t\t\t\ttimeZone: \"UTC\",\n\t\t\t\t\ttimeZoneName: \"short\"\n\t\t\t\t});\n\t\t\t} catch (error) {}\n\t\t}\n\t\treturn fallback;\n\t}\n\n\tstatic formatFileSize(value, fallback = 'n/a') {\n\t\tlet units = ['B', 'kB', 'MB', 'GB', 'TB', 'PB'];\n\t\tif (typeof value !== 'number') {\n\t\t\treturn fallback;\n\t\t}\n\t\tlet i = value == 0 ? 0 : Math.floor( Math.log(value) / Math.log(1024) );\n\t\tlet size = value / Math.pow(1024, i);\n\t\tlet sizeStr = size.toLocaleString(undefined, {maximumFractionDigits: 1});\n\t\treturn `${sizeStr} ${units[i]}`;\n\t}\n\n\tstatic formatProcessSignature(process, html = true) {\n\t\tlet params = [];\n\t\tif (Array.isArray(process.parameters)) {\n\t\t\tfor(let i in process.parameters) {\n\t\t\t\tlet p = process.parameters[i];\n\t\t\t\tlet pType = Utils.dataType(p.schema, true, 1);\n\t\t\t\tlet req = p.optional ? '?' : '';\n\t\t\t\tlet pDefault = '';\n\t\t\t\tif (p.optional && typeof p.default !== 'undefined') {\n\t\t\t\t\tpDefault = JSON.stringify(p.default);\n\t\t\t\t}\n\t\t\t\tlet pStr;\n\t\t\t\tif (html) {\n\t\t\t\t\tpStr = `${req} ${ Utils.htmlentities(pType) } ${ Utils.htmlentities(p.name) } `;\n\t\t\t\t\tif (pDefault) {\n\t\t\t\t\t\tif (pDefault.length > 30) {\n\t\t\t\t\t\t\tpDefault = `… `;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tpStr += ` = ${pDefault} `;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tpStr = req + pType + \" \" + p.name + pDefault;\n\t\t\t\t}\n\t\t\t\tparams.push(pStr);\n\t\t\t}\n\t\t}\n\t\tlet paramStr = \"(\" + params.join(\", \") + \") : \";\n\n\t\tlet returnSchema = Utils.isObject(process.returns) && process.returns.schema && typeof process.returns.schema === 'object' ? process.returns.schema : {};\n\t\tlet returns = Utils.dataType(returnSchema, true, 2);\n\n\t\tif (html) {\n\t\t\treturn `${ Utils.htmlentities(process.process.id) } ${paramStr}${ Utils.htmlentities(returns) } `;\n\t\t}\n\t\telse {\n\t\t\treturn process.process.id + paramStr + returns;\n\t\t}\n\t}\n\n\tstatic search(searchterm, target, and = true) {\n\t\tif (typeof searchterm !== 'string' || searchterm.length === 0) {\n\t\t\treturn false;\n\t\t}\n\t\tif (Utils.isObject(target)) {\n\t\t\ttarget = Object.values(target);\n\t\t}\n\t\telse if (typeof target === 'string') {\n\t\t\ttarget = [target];\n\t\t}\n\n\t\tif (!Array.isArray(target)) {\n\t\t\treturn false;\n\t\t}\n\n\t\t// Prepare search terms\n\t\tsearchterm = searchterm.toLowerCase().split(SEARCH_SPLITCHARS);\n\n\t\t// Prepare text to search in\n\t\ttarget = target\n\t\t\t.map(v => {\n\t\t\t\tif (typeof v === 'string') {\n\t\t\t\t\treturn v;\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\t// Convert non-strings to a string (JSON-related chars \"': are added to SEARCH_SPLITCHARS)\n\t\t\t\t\treturn JSON.stringify(v, SEARCH_SERIALIZER, 0);\n\t\t\t\t}\n\t\t\t})\n\t\t\t.join(' ') // Merge into a single string\n\t\t\t.replace(SEARCH_SPLITCHARS, ' ') // replace split chars with white spaces\n\t\t\t.toLowerCase(); // Lowercase\n\n\t\t// Search with \"and\" or \"or\"\n\t\tlet fn = and ? 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