CT_mapping_steps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "960d3adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import ast\n",
    "import math\n",
    "import pandas as pd\n",
    "import geopandas as gpd \n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "from datetime import datetime, timedelta\n",
    "from shapely.geometry import Polygon\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "\n",
    "import ee\n",
    "import geemap.foliumap\n",
    "import eeconvert\n",
    "\n",
    "ee.Initialize()\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "sns.set()\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be9f23d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif, SelectKBest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, matthews_corrcoef\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8eeabf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gee_tools.datasources.sentinel2 as opt_s2\n",
    "from gee_tools.imgtools import getScaledImage\n",
    "from gee_tools.harmonics import run_std_regressions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528b599e",
   "metadata": {},
   "source": [
    "### Step 0: Survey data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8ffc7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dummy data for the purpose of demonstration\n",
    "survey_df = pd.read_csv('data/0_survey_data/raw_survey.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "564059db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hh_id</th>\n",
       "      <th>plotid</th>\n",
       "      <th>crop</th>\n",
       "      <th>year</th>\n",
       "      <th>season</th>\n",
       "      <th>irrigation</th>\n",
       "      <th>fertilizer</th>\n",
       "      <th>planting_date</th>\n",
       "      <th>geom_poly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hh_01</td>\n",
       "      <td>pl_1</td>\n",
       "      <td>maize</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{\"type\":\"Polygon\",\"coordinates\":[[[33.17239286...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hh_02</td>\n",
       "      <td>pl_2</td>\n",
       "      <td>maize_hybrid</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{\"type\":\"Polygon\",\"coordinates\":[[[33.15676095...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hh_03</td>\n",
       "      <td>pl_3</td>\n",
       "      <td>rice</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>yes</td>\n",
       "      <td>50.0</td>\n",
       "      <td>21-May</td>\n",
       "      <td>{\"type\":\"Polygon\",\"coordinates\":[[[33.16654564...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hh_04</td>\n",
       "      <td>pl_4</td>\n",
       "      <td>sorghum</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{\"type\":\"Polygon\",\"coordinates\":[[[33.16905619...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hh_05</td>\n",
       "      <td>Pl_5</td>\n",
       "      <td>beans</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>April</td>\n",
       "      <td>{\"type\":\"Polygon\",\"coordinates\":[[[33.14849974...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hh_id plotid          crop  year season irrigation  fertilizer  \\\n",
       "0  hh_01   pl_1         maize  2019  rainy         no         NaN   \n",
       "1  hh_02   pl_2  maize_hybrid  2019  rainy        NaN        60.0   \n",
       "2  hh_03   pl_3          rice  2019  rainy        yes        50.0   \n",
       "3  hh_04   pl_4       sorghum  2019  rainy        NaN         NaN   \n",
       "4  hh_05   Pl_5         beans  2019  rainy        NaN        30.0   \n",
       "\n",
       "  planting_date                                          geom_poly  \n",
       "0           NaN  {\"type\":\"Polygon\",\"coordinates\":[[[33.17239286...  \n",
       "1           NaN  {\"type\":\"Polygon\",\"coordinates\":[[[33.15676095...  \n",
       "2        21-May  {\"type\":\"Polygon\",\"coordinates\":[[[33.16654564...  \n",
       "3           NaN  {\"type\":\"Polygon\",\"coordinates\":[[[33.16905619...  \n",
       "4         April  {\"type\":\"Polygon\",\"coordinates\":[[[33.14849974...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e125306a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"type\":\"Polygon\",\"coordinates\":[[[33.17239286542065,-13.90195782152107],[33.17355157971508,-13.902166112825665],[33.17391636014111,-13.89804191011496],[33.17262889981396,-13.897958592131438],[33.17239286542065,-13.90195782152107]]]}'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df['geom_poly'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0508cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hh_id</th>\n",
       "      <th>plotid</th>\n",
       "      <th>crop</th>\n",
       "      <th>year</th>\n",
       "      <th>season</th>\n",
       "      <th>irrigation</th>\n",
       "      <th>fertilizer</th>\n",
       "      <th>planting_date</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hh_01</td>\n",
       "      <td>pl_1</td>\n",
       "      <td>maize</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((33.17239286542065 -13.90195782152107...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hh_02</td>\n",
       "      <td>pl_2</td>\n",
       "      <td>maize_hybrid</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((33.15676095128185 -13.89990093482503...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hh_03</td>\n",
       "      <td>pl_3</td>\n",
       "      <td>rice</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>yes</td>\n",
       "      <td>50.0</td>\n",
       "      <td>21-May</td>\n",
       "      <td>POLYGON ((33.16654564976818 -13.90262956030594...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hh_04</td>\n",
       "      <td>pl_4</td>\n",
       "      <td>sorghum</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((33.16905619740612 -13.90460831438962...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hh_05</td>\n",
       "      <td>Pl_5</td>\n",
       "      <td>beans</td>\n",
       "      <td>2019</td>\n",
       "      <td>rainy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>April</td>\n",
       "      <td>POLYGON ((33.14849974751599 -13.89821375336131...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hh_id plotid          crop  year season irrigation  fertilizer  \\\n",
       "0  hh_01   pl_1         maize  2019  rainy         no         NaN   \n",
       "1  hh_02   pl_2  maize_hybrid  2019  rainy        NaN        60.0   \n",
       "2  hh_03   pl_3          rice  2019  rainy        yes        50.0   \n",
       "3  hh_04   pl_4       sorghum  2019  rainy        NaN         NaN   \n",
       "4  hh_05   Pl_5         beans  2019  rainy        NaN        30.0   \n",
       "\n",
       "  planting_date                                           geometry  \n",
       "0           NaN  POLYGON ((33.17239286542065 -13.90195782152107...  \n",
       "1           NaN  POLYGON ((33.15676095128185 -13.89990093482503...  \n",
       "2        21-May  POLYGON ((33.16654564976818 -13.90262956030594...  \n",
       "3           NaN  POLYGON ((33.16905619740612 -13.90460831438962...  \n",
       "4         April  POLYGON ((33.14849974751599 -13.89821375336131...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean geometry column \n",
    "survey_df['geom_poly'] = survey_df.apply(lambda row: ast.literal_eval(row['geom_poly']), axis=1)\n",
    "survey_df['geom_poly'] = survey_df.apply(lambda row: Polygon(row['geom_poly']['coordinates'][0]), axis=1)\n",
    "survey_df = survey_df.rename(columns={'geom_poly': 'geometry'})\n",
    "survey_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4d377c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convet to geodataframe\n",
    "survey_gdf = gpd.GeoDataFrame(survey_df, geometry='geometry')\n",
    "survey_gdf['geom_id'] = survey_gdf['hh_id'] + '_' + survey_gdf['plotid']\n",
    "\n",
    "# clean crop names\n",
    "survey_gdf['croptype_str'] = 'other'\n",
    "survey_gdf.loc[survey_gdf['crop'].str.contains('maize'), 'croptype_str'] = 'maize'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e8e0f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select desired columns and write file\n",
    "survey_gdf = survey_gdf[['geom_id', 'year', 'croptype_str', 'geometry']]\n",
    "survey_gdf.to_file(\"data/0_survey_data/survey_clean.gpkg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e522513",
   "metadata": {},
   "source": [
    "### Step 1: Generating features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3afebb",
   "metadata": {},
   "source": [
    "##### We use harmonic regressions to characterize crop phenology"
   ]
  },
  {
   "attachments": {
    "harmonic_ts.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b1277899",
   "metadata": {},
   "source": [
    "![harmonic_ts.png](attachment:harmonic_ts.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "173524d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting a small geometry for demonstration purposes\n",
    "roi = ee.Geometry.Polygon([[33.13382026557807,-13.923324245171836],\n",
    "                           [33.17873602978405,-13.923324245171836],\n",
    "                           [33.17873602978405,-13.87972406080146],\n",
    "                           [33.13382026557807,-13.87972406080146], \n",
    "                           [33.13382026557807,-13.923324245171836]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d61eb55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to feature collection\n",
    "survey_fc = eeconvert.gdfToFc(survey_gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6f51ae3b",
   "metadata": {},
   "outputs": [
    {
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       "        \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<geemap.foliumap.Map at 0x7fd42e45b050>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize ROI\n",
    "Map = geemap.foliumap.Map()\n",
    "\n",
    "Map.add_basemap('SATELLITE')\n",
    "Map.addLayer(roi, {}, 'roi')\n",
    "Map.addLayer(survey_fc, {'color':'F1216D'}, 'fields')\n",
    "Map.centerObject(roi)\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ed09f9",
   "metadata": {},
   "source": [
    "#### 1.1 Obtaining desired time series satellite data from GEE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0b10e85b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we will make use of some of our orgaizations code which is publicly available, \n",
    "# i.e. the gee-tools module installed previously in our virtual environment\n",
    "\n",
    "# start and end dates have been chosen to center the year around the middle of season (information from FAO)\n",
    "# season of interest is 2018/19\n",
    "start_date = ee.Date('2018-08-28')\n",
    "end_date = ee.Date('2019-08-27')\n",
    "\n",
    "s2coll = opt_s2.Sentinel2TOA(filterpoly=roi, start_date=start_date, end_date=end_date, \n",
    "                            addVIs=True, addCloudMasks=True).get_img_coll()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d8e98e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to mask clouds using decision tree classifier band\n",
    "def maskClouds_toa(img):\n",
    "    fse = img.select(['QA_FSEV1'])\n",
    "    clear = fse.updateMask(fse.eq(1))\n",
    "    return img.updateMask(clear)\n",
    "\n",
    "# Define the bandames in the S2-2A data which we want to keep\n",
    "bandnames = ['AEROS','BLUE','GREEN','RED','RDED1','RDED2','RDED3','NIR','RDED4',\n",
    "             'VAPOR','SWIR1','SWIR2','NBR1','NDTI','GCVI','NDVI','SNDVI']\n",
    "# Mask clouds using the function above\n",
    "s2coll = s2coll.map(lambda img: maskClouds_toa(img))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82097f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2_proj = (\n",
    "    ee.Image(ee.ImageCollection(\"COPERNICUS/S2\").first())\n",
    "    .select(\"B3\")\n",
    "    .reproject(\"EPSG:3857\")\n",
    "    .projection()\n",
    ")\n",
    "s2coll = s2coll.map(lambda img: img.select(bandnames).setDefaultProjection(crs=s2_proj, scale=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9737f86f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AEROS\n",
      "BLUE\n",
      "GREEN\n",
      "RED\n",
      "RDED1\n",
      "RDED2\n",
      "RDED3\n",
      "NIR\n",
      "RDED4\n",
      "VAPOR\n",
      "SWIR1\n",
      "SWIR2\n",
      "NBR1\n",
      "NDTI\n",
      "GCVI\n",
      "NDVI\n",
      "SNDVI\n"
     ]
    }
   ],
   "source": [
    "# check bands\n",
    "for band in s2coll.first().getInfo()['bands']:\n",
    "    print(band['id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc984550",
   "metadata": {},
   "source": [
    "#### 1.2 Harmonic curve fitting over time series data - An example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c59e8b2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "omega = 1.2\n",
    "nharmonics = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6034336f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function of fourier series\n",
    "def fourier(t, *coeffs):\n",
    "    gcvi_fit = coeffs[0] + coeffs[1] * t\n",
    "    for n in range(nharmonics):\n",
    "        timerad = t * 2 * (n+1) * np.pi * omega\n",
    "        gcvi_fit += coeffs[2 + n*2] * np.cos(timerad) + coeffs[3 + n*2] * np.sin(timerad)\n",
    "    return gcvi_fit\n",
    "\n",
    "# adding a row for each day of the year\n",
    "def gen_dates():\n",
    "    start_date = '28/08/2018'\n",
    "    end_date = '27/08/2019'\n",
    "    rng = pd.date_range(start=start_date, end=end_date)\n",
    "    df = pd.DataFrame({ 'DATE': rng})\n",
    "    df['t'] = df['DATE'] - datetime.strptime(start_date, '%d/%m/%Y')\n",
    "    df['t'] = df[\"t\"] / (datetime.strptime(end_date, '%d/%m/%Y') - datetime.strptime(start_date, '%d/%m/%Y'))\n",
    "    return df\n",
    "\n",
    "def clean_ts(df):\n",
    "    df['DATE'] = pd.to_datetime(pd.to_datetime(df['DATE'], format='%Y-%m-%dT%H:%M:%S').dt.strftime('%Y-%m-%d'))\n",
    "    df = df.drop_duplicates(['DATE'])\n",
    "    master_df = gen_dates()\n",
    "    df = master_df.merge(df, on='DATE', how='outer')\n",
    "    df['t'] = df['t_y']\n",
    "    df.loc[df['t'].isnull(),'t'] = df.loc[df['t'].isnull(), 't_x'] \n",
    "    df = df.sort_values(by=['DATE'])\n",
    "    return df[['DATE', 'GCVI', 't']]\n",
    "\n",
    "def fit_ts(df):\n",
    "    df = clean_ts(df)\n",
    "    popt, pcov = curve_fit(fourier, df[~df['GCVI'].isnull()]['t'],\n",
    "                           df[~df['GCVI'].isnull()]['GCVI'], [1, 1] + [1, 1]*nharmonics)\n",
    "    df['GCVI_HARMFIT'] = fourier(df['t'], *popt)\n",
    "    return df\n",
    "\n",
    "# example time series for 3 different crops\n",
    "maize_ts = pd.read_csv('data/1_satellite_features/maize_ts.csv')\n",
    "groundnut_ts = pd.read_csv('data/1_satellite_features/groundnut_ts.csv')\n",
    "soyabean_ts = pd.read_csv('data/1_satellite_features/soyabean_ts.csv')\n",
    "\n",
    "maize_ts = fit_ts(maize_ts)\n",
    "groundnut_ts = fit_ts(groundnut_ts)\n",
    "soyabean_ts = fit_ts(soyabean_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1fee3851",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/Users/shruti/anaconda3/envs/ct_map/lib/python3.7/site-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 936x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 296,
       "width": 1066
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the peaks look lower as we haven't applied any correction (top of atmosphere to surface reflectance) here\n",
    "\n",
    "dfs = [maize_ts, groundnut_ts, soyabean_ts]\n",
    "crops = ['Maize', 'Groundnut', 'Soyabean']\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette(\"bright\")\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(13,4), sharex=True, sharey=True, constrained_layout=True)\n",
    "fig.add_subplot(111, frameon=False)\n",
    "plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n",
    "plt.grid(b=None)\n",
    "plt.xlabel('Date', fontsize=15)\n",
    "plt.ylabel('GCVI', fontsize=15)\n",
    "\n",
    "axs = ax.flatten()\n",
    "\n",
    "for n in range(len(dfs)):\n",
    "    df = dfs[n]\n",
    "    p1,  = axs[n].plot_date(df['DATE'], df['GCVI'], c='b')\n",
    "    axs[n].set_xlabel(' ', fontsize=15)\n",
    "    axs[n].set_ylabel(' ', fontsize=15)\n",
    "    p2, = axs[n].plot(df['DATE'], df['GCVI_HARMFIT'], c='r')\n",
    "    axs[n].xaxis.set_major_formatter(mdates.DateFormatter('%b \\n%Y'))\n",
    "    ax[n].xaxis.set_major_locator(mdates.MonthLocator(interval=2))\n",
    "    axs[n].set_title('S2 GCVI - {}'.format(crops[n]), fontsize=16)\n",
    "    \n",
    "plt.figlegend((p1, p2), ('Observed', 'Fitted'), bbox_to_anchor=(1.01,0.96), loc='upper left', prop={\"size\":13})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c244fd2",
   "metadata": {},
   "source": [
    "#### 1.3 Harmonic exports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb3dfae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we need to repeat the above to obtain and export the harmonic coefficients \n",
    "# for all pixels in our ROI. GEEs parallel processing comes in handy here\n",
    "# We are computing harmonics over GCVI only, but the same process can be used for other bands/VIs of interest \n",
    "\n",
    "slhr = run_std_regressions(imagecoll=s2coll, bands=['GCVI'], refdate=start_date, independents=None, \n",
    "                           omega=1.2, nharmonics=2)\n",
    "\n",
    "# Convert to int32 to save some storage. scaling count differently as it may break Int32 limits.\n",
    "scaler = ee.Dictionary.fromLists(\n",
    "    slhr.bandNames().removeAll([f\"GCVI_count\"]),\n",
    "    ee.List.repeat(1e7, slhr.bandNames().length().subtract(1))\n",
    "    ).combine(ee.Dictionary({\n",
    "        f\"GCVI_count\": 1,\n",
    "    })\n",
    ") \n",
    "\n",
    "slhr = getScaledImage(slhr, scaler).toInt32().set(scaler)  \n",
    "\n",
    "slhr = slhr.set(\n",
    "    'omega', omega,\n",
    "    'bands', ['GCVI'],\n",
    "    'nharmonics', nharmonics,\n",
    "    'start_date', ee.Date(start_date).format(),\n",
    "    'end_date', ee.Date(end_date).format(),\n",
    "    'start_ms', ee.Date(start_date).millis(),\n",
    "    'end_ms', ee.Date(end_date).millis(),\n",
    "    'year', 2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fdc8a9ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export coefficients to GEE asset library \n",
    "task = ee.batch.Export.image.toAsset(\n",
    "    image=slhr.clip(roi), \n",
    "    description='exporting_gcvi_coeffs', \n",
    "    assetId='users/shruti/cega_geo4dev/gcvi_harmonic_coeffs', ### change this path if you want to export\n",
    "                                                              ### this to your GEE library \n",
    "                                                              ### (make sure the folder you're exporting to exists) \n",
    "    region=roi, \n",
    "    scale=10\n",
    ")\n",
    "\n",
    "task.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2caba603",
   "metadata": {},
   "source": [
    "#### 1.4 VIsualizing the coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf899f9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_phase_ampl(harmcoeffs, band):\n",
    "    cosname = band+'_cos1'\n",
    "    sinname = band+'_sin1'\n",
    "    constant = band+'_constant' \n",
    "    \n",
    "    # Compute phase and amplitude.\n",
    "    phase = harmcoeffs.select(cosname).atan2(\n",
    "        harmcoeffs.select(sinname))\n",
    "    amplitude = harmcoeffs.select(cosname).hypot(\n",
    "        harmcoeffs.select(sinname))\n",
    "    vertshift = harmcoeffs.select(constant)\n",
    "    \n",
    "    #Use the HSV to RGB transform to display phase and amplitude\n",
    "    rgb = phase.unitScale(0, math.pi*0.5).addBands(\n",
    "        amplitude.multiply(0.000001)).addBands(\n",
    "        vertshift.multiply(0.0000001)).hsvToRgb();\n",
    "    return rgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "18c9117d",
   "metadata": {},
   "outputs": [
    {
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       "        \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<geemap.foliumap.Map at 0x7fd44ca18bd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcvi_coeffs = ee.Image('users/shruti/cega_geo4dev/gcvi_harmonic_coeffs')\n",
    "\n",
    "rgb = get_phase_ampl(gcvi_coeffs, 'GCVI');\n",
    "Map = geemap.foliumap.Map()\n",
    "Map.setOptions('SATELLITE');\n",
    "Map.addLayer(rgb, {}, 'Phase amplitude - GCVI - 2019');\n",
    "\n",
    "Map.centerObject(gcvi_coeffs)\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1269f02",
   "metadata": {},
   "source": [
    "### Step 2: Generating composites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3bdef2fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a function to compute aggregates of satellite features over polygon geometries\n",
    "def reduce_img_reg(f, img, red, prop_names=None, geo=False):\n",
    "    props = img.reduceRegion(\n",
    "      reducer=red,\n",
    "      geometry=f.geometry(),\n",
    "      scale=10\n",
    "    )\n",
    "    if geo:\n",
    "        return ee.Feature(ee.Geometry(f.geometry()).centroid(), \n",
    "                          props).copyProperties(f, prop_names).copyProperties(img, None, img.bandNames())\n",
    "    return ee.Feature(None, props).copyProperties(f, prop_names).copyProperties(img, None, img.bandNames())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "92f7b1ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_means = ee.FeatureCollection(survey_fc.map(\n",
    "    lambda f: reduce_img_reg(f, gcvi_coeffs, ee.Reducer.mean(), \n",
    "                             ['geom_id', 'year', 'croptype_str', 'geometry'], True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4caac6f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export composites to a google drive folder \n",
    "task = ee.batch.Export.table.toDrive(\n",
    "    poly_means, 'gcvi_composites', '2_composites'\n",
    ")\n",
    "\n",
    "task.start()\n",
    "\n",
    "### You will need to move the exported composites to the desired folder location inside the github repo.\n",
    "### The above code will export the coefficients to your main drive folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f52fa0e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system:index</th>\n",
       "      <th>GCVI_constant</th>\n",
       "      <th>GCVI_cos1</th>\n",
       "      <th>GCVI_cos2</th>\n",
       "      <th>GCVI_count</th>\n",
       "      <th>GCVI_mean</th>\n",
       "      <th>GCVI_r2</th>\n",
       "      <th>GCVI_rmse</th>\n",
       "      <th>GCVI_sin1</th>\n",
       "      <th>GCVI_sin2</th>\n",
       "      <th>GCVI_t</th>\n",
       "      <th>GCVI_variance</th>\n",
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       "      <th>end_date</th>\n",
       "      <th>end_ms</th>\n",
       "      <th>geom_id</th>\n",
       "      <th>nharmonics</th>\n",
       "      <th>omega</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_ms</th>\n",
       "      <th>year</th>\n",
       "      <th>.geo</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>-8.371680e+06</td>\n",
       "      <td>1.854869e+06</td>\n",
       "      <td>76.257737</td>\n",
       "      <td>9.627959e+06</td>\n",
       "      <td>8.975811e+06</td>\n",
       "      <td>2.038523e+06</td>\n",
       "      <td>-5.599457e+06</td>\n",
       "      <td>2.025134e+06</td>\n",
       "      <td>-1.861404e+06</td>\n",
       "      <td>4.149967e+06</td>\n",
       "      <td>maize</td>\n",
       "      <td>2019-08-27T00:00:00</td>\n",
       "      <td>1.566864e+12</td>\n",
       "      <td>hh_01_pl_1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2018-08-28T00:00:00</td>\n",
       "      <td>1.535414e+12</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>{\"type\":\"Point\",\"coordinates\":[33.173128396466...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8.936620e+06</td>\n",
       "      <td>-2.455082e+06</td>\n",
       "      <td>5.011567e+05</td>\n",
       "      <td>64.840661</td>\n",
       "      <td>8.486153e+06</td>\n",
       "      <td>9.120451e+06</td>\n",
       "      <td>9.736973e+05</td>\n",
       "      <td>-4.208050e+06</td>\n",
       "      <td>1.265893e+06</td>\n",
       "      <td>1.721280e+06</td>\n",
       "      <td>1.189179e+06</td>\n",
       "      <td>maize</td>\n",
       "      <td>2019-08-27T00:00:00</td>\n",
       "      <td>1.566864e+12</td>\n",
       "      <td>hh_02_pl_2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2018-08-28T00:00:00</td>\n",
       "      <td>1.535414e+12</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>{\"type\":\"Point\",\"coordinates\":[33.157086105947...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.334086e+07</td>\n",
       "      <td>-8.924441e+06</td>\n",
       "      <td>2.708350e+06</td>\n",
       "      <td>73.839920</td>\n",
       "      <td>9.530495e+06</td>\n",
       "      <td>9.088763e+06</td>\n",
       "      <td>1.904451e+06</td>\n",
       "      <td>-5.338512e+06</td>\n",
       "      <td>2.837326e+06</td>\n",
       "      <td>5.865184e+05</td>\n",
       "      <td>3.926292e+06</td>\n",
       "      <td>other</td>\n",
       "      <td>2019-08-27T00:00:00</td>\n",
       "      <td>1.566864e+12</td>\n",
       "      <td>hh_03_pl_3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2018-08-28T00:00:00</td>\n",
       "      <td>1.535414e+12</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>{\"type\":\"Point\",\"coordinates\":[33.166562404524...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.353212e+07</td>\n",
       "      <td>-5.175330e+06</td>\n",
       "      <td>-4.396485e+05</td>\n",
       "      <td>77.134834</td>\n",
       "      <td>9.964452e+06</td>\n",
       "      <td>9.146189e+06</td>\n",
       "      <td>1.665679e+06</td>\n",
       "      <td>-6.451393e+06</td>\n",
       "      <td>2.748717e+06</td>\n",
       "      <td>-2.411802e+06</td>\n",
       "      <td>3.228105e+06</td>\n",
       "      <td>other</td>\n",
       "      <td>2019-08-27T00:00:00</td>\n",
       "      <td>1.566864e+12</td>\n",
       "      <td>hh_04_pl_4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2018-08-28T00:00:00</td>\n",
       "      <td>1.535414e+12</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>{\"type\":\"Point\",\"coordinates\":[33.168976534153...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.398733e+07</td>\n",
       "      <td>-2.556686e+06</td>\n",
       "      <td>-8.136107e+05</td>\n",
       "      <td>72.768000</td>\n",
       "      <td>1.394322e+07</td>\n",
       "      <td>8.862463e+06</td>\n",
       "      <td>1.880055e+06</td>\n",
       "      <td>-6.524679e+06</td>\n",
       "      <td>6.974574e+05</td>\n",
       "      <td>2.863906e+06</td>\n",
       "      <td>3.149390e+06</td>\n",
       "      <td>other</td>\n",
       "      <td>2019-08-27T00:00:00</td>\n",
       "      <td>1.566864e+12</td>\n",
       "      <td>hh_05_Pl_5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2018-08-28T00:00:00</td>\n",
       "      <td>1.535414e+12</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>{\"type\":\"Point\",\"coordinates\":[33.148531811388...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   system:index  GCVI_constant     GCVI_cos1     GCVI_cos2  GCVI_count  \\\n",
       "0             0   1.454038e+07 -8.371680e+06  1.854869e+06   76.257737   \n",
       "1             1   8.936620e+06 -2.455082e+06  5.011567e+05   64.840661   \n",
       "2             2   1.334086e+07 -8.924441e+06  2.708350e+06   73.839920   \n",
       "3             3   1.353212e+07 -5.175330e+06 -4.396485e+05   77.134834   \n",
       "4             4   1.398733e+07 -2.556686e+06 -8.136107e+05   72.768000   \n",
       "\n",
       "      GCVI_mean       GCVI_r2     GCVI_rmse     GCVI_sin1     GCVI_sin2  \\\n",
       "0  9.627959e+06  8.975811e+06  2.038523e+06 -5.599457e+06  2.025134e+06   \n",
       "1  8.486153e+06  9.120451e+06  9.736973e+05 -4.208050e+06  1.265893e+06   \n",
       "2  9.530495e+06  9.088763e+06  1.904451e+06 -5.338512e+06  2.837326e+06   \n",
       "3  9.964452e+06  9.146189e+06  1.665679e+06 -6.451393e+06  2.748717e+06   \n",
       "4  1.394322e+07  8.862463e+06  1.880055e+06 -6.524679e+06  6.974574e+05   \n",
       "\n",
       "         GCVI_t  GCVI_variance croptype_str             end_date  \\\n",
       "0 -1.861404e+06   4.149967e+06        maize  2019-08-27T00:00:00   \n",
       "1  1.721280e+06   1.189179e+06        maize  2019-08-27T00:00:00   \n",
       "2  5.865184e+05   3.926292e+06        other  2019-08-27T00:00:00   \n",
       "3 -2.411802e+06   3.228105e+06        other  2019-08-27T00:00:00   \n",
       "4  2.863906e+06   3.149390e+06        other  2019-08-27T00:00:00   \n",
       "\n",
       "         end_ms     geom_id  nharmonics  omega           start_date  \\\n",
       "0  1.566864e+12  hh_01_pl_1         2.0    1.2  2018-08-28T00:00:00   \n",
       "1  1.566864e+12  hh_02_pl_2         2.0    1.2  2018-08-28T00:00:00   \n",
       "2  1.566864e+12  hh_03_pl_3         2.0    1.2  2018-08-28T00:00:00   \n",
       "3  1.566864e+12  hh_04_pl_4         2.0    1.2  2018-08-28T00:00:00   \n",
       "4  1.566864e+12  hh_05_Pl_5         2.0    1.2  2018-08-28T00:00:00   \n",
       "\n",
       "       start_ms    year                                               .geo  \n",
       "0  1.535414e+12  2019.0  {\"type\":\"Point\",\"coordinates\":[33.173128396466...  \n",
       "1  1.535414e+12  2019.0  {\"type\":\"Point\",\"coordinates\":[33.157086105947...  \n",
       "2  1.535414e+12  2019.0  {\"type\":\"Point\",\"coordinates\":[33.166562404524...  \n",
       "3  1.535414e+12  2019.0  {\"type\":\"Point\",\"coordinates\":[33.168976534153...  \n",
       "4  1.535414e+12  2019.0  {\"type\":\"Point\",\"coordinates\":[33.148531811388...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the exported compoites\n",
    "gcvi_composites = pd.read_csv('data/2_composites/gcvi_composites.csv')\n",
    "gcvi_composites"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d4db71",
   "metadata": {},
   "source": [
    "### Step 3: Training an ML model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd829976",
   "metadata": {},
   "source": [
    "##### Metrics\n",
    "\n",
    "Accuracy measures the fraction of correct predictions to total predictions. An accuracy score of 1 represents perfect prediction, and 0 indicates completely wrong prediction. \n",
    "\n",
    "The Matthews correlation coefficient (MCC) evaluates performance on all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives). An MCC score of +1 represents a perfect prediction, 0 represents random prediction, and −1 an inverse prediction."
   ]
  },
  {
   "attachments": {
    "metrics.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "630cfcc2",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"attachment:metrics.png\" width=750/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eaf760ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read feature-label composites provided in data folder\n",
    "train_df = pd.read_csv('data/3_ml_model/training_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "738214bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOM_ID</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MAIZELAND</th>\n",
       "      <th>ASP</th>\n",
       "      <th>BLUE_constant</th>\n",
       "      <th>BLUE_cos1</th>\n",
       "      <th>BLUE_cos2</th>\n",
       "      <th>BLUE_mean</th>\n",
       "      <th>BLUE_r2</th>\n",
       "      <th>BLUE_rmse</th>\n",
       "      <th>BLUE_sin1</th>\n",
       "      <th>BLUE_sin2</th>\n",
       "      <th>BLUE_t</th>\n",
       "      <th>BLUE_variance</th>\n",
       "      <th>COUNT_s2</th>\n",
       "      <th>ELEV</th>\n",
       "      <th>GCVI_constant</th>\n",
       "      <th>GCVI_cos1</th>\n",
       "      <th>GCVI_cos2</th>\n",
       "      <th>GCVI_mean</th>\n",
       "      <th>GCVI_r2</th>\n",
       "      <th>GCVI_rmse</th>\n",
       "      <th>GCVI_sin1</th>\n",
       "      <th>GCVI_sin2</th>\n",
       "      <th>GCVI_t</th>\n",
       "      <th>GCVI_variance</th>\n",
       "      <th>GREEN_constant</th>\n",
       "      <th>GREEN_cos1</th>\n",
       "      <th>GREEN_cos2</th>\n",
       "      <th>GREEN_mean</th>\n",
       "      <th>GREEN_r2</th>\n",
       "      <th>GREEN_rmse</th>\n",
       "      <th>GREEN_sin1</th>\n",
       "      <th>GREEN_sin2</th>\n",
       "      <th>GREEN_t</th>\n",
       "      <th>GREEN_variance</th>\n",
       "      <th>NBR1_constant</th>\n",
       "      <th>NBR1_cos1</th>\n",
       "      <th>NBR1_cos2</th>\n",
       "      <th>NBR1_mean</th>\n",
       "      <th>NBR1_r2</th>\n",
       "      <th>NBR1_rmse</th>\n",
       "      <th>NBR1_sin1</th>\n",
       "      <th>NBR1_sin2</th>\n",
       "      <th>NBR1_t</th>\n",
       "      <th>NBR1_variance</th>\n",
       "      <th>NDTI_constant</th>\n",
       "      <th>NDTI_cos1</th>\n",
       "      <th>NDTI_cos2</th>\n",
       "      <th>NDTI_mean</th>\n",
       "      <th>NDTI_r2</th>\n",
       "      <th>NDTI_rmse</th>\n",
       "      <th>NDTI_sin1</th>\n",
       "      <th>NDTI_sin2</th>\n",
       "      <th>NDTI_t</th>\n",
       "      <th>NDTI_variance</th>\n",
       "      <th>NDVI_constant</th>\n",
       "      <th>NDVI_cos1</th>\n",
       "      <th>NDVI_cos2</th>\n",
       "      <th>NDVI_mean</th>\n",
       "      <th>NDVI_r2</th>\n",
       "      <th>NDVI_rmse</th>\n",
       "      <th>NDVI_sin1</th>\n",
       "      <th>NDVI_sin2</th>\n",
       "      <th>NDVI_t</th>\n",
       "      <th>NDVI_variance</th>\n",
       "      <th>NIR_constant</th>\n",
       "      <th>NIR_cos1</th>\n",
       "      <th>NIR_cos2</th>\n",
       "      <th>NIR_mean</th>\n",
       "      <th>NIR_r2</th>\n",
       "      <th>NIR_rmse</th>\n",
       "      <th>NIR_sin1</th>\n",
       "      <th>NIR_sin2</th>\n",
       "      <th>NIR_t</th>\n",
       "      <th>NIR_variance</th>\n",
       "      <th>Ptot</th>\n",
       "      <th>RDED4_constant</th>\n",
       "      <th>RDED4_cos1</th>\n",
       "      <th>RDED4_cos2</th>\n",
       "      <th>RDED4_mean</th>\n",
       "      <th>RDED4_r2</th>\n",
       "      <th>RDED4_rmse</th>\n",
       "      <th>RDED4_sin1</th>\n",
       "      <th>RDED4_sin2</th>\n",
       "      <th>RDED4_t</th>\n",
       "      <th>RDED4_variance</th>\n",
       "      <th>RED_constant</th>\n",
       "      <th>RED_cos1</th>\n",
       "      <th>RED_cos2</th>\n",
       "      <th>RED_mean</th>\n",
       "      <th>RED_r2</th>\n",
       "      <th>RED_rmse</th>\n",
       "      <th>RED_sin1</th>\n",
       "      <th>RED_sin2</th>\n",
       "      <th>RED_t</th>\n",
       "      <th>RED_variance</th>\n",
       "      <th>SLO</th>\n",
       "      <th>SNDVI_constant</th>\n",
       "      <th>SNDVI_cos1</th>\n",
       "      <th>SNDVI_cos2</th>\n",
       "      <th>SNDVI_mean</th>\n",
       "      <th>SNDVI_r2</th>\n",
       "      <th>SNDVI_rmse</th>\n",
       "      <th>SNDVI_sin1</th>\n",
       "      <th>SNDVI_sin2</th>\n",
       "      <th>SNDVI_t</th>\n",
       "      <th>SNDVI_variance</th>\n",
       "      <th>SWIR1_constant</th>\n",
       "      <th>SWIR1_cos1</th>\n",
       "      <th>SWIR1_cos2</th>\n",
       "      <th>SWIR1_mean</th>\n",
       "      <th>SWIR1_r2</th>\n",
       "      <th>SWIR1_rmse</th>\n",
       "      <th>SWIR1_sin1</th>\n",
       "      <th>SWIR1_sin2</th>\n",
       "      <th>SWIR1_t</th>\n",
       "      <th>SWIR1_variance</th>\n",
       "      <th>SWIR2_constant</th>\n",
       "      <th>SWIR2_cos1</th>\n",
       "      <th>SWIR2_cos2</th>\n",
       "      <th>SWIR2_mean</th>\n",
       "      <th>SWIR2_r2</th>\n",
       "      <th>SWIR2_rmse</th>\n",
       "      <th>SWIR2_sin1</th>\n",
       "      <th>SWIR2_sin2</th>\n",
       "      <th>SWIR2_t</th>\n",
       "      <th>SWIR2_variance</th>\n",
       "      <th>Tavg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65050</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>306.236739</td>\n",
       "      <td>528178.1776</td>\n",
       "      <td>90086.8010</td>\n",
       "      <td>4100.358135</td>\n",
       "      <td>582484.5895</td>\n",
       "      <td>0.000</td>\n",
       "      <td>76131.17230</td>\n",
       "      <td>173639.72840</td>\n",
       "      <td>52293.82912</td>\n",
       "      <td>96604.004630</td>\n",
       "      <td>2575.542542</td>\n",
       "      <td>23.190459</td>\n",
       "      <td>532.294055</td>\n",
       "      <td>30963726.65</td>\n",
       "      <td>-5.843684e+06</td>\n",
       "      <td>-1.352605e+06</td>\n",
       "      <td>29367893.78</td>\n",
       "      <td>9284874.586</td>\n",
       "      <td>3126710.898</td>\n",
       "      <td>-1.593106e+07</td>\n",
       "      <td>4.623245e+04</td>\n",
       "      <td>-4818272.732</td>\n",
       "      <td>1.510712e+07</td>\n",
       "      <td>8.034497e+05</td>\n",
       "      <td>21003.62585</td>\n",
       "      <td>4905.827341</td>\n",
       "      <td>8.433110e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76227.50908</td>\n",
       "      <td>123560.51480</td>\n",
       "      <td>43582.34105</td>\n",
       "      <td>87166.52047</td>\n",
       "      <td>1433.302243</td>\n",
       "      <td>463273.58130</td>\n",
       "      <td>-7.633960e+05</td>\n",
       "      <td>55986.42755</td>\n",
       "      <td>4.021584e+05</td>\n",
       "      <td>9592590.693</td>\n",
       "      <td>278688.4838</td>\n",
       "      <td>-1.881155e+06</td>\n",
       "      <td>17033.86223</td>\n",
       "      <td>-334422.8765</td>\n",
       "      <td>216996.8683</td>\n",
       "      <td>2.555126e+06</td>\n",
       "      <td>-153446.3158</td>\n",
       "      <td>40588.19758</td>\n",
       "      <td>2.376836e+06</td>\n",
       "      <td>7184049.152</td>\n",
       "      <td>173673.6568</td>\n",
       "      <td>-755274.0135</td>\n",
       "      <td>146214.03950</td>\n",
       "      <td>-472168.73690</td>\n",
       "      <td>25617.79672</td>\n",
       "      <td>5859475.350</td>\n",
       "      <td>-8.087324e+05</td>\n",
       "      <td>-19735.69277</td>\n",
       "      <td>5916454.285</td>\n",
       "      <td>9468809.621</td>\n",
       "      <td>390322.8142</td>\n",
       "      <td>-2187971.305</td>\n",
       "      <td>-28332.01851</td>\n",
       "      <td>-1.769286e+05</td>\n",
       "      <td>307849.3300</td>\n",
       "      <td>3098372.347</td>\n",
       "      <td>-417895.8900</td>\n",
       "      <td>-56739.40869</td>\n",
       "      <td>3199624.732</td>\n",
       "      <td>8.787986e+06</td>\n",
       "      <td>238022.6846</td>\n",
       "      <td>-816850.95510</td>\n",
       "      <td>168196.96900</td>\n",
       "      <td>91178.61410</td>\n",
       "      <td>55190.46280</td>\n",
       "      <td>126446.0</td>\n",
       "      <td>3381615.891</td>\n",
       "      <td>-491078.8273</td>\n",
       "      <td>-20805.25525</td>\n",
       "      <td>3374126.497</td>\n",
       "      <td>8651731.802</td>\n",
       "      <td>237437.1363</td>\n",
       "      <td>-664232.46810</td>\n",
       "      <td>151849.15240</td>\n",
       "      <td>-24969.93094</td>\n",
       "      <td>44724.40370</td>\n",
       "      <td>7.858479e+05</td>\n",
       "      <td>101244.0110</td>\n",
       "      <td>27550.76646</td>\n",
       "      <td>7.832893e+05</td>\n",
       "      <td>0.000</td>\n",
       "      <td>90945.57351</td>\n",
       "      <td>328863.0039</td>\n",
       "      <td>14220.81737</td>\n",
       "      <td>41060.34069</td>\n",
       "      <td>7633.548950</td>\n",
       "      <td>2.742257</td>\n",
       "      <td>4161206.106</td>\n",
       "      <td>-6.743411e+05</td>\n",
       "      <td>-41553.41011</td>\n",
       "      <td>4234060.549</td>\n",
       "      <td>9539888.905</td>\n",
       "      <td>281657.0740</td>\n",
       "      <td>-1.692620e+06</td>\n",
       "      <td>47555.89747</td>\n",
       "      <td>-78489.34817</td>\n",
       "      <td>187369.92490</td>\n",
       "      <td>2780950.574</td>\n",
       "      <td>68921.26486</td>\n",
       "      <td>-31681.60591</td>\n",
       "      <td>2898062.780</td>\n",
       "      <td>0.000</td>\n",
       "      <td>189168.4001</td>\n",
       "      <td>338548.7052</td>\n",
       "      <td>92196.19331</td>\n",
       "      <td>256602.91310</td>\n",
       "      <td>9432.264507</td>\n",
       "      <td>1662812.244</td>\n",
       "      <td>83637.20826</td>\n",
       "      <td>-41004.889640</td>\n",
       "      <td>1813960.212</td>\n",
       "      <td>2.616401e+05</td>\n",
       "      <td>148482.6661</td>\n",
       "      <td>510695.9847</td>\n",
       "      <td>7949.12460</td>\n",
       "      <td>365843.3685</td>\n",
       "      <td>12057.98434</td>\n",
       "      <td>2815.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33403</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>268.813534</td>\n",
       "      <td>520043.8741</td>\n",
       "      <td>111342.7888</td>\n",
       "      <td>21013.530470</td>\n",
       "      <td>521159.0788</td>\n",
       "      <td>282962.014</td>\n",
       "      <td>221801.46800</td>\n",
       "      <td>71160.93556</td>\n",
       "      <td>88514.38491</td>\n",
       "      <td>-68576.377910</td>\n",
       "      <td>10043.545810</td>\n",
       "      <td>70.529261</td>\n",
       "      <td>744.631374</td>\n",
       "      <td>25486566.75</td>\n",
       "      <td>-1.077294e+07</td>\n",
       "      <td>-1.489064e+06</td>\n",
       "      <td>21754300.26</td>\n",
       "      <td>7643107.011</td>\n",
       "      <td>5097889.490</td>\n",
       "      <td>-5.079846e+06</td>\n",
       "      <td>-1.855463e+06</td>\n",
       "      <td>-1838881.269</td>\n",
       "      <td>1.149109e+07</td>\n",
       "      <td>7.450565e+05</td>\n",
       "      <td>53446.10830</td>\n",
       "      <td>-3377.697969</td>\n",
       "      <td>7.672712e+05</td>\n",
       "      <td>368042.61</td>\n",
       "      <td>215185.22290</td>\n",
       "      <td>177906.13290</td>\n",
       "      <td>89198.87596</td>\n",
       "      <td>28043.30217</td>\n",
       "      <td>9447.385040</td>\n",
       "      <td>-56959.07265</td>\n",
       "      <td>-1.985843e+06</td>\n",
       "      <td>-130025.48820</td>\n",
       "      <td>-8.364843e+05</td>\n",
       "      <td>9104928.285</td>\n",
       "      <td>464348.5476</td>\n",
       "      <td>-5.787120e+05</td>\n",
       "      <td>-322787.50290</td>\n",
       "      <td>-488528.7580</td>\n",
       "      <td>254673.4002</td>\n",
       "      <td>2.343221e+06</td>\n",
       "      <td>-911974.2079</td>\n",
       "      <td>-98704.33284</td>\n",
       "      <td>1.836493e+06</td>\n",
       "      <td>8551323.751</td>\n",
       "      <td>292268.0755</td>\n",
       "      <td>-485077.8029</td>\n",
       "      <td>-107632.88650</td>\n",
       "      <td>-551467.84260</td>\n",
       "      <td>66399.16158</td>\n",
       "      <td>5081766.716</td>\n",
       "      <td>-2.279128e+06</td>\n",
       "      <td>-105880.62640</td>\n",
       "      <td>4167130.628</td>\n",
       "      <td>8612914.517</td>\n",
       "      <td>728993.6974</td>\n",
       "      <td>-1148340.699</td>\n",
       "      <td>-453623.60500</td>\n",
       "      <td>-6.750696e+05</td>\n",
       "      <td>416049.6623</td>\n",
       "      <td>2426125.673</td>\n",
       "      <td>-511884.1189</td>\n",
       "      <td>-85483.41141</td>\n",
       "      <td>2231211.655</td>\n",
       "      <td>5.421294e+06</td>\n",
       "      <td>308059.8758</td>\n",
       "      <td>200694.46290</td>\n",
       "      <td>-10712.59599</td>\n",
       "      <td>-49709.71438</td>\n",
       "      <td>25259.50491</td>\n",
       "      <td>67588.0</td>\n",
       "      <td>2629893.829</td>\n",
       "      <td>-614055.5825</td>\n",
       "      <td>-55031.32127</td>\n",
       "      <td>2386416.742</td>\n",
       "      <td>6830538.042</td>\n",
       "      <td>299476.4853</td>\n",
       "      <td>138177.70070</td>\n",
       "      <td>23692.22858</td>\n",
       "      <td>-112190.97010</td>\n",
       "      <td>28557.17409</td>\n",
       "      <td>8.017304e+05</td>\n",
       "      <td>305019.3358</td>\n",
       "      <td>-41975.73443</td>\n",
       "      <td>9.325906e+05</td>\n",
       "      <td>1439553.783</td>\n",
       "      <td>207335.37370</td>\n",
       "      <td>331221.8205</td>\n",
       "      <td>145060.80920</td>\n",
       "      <td>131291.15770</td>\n",
       "      <td>16848.387460</td>\n",
       "      <td>9.004978</td>\n",
       "      <td>3358195.995</td>\n",
       "      <td>-1.564481e+06</td>\n",
       "      <td>-72991.59290</td>\n",
       "      <td>2727142.091</td>\n",
       "      <td>8575098.693</td>\n",
       "      <td>484576.1889</td>\n",
       "      <td>-5.931784e+05</td>\n",
       "      <td>-305551.68720</td>\n",
       "      <td>-430572.25320</td>\n",
       "      <td>178474.52930</td>\n",
       "      <td>2452477.234</td>\n",
       "      <td>479652.28060</td>\n",
       "      <td>-58885.99220</td>\n",
       "      <td>2642768.954</td>\n",
       "      <td>8331235.344</td>\n",
       "      <td>237987.5122</td>\n",
       "      <td>536693.0155</td>\n",
       "      <td>236267.29270</td>\n",
       "      <td>184742.76400</td>\n",
       "      <td>34452.740480</td>\n",
       "      <td>1552623.277</td>\n",
       "      <td>648092.78470</td>\n",
       "      <td>988.492264</td>\n",
       "      <td>1894117.754</td>\n",
       "      <td>8.697937e+06</td>\n",
       "      <td>238299.6896</td>\n",
       "      <td>571774.9742</td>\n",
       "      <td>251199.64830</td>\n",
       "      <td>380568.2863</td>\n",
       "      <td>44573.17974</td>\n",
       "      <td>2491.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67603</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>151.072313</td>\n",
       "      <td>540275.6733</td>\n",
       "      <td>102720.7981</td>\n",
       "      <td>-32894.067290</td>\n",
       "      <td>530325.2644</td>\n",
       "      <td>0.000</td>\n",
       "      <td>128507.55070</td>\n",
       "      <td>69511.70563</td>\n",
       "      <td>52642.48898</td>\n",
       "      <td>-92790.161540</td>\n",
       "      <td>3205.974285</td>\n",
       "      <td>103.372486</td>\n",
       "      <td>1026.114269</td>\n",
       "      <td>24088031.23</td>\n",
       "      <td>-6.292671e+06</td>\n",
       "      <td>2.523676e+06</td>\n",
       "      <td>21219894.39</td>\n",
       "      <td>7058058.360</td>\n",
       "      <td>3968183.405</td>\n",
       "      <td>-5.437737e+06</td>\n",
       "      <td>-1.771527e+06</td>\n",
       "      <td>-1473325.605</td>\n",
       "      <td>5.981850e+06</td>\n",
       "      <td>7.995358e+05</td>\n",
       "      <td>33717.11137</td>\n",
       "      <td>-9846.225058</td>\n",
       "      <td>7.771466e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>121870.34880</td>\n",
       "      <td>87219.24265</td>\n",
       "      <td>70654.03809</td>\n",
       "      <td>-81460.43397</td>\n",
       "      <td>3033.742653</td>\n",
       "      <td>-105289.32320</td>\n",
       "      <td>-1.274859e+06</td>\n",
       "      <td>779012.81110</td>\n",
       "      <td>-6.991448e+05</td>\n",
       "      <td>8536064.311</td>\n",
       "      <td>480380.5405</td>\n",
       "      <td>-1.114443e+06</td>\n",
       "      <td>74834.49913</td>\n",
       "      <td>-331754.4366</td>\n",
       "      <td>172236.1513</td>\n",
       "      <td>1.573334e+06</td>\n",
       "      <td>-670855.0632</td>\n",
       "      <td>305315.32160</td>\n",
       "      <td>1.443517e+06</td>\n",
       "      <td>8799932.370</td>\n",
       "      <td>202124.4073</td>\n",
       "      <td>-430178.0357</td>\n",
       "      <td>117483.07310</td>\n",
       "      <td>169249.96500</td>\n",
       "      <td>36613.15864</td>\n",
       "      <td>5494103.965</td>\n",
       "      <td>-1.333350e+06</td>\n",
       "      <td>815211.80090</td>\n",
       "      <td>4700365.061</td>\n",
       "      <td>8504662.175</td>\n",
       "      <td>553068.8053</td>\n",
       "      <td>-1305233.894</td>\n",
       "      <td>-384330.34910</td>\n",
       "      <td>-6.407246e+05</td>\n",
       "      <td>212094.1358</td>\n",
       "      <td>2602069.871</td>\n",
       "      <td>-343664.3319</td>\n",
       "      <td>193130.23470</td>\n",
       "      <td>2346219.657</td>\n",
       "      <td>1.396782e+06</td>\n",
       "      <td>280981.4322</td>\n",
       "      <td>-148079.59790</td>\n",
       "      <td>78288.94625</td>\n",
       "      <td>-310662.78820</td>\n",
       "      <td>18580.53606</td>\n",
       "      <td>66096.0</td>\n",
       "      <td>2775789.601</td>\n",
       "      <td>-400451.9522</td>\n",
       "      <td>171911.59300</td>\n",
       "      <td>2516961.023</td>\n",
       "      <td>1178265.159</td>\n",
       "      <td>270795.0031</td>\n",
       "      <td>-167084.98550</td>\n",
       "      <td>107957.01660</td>\n",
       "      <td>-285450.79540</td>\n",
       "      <td>17588.44345</td>\n",
       "      <td>7.458267e+05</td>\n",
       "      <td>191565.9213</td>\n",
       "      <td>-124943.24130</td>\n",
       "      <td>8.404934e+05</td>\n",
       "      <td>0.000</td>\n",
       "      <td>133147.09460</td>\n",
       "      <td>248258.0875</td>\n",
       "      <td>123154.33720</td>\n",
       "      <td>38298.77630</td>\n",
       "      <td>8434.396945</td>\n",
       "      <td>3.334010</td>\n",
       "      <td>3710017.096</td>\n",
       "      <td>-9.615002e+05</td>\n",
       "      <td>586382.50550</td>\n",
       "      <td>3114419.944</td>\n",
       "      <td>8343599.216</td>\n",
       "      <td>397338.1950</td>\n",
       "      <td>-8.475202e+05</td>\n",
       "      <td>-194991.13010</td>\n",
       "      <td>-528363.93840</td>\n",
       "      <td>99023.76344</td>\n",
       "      <td>2653306.324</td>\n",
       "      <td>304328.19050</td>\n",
       "      <td>-225506.89930</td>\n",
       "      <td>2703295.289</td>\n",
       "      <td>7106868.446</td>\n",
       "      <td>271772.2895</td>\n",
       "      <td>477940.7194</td>\n",
       "      <td>88491.87210</td>\n",
       "      <td>-147970.27520</td>\n",
       "      <td>31064.515850</td>\n",
       "      <td>1985264.876</td>\n",
       "      <td>445003.23070</td>\n",
       "      <td>-295049.423500</td>\n",
       "      <td>2081167.794</td>\n",
       "      <td>8.199563e+06</td>\n",
       "      <td>261912.4926</td>\n",
       "      <td>553148.8109</td>\n",
       "      <td>60210.98531</td>\n",
       "      <td>-145757.2449</td>\n",
       "      <td>39670.43397</td>\n",
       "      <td>2438.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27880</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>99.645390</td>\n",
       "      <td>540003.3130</td>\n",
       "      <td>102585.9423</td>\n",
       "      <td>-39601.955080</td>\n",
       "      <td>546266.7262</td>\n",
       "      <td>0.000</td>\n",
       "      <td>117503.74230</td>\n",
       "      <td>77797.32057</td>\n",
       "      <td>55842.12435</td>\n",
       "      <td>-7670.785343</td>\n",
       "      <td>2540.086525</td>\n",
       "      <td>59.888889</td>\n",
       "      <td>610.604728</td>\n",
       "      <td>23088247.80</td>\n",
       "      <td>-7.060261e+06</td>\n",
       "      <td>1.964392e+06</td>\n",
       "      <td>21160024.96</td>\n",
       "      <td>7401073.243</td>\n",
       "      <td>3815793.550</td>\n",
       "      <td>-5.586269e+06</td>\n",
       "      <td>-3.202540e+05</td>\n",
       "      <td>-2705204.986</td>\n",
       "      <td>5.832727e+06</td>\n",
       "      <td>8.103062e+05</td>\n",
       "      <td>15030.15745</td>\n",
       "      <td>-1001.079905</td>\n",
       "      <td>8.187527e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>122655.75410</td>\n",
       "      <td>112192.12530</td>\n",
       "      <td>88356.52388</td>\n",
       "      <td>19173.71584</td>\n",
       "      <td>2668.711111</td>\n",
       "      <td>-51101.40142</td>\n",
       "      <td>-1.594227e+06</td>\n",
       "      <td>351548.72480</td>\n",
       "      <td>-5.442817e+05</td>\n",
       "      <td>9037890.795</td>\n",
       "      <td>405232.9608</td>\n",
       "      <td>-6.535529e+05</td>\n",
       "      <td>71221.72577</td>\n",
       "      <td>-645706.0927</td>\n",
       "      <td>173248.1513</td>\n",
       "      <td>1.781833e+06</td>\n",
       "      <td>-735892.9485</td>\n",
       "      <td>184093.70970</td>\n",
       "      <td>1.496716e+06</td>\n",
       "      <td>8031877.129</td>\n",
       "      <td>287248.3872</td>\n",
       "      <td>-359713.1243</td>\n",
       "      <td>23216.33664</td>\n",
       "      <td>-418345.31110</td>\n",
       "      <td>42581.91300</td>\n",
       "      <td>4471406.703</td>\n",
       "      <td>-1.944771e+06</td>\n",
       "      <td>582071.65390</td>\n",
       "      <td>3697549.242</td>\n",
       "      <td>8677554.224</td>\n",
       "      <td>620897.1636</td>\n",
       "      <td>-1172727.706</td>\n",
       "      <td>-11271.98771</td>\n",
       "      <td>-1.171547e+06</td>\n",
       "      <td>301139.0908</td>\n",
       "      <td>2587027.920</td>\n",
       "      <td>-521796.7135</td>\n",
       "      <td>192811.18160</td>\n",
       "      <td>2468457.942</td>\n",
       "      <td>6.686816e+06</td>\n",
       "      <td>272766.1962</td>\n",
       "      <td>-107085.22700</td>\n",
       "      <td>182566.27000</td>\n",
       "      <td>-128796.21890</td>\n",
       "      <td>28104.96265</td>\n",
       "      <td>103319.0</td>\n",
       "      <td>2869880.007</td>\n",
       "      <td>-541866.4993</td>\n",
       "      <td>143824.47330</td>\n",
       "      <td>2696838.584</td>\n",
       "      <td>7548438.957</td>\n",
       "      <td>264769.1877</td>\n",
       "      <td>-186877.77540</td>\n",
       "      <td>199277.48180</td>\n",
       "      <td>-243279.09880</td>\n",
       "      <td>28920.72482</td>\n",
       "      <td>9.977169e+05</td>\n",
       "      <td>313359.3565</td>\n",
       "      <td>-98536.06288</td>\n",
       "      <td>1.129305e+06</td>\n",
       "      <td>0.000</td>\n",
       "      <td>181855.15600</td>\n",
       "      <td>262324.1905</td>\n",
       "      <td>98342.89314</td>\n",
       "      <td>201189.56640</td>\n",
       "      <td>11303.387230</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3098431.934</td>\n",
       "      <td>-1.407098e+06</td>\n",
       "      <td>440039.97120</td>\n",
       "      <td>2566647.429</td>\n",
       "      <td>8792406.665</td>\n",
       "      <td>424655.3059</td>\n",
       "      <td>-7.943952e+05</td>\n",
       "      <td>39428.95177</td>\n",
       "      <td>-791727.74000</td>\n",
       "      <td>155079.92580</td>\n",
       "      <td>2617552.920</td>\n",
       "      <td>349642.84300</td>\n",
       "      <td>-23740.80946</td>\n",
       "      <td>2738402.017</td>\n",
       "      <td>1937196.829</td>\n",
       "      <td>299123.5939</td>\n",
       "      <td>254727.2080</td>\n",
       "      <td>179708.90640</td>\n",
       "      <td>158092.73620</td>\n",
       "      <td>18669.939480</td>\n",
       "      <td>1828379.731</td>\n",
       "      <td>536305.44350</td>\n",
       "      <td>-76296.884160</td>\n",
       "      <td>2051166.785</td>\n",
       "      <td>7.187362e+06</td>\n",
       "      <td>271888.3839</td>\n",
       "      <td>352486.3660</td>\n",
       "      <td>166861.79240</td>\n",
       "      <td>321106.5433</td>\n",
       "      <td>26555.78156</td>\n",
       "      <td>2636.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14418</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>263.847756</td>\n",
       "      <td>797497.8810</td>\n",
       "      <td>122385.5599</td>\n",
       "      <td>-103710.072200</td>\n",
       "      <td>819440.3941</td>\n",
       "      <td>0.000</td>\n",
       "      <td>65285.49768</td>\n",
       "      <td>279521.21490</td>\n",
       "      <td>51083.07612</td>\n",
       "      <td>58575.526240</td>\n",
       "      <td>4966.160405</td>\n",
       "      <td>26.716899</td>\n",
       "      <td>663.444632</td>\n",
       "      <td>15954363.74</td>\n",
       "      <td>-5.378930e+06</td>\n",
       "      <td>8.120316e+05</td>\n",
       "      <td>14622262.37</td>\n",
       "      <td>9127750.138</td>\n",
       "      <td>1731791.014</td>\n",
       "      <td>-7.530542e+06</td>\n",
       "      <td>2.274176e+05</td>\n",
       "      <td>-1344951.318</td>\n",
       "      <td>3.975110e+06</td>\n",
       "      <td>1.092474e+06</td>\n",
       "      <td>35366.52568</td>\n",
       "      <td>-101625.158400</td>\n",
       "      <td>1.063179e+06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64163.08316</td>\n",
       "      <td>334923.14100</td>\n",
       "      <td>33570.42845</td>\n",
       "      <td>29829.09652</td>\n",
       "      <td>6663.727452</td>\n",
       "      <td>-949212.66270</td>\n",
       "      <td>-1.443777e+06</td>\n",
       "      <td>219614.56900</td>\n",
       "      <td>-1.463655e+06</td>\n",
       "      <td>9395118.950</td>\n",
       "      <td>249225.2386</td>\n",
       "      <td>-7.671768e+05</td>\n",
       "      <td>131394.82060</td>\n",
       "      <td>-287943.8862</td>\n",
       "      <td>118868.1428</td>\n",
       "      <td>9.732814e+05</td>\n",
       "      <td>-567412.6892</td>\n",
       "      <td>188374.09390</td>\n",
       "      <td>8.674565e+05</td>\n",
       "      <td>9011932.642</td>\n",
       "      <td>168902.9727</td>\n",
       "      <td>-710767.3364</td>\n",
       "      <td>-70863.29267</td>\n",
       "      <td>-87737.57647</td>\n",
       "      <td>39644.90530</td>\n",
       "      <td>3567546.504</td>\n",
       "      <td>-1.684884e+06</td>\n",
       "      <td>400899.76120</td>\n",
       "      <td>3052894.718</td>\n",
       "      <td>9490520.055</td>\n",
       "      <td>315002.0080</td>\n",
       "      <td>-1783082.461</td>\n",
       "      <td>810.83059</td>\n",
       "      <td>-4.639359e+05</td>\n",
       "      <td>251538.8427</td>\n",
       "      <td>2642095.392</td>\n",
       "      <td>-402962.8264</td>\n",
       "      <td>-96587.54805</td>\n",
       "      <td>2436510.402</td>\n",
       "      <td>7.870944e+05</td>\n",
       "      <td>171566.5542</td>\n",
       "      <td>-16017.56142</td>\n",
       "      <td>21666.23020</td>\n",
       "      <td>-128837.62970</td>\n",
       "      <td>14631.47995</td>\n",
       "      <td>109903.0</td>\n",
       "      <td>2824125.676</td>\n",
       "      <td>-468419.6755</td>\n",
       "      <td>-86014.21528</td>\n",
       "      <td>2639098.397</td>\n",
       "      <td>2369171.617</td>\n",
       "      <td>165650.1337</td>\n",
       "      <td>-11553.39665</td>\n",
       "      <td>83974.17659</td>\n",
       "      <td>-48042.18418</td>\n",
       "      <td>16981.90601</td>\n",
       "      <td>1.294223e+06</td>\n",
       "      <td>256646.7089</td>\n",
       "      <td>-185644.07390</td>\n",
       "      <td>1.352449e+06</td>\n",
       "      <td>2932273.590</td>\n",
       "      <td>96236.60701</td>\n",
       "      <td>527386.8221</td>\n",
       "      <td>53702.73547</td>\n",
       "      <td>118559.27400</td>\n",
       "      <td>16601.639510</td>\n",
       "      <td>1.773885</td>\n",
       "      <td>2511715.280</td>\n",
       "      <td>-1.208438e+06</td>\n",
       "      <td>267676.54160</td>\n",
       "      <td>2113631.553</td>\n",
       "      <td>9365876.246</td>\n",
       "      <td>250125.0936</td>\n",
       "      <td>-1.202947e+06</td>\n",
       "      <td>5734.40833</td>\n",
       "      <td>-358116.25850</td>\n",
       "      <td>122492.51150</td>\n",
       "      <td>3198884.276</td>\n",
       "      <td>372289.17070</td>\n",
       "      <td>-268008.84210</td>\n",
       "      <td>3267926.654</td>\n",
       "      <td>3186558.457</td>\n",
       "      <td>135751.9242</td>\n",
       "      <td>490262.3995</td>\n",
       "      <td>22351.75855</td>\n",
       "      <td>66274.48389</td>\n",
       "      <td>19567.153090</td>\n",
       "      <td>2699467.602</td>\n",
       "      <td>586499.69140</td>\n",
       "      <td>-335754.779900</td>\n",
       "      <td>2833559.915</td>\n",
       "      <td>9.350080e+06</td>\n",
       "      <td>168176.8342</td>\n",
       "      <td>835163.2005</td>\n",
       "      <td>106029.91970</td>\n",
       "      <td>171106.6689</td>\n",
       "      <td>47862.02589</td>\n",
       "      <td>2570.302941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GEOM_ID  YEAR  MAIZELAND         ASP  BLUE_constant    BLUE_cos1  \\\n",
       "0    65050  2019          0  306.236739    528178.1776   90086.8010   \n",
       "1    33403  2018          1  268.813534    520043.8741  111342.7888   \n",
       "2    67603  2018          0  151.072313    540275.6733  102720.7981   \n",
       "3    27880  2019          0   99.645390    540003.3130  102585.9423   \n",
       "4    14418  2019          1  263.847756    797497.8810  122385.5599   \n",
       "\n",
       "       BLUE_cos2    BLUE_mean     BLUE_r2     BLUE_rmse     BLUE_sin1  \\\n",
       "0    4100.358135  582484.5895       0.000   76131.17230  173639.72840   \n",
       "1   21013.530470  521159.0788  282962.014  221801.46800   71160.93556   \n",
       "2  -32894.067290  530325.2644       0.000  128507.55070   69511.70563   \n",
       "3  -39601.955080  546266.7262       0.000  117503.74230   77797.32057   \n",
       "4 -103710.072200  819440.3941       0.000   65285.49768  279521.21490   \n",
       "\n",
       "     BLUE_sin2        BLUE_t  BLUE_variance    COUNT_s2         ELEV  \\\n",
       "0  52293.82912  96604.004630    2575.542542   23.190459   532.294055   \n",
       "1  88514.38491 -68576.377910   10043.545810   70.529261   744.631374   \n",
       "2  52642.48898 -92790.161540    3205.974285  103.372486  1026.114269   \n",
       "3  55842.12435  -7670.785343    2540.086525   59.888889   610.604728   \n",
       "4  51083.07612  58575.526240    4966.160405   26.716899   663.444632   \n",
       "\n",
       "   GCVI_constant     GCVI_cos1     GCVI_cos2    GCVI_mean      GCVI_r2  \\\n",
       "0    30963726.65 -5.843684e+06 -1.352605e+06  29367893.78  9284874.586   \n",
       "1    25486566.75 -1.077294e+07 -1.489064e+06  21754300.26  7643107.011   \n",
       "2    24088031.23 -6.292671e+06  2.523676e+06  21219894.39  7058058.360   \n",
       "3    23088247.80 -7.060261e+06  1.964392e+06  21160024.96  7401073.243   \n",
       "4    15954363.74 -5.378930e+06  8.120316e+05  14622262.37  9127750.138   \n",
       "\n",
       "     GCVI_rmse     GCVI_sin1     GCVI_sin2       GCVI_t  GCVI_variance  \\\n",
       "0  3126710.898 -1.593106e+07  4.623245e+04 -4818272.732   1.510712e+07   \n",
       "1  5097889.490 -5.079846e+06 -1.855463e+06 -1838881.269   1.149109e+07   \n",
       "2  3968183.405 -5.437737e+06 -1.771527e+06 -1473325.605   5.981850e+06   \n",
       "3  3815793.550 -5.586269e+06 -3.202540e+05 -2705204.986   5.832727e+06   \n",
       "4  1731791.014 -7.530542e+06  2.274176e+05 -1344951.318   3.975110e+06   \n",
       "\n",
       "   GREEN_constant   GREEN_cos1     GREEN_cos2    GREEN_mean   GREEN_r2  \\\n",
       "0    8.034497e+05  21003.62585    4905.827341  8.433110e+05       0.00   \n",
       "1    7.450565e+05  53446.10830   -3377.697969  7.672712e+05  368042.61   \n",
       "2    7.995358e+05  33717.11137   -9846.225058  7.771466e+05       0.00   \n",
       "3    8.103062e+05  15030.15745   -1001.079905  8.187527e+05       0.00   \n",
       "4    1.092474e+06  35366.52568 -101625.158400  1.063179e+06       0.00   \n",
       "\n",
       "     GREEN_rmse    GREEN_sin1   GREEN_sin2      GREEN_t  GREEN_variance  \\\n",
       "0   76227.50908  123560.51480  43582.34105  87166.52047     1433.302243   \n",
       "1  215185.22290  177906.13290  89198.87596  28043.30217     9447.385040   \n",
       "2  121870.34880   87219.24265  70654.03809 -81460.43397     3033.742653   \n",
       "3  122655.75410  112192.12530  88356.52388  19173.71584     2668.711111   \n",
       "4   64163.08316  334923.14100  33570.42845  29829.09652     6663.727452   \n",
       "\n",
       "   NBR1_constant     NBR1_cos1     NBR1_cos2     NBR1_mean      NBR1_r2  \\\n",
       "0   463273.58130 -7.633960e+05   55986.42755  4.021584e+05  9592590.693   \n",
       "1   -56959.07265 -1.985843e+06 -130025.48820 -8.364843e+05  9104928.285   \n",
       "2  -105289.32320 -1.274859e+06  779012.81110 -6.991448e+05  8536064.311   \n",
       "3   -51101.40142 -1.594227e+06  351548.72480 -5.442817e+05  9037890.795   \n",
       "4  -949212.66270 -1.443777e+06  219614.56900 -1.463655e+06  9395118.950   \n",
       "\n",
       "     NBR1_rmse     NBR1_sin1     NBR1_sin2       NBR1_t  NBR1_variance  \\\n",
       "0  278688.4838 -1.881155e+06   17033.86223 -334422.8765    216996.8683   \n",
       "1  464348.5476 -5.787120e+05 -322787.50290 -488528.7580    254673.4002   \n",
       "2  480380.5405 -1.114443e+06   74834.49913 -331754.4366    172236.1513   \n",
       "3  405232.9608 -6.535529e+05   71221.72577 -645706.0927    173248.1513   \n",
       "4  249225.2386 -7.671768e+05  131394.82060 -287943.8862    118868.1428   \n",
       "\n",
       "   NDTI_constant    NDTI_cos1     NDTI_cos2     NDTI_mean      NDTI_r2  \\\n",
       "0   2.555126e+06 -153446.3158   40588.19758  2.376836e+06  7184049.152   \n",
       "1   2.343221e+06 -911974.2079  -98704.33284  1.836493e+06  8551323.751   \n",
       "2   1.573334e+06 -670855.0632  305315.32160  1.443517e+06  8799932.370   \n",
       "3   1.781833e+06 -735892.9485  184093.70970  1.496716e+06  8031877.129   \n",
       "4   9.732814e+05 -567412.6892  188374.09390  8.674565e+05  9011932.642   \n",
       "\n",
       "     NDTI_rmse    NDTI_sin1     NDTI_sin2        NDTI_t  NDTI_variance  \\\n",
       "0  173673.6568 -755274.0135  146214.03950 -472168.73690    25617.79672   \n",
       "1  292268.0755 -485077.8029 -107632.88650 -551467.84260    66399.16158   \n",
       "2  202124.4073 -430178.0357  117483.07310  169249.96500    36613.15864   \n",
       "3  287248.3872 -359713.1243   23216.33664 -418345.31110    42581.91300   \n",
       "4  168902.9727 -710767.3364  -70863.29267  -87737.57647    39644.90530   \n",
       "\n",
       "   NDVI_constant     NDVI_cos1     NDVI_cos2    NDVI_mean      NDVI_r2  \\\n",
       "0    5859475.350 -8.087324e+05  -19735.69277  5916454.285  9468809.621   \n",
       "1    5081766.716 -2.279128e+06 -105880.62640  4167130.628  8612914.517   \n",
       "2    5494103.965 -1.333350e+06  815211.80090  4700365.061  8504662.175   \n",
       "3    4471406.703 -1.944771e+06  582071.65390  3697549.242  8677554.224   \n",
       "4    3567546.504 -1.684884e+06  400899.76120  3052894.718  9490520.055   \n",
       "\n",
       "     NDVI_rmse    NDVI_sin1     NDVI_sin2        NDVI_t  NDVI_variance  \\\n",
       "0  390322.8142 -2187971.305  -28332.01851 -1.769286e+05    307849.3300   \n",
       "1  728993.6974 -1148340.699 -453623.60500 -6.750696e+05    416049.6623   \n",
       "2  553068.8053 -1305233.894 -384330.34910 -6.407246e+05    212094.1358   \n",
       "3  620897.1636 -1172727.706  -11271.98771 -1.171547e+06    301139.0908   \n",
       "4  315002.0080 -1783082.461     810.83059 -4.639359e+05    251538.8427   \n",
       "\n",
       "   NIR_constant     NIR_cos1      NIR_cos2     NIR_mean        NIR_r2  \\\n",
       "0   3098372.347 -417895.8900  -56739.40869  3199624.732  8.787986e+06   \n",
       "1   2426125.673 -511884.1189  -85483.41141  2231211.655  5.421294e+06   \n",
       "2   2602069.871 -343664.3319  193130.23470  2346219.657  1.396782e+06   \n",
       "3   2587027.920 -521796.7135  192811.18160  2468457.942  6.686816e+06   \n",
       "4   2642095.392 -402962.8264  -96587.54805  2436510.402  7.870944e+05   \n",
       "\n",
       "      NIR_rmse      NIR_sin1      NIR_sin2         NIR_t  NIR_variance  \\\n",
       "0  238022.6846 -816850.95510  168196.96900   91178.61410   55190.46280   \n",
       "1  308059.8758  200694.46290  -10712.59599  -49709.71438   25259.50491   \n",
       "2  280981.4322 -148079.59790   78288.94625 -310662.78820   18580.53606   \n",
       "3  272766.1962 -107085.22700  182566.27000 -128796.21890   28104.96265   \n",
       "4  171566.5542  -16017.56142   21666.23020 -128837.62970   14631.47995   \n",
       "\n",
       "       Ptot  RDED4_constant   RDED4_cos1    RDED4_cos2   RDED4_mean  \\\n",
       "0  126446.0     3381615.891 -491078.8273  -20805.25525  3374126.497   \n",
       "1   67588.0     2629893.829 -614055.5825  -55031.32127  2386416.742   \n",
       "2   66096.0     2775789.601 -400451.9522  171911.59300  2516961.023   \n",
       "3  103319.0     2869880.007 -541866.4993  143824.47330  2696838.584   \n",
       "4  109903.0     2824125.676 -468419.6755  -86014.21528  2639098.397   \n",
       "\n",
       "      RDED4_r2   RDED4_rmse    RDED4_sin1    RDED4_sin2       RDED4_t  \\\n",
       "0  8651731.802  237437.1363 -664232.46810  151849.15240  -24969.93094   \n",
       "1  6830538.042  299476.4853  138177.70070   23692.22858 -112190.97010   \n",
       "2  1178265.159  270795.0031 -167084.98550  107957.01660 -285450.79540   \n",
       "3  7548438.957  264769.1877 -186877.77540  199277.48180 -243279.09880   \n",
       "4  2369171.617  165650.1337  -11553.39665   83974.17659  -48042.18418   \n",
       "\n",
       "   RDED4_variance  RED_constant     RED_cos1      RED_cos2      RED_mean  \\\n",
       "0     44724.40370  7.858479e+05  101244.0110   27550.76646  7.832893e+05   \n",
       "1     28557.17409  8.017304e+05  305019.3358  -41975.73443  9.325906e+05   \n",
       "2     17588.44345  7.458267e+05  191565.9213 -124943.24130  8.404934e+05   \n",
       "3     28920.72482  9.977169e+05  313359.3565  -98536.06288  1.129305e+06   \n",
       "4     16981.90601  1.294223e+06  256646.7089 -185644.07390  1.352449e+06   \n",
       "\n",
       "        RED_r2      RED_rmse     RED_sin1      RED_sin2         RED_t  \\\n",
       "0        0.000   90945.57351  328863.0039   14220.81737   41060.34069   \n",
       "1  1439553.783  207335.37370  331221.8205  145060.80920  131291.15770   \n",
       "2        0.000  133147.09460  248258.0875  123154.33720   38298.77630   \n",
       "3        0.000  181855.15600  262324.1905   98342.89314  201189.56640   \n",
       "4  2932273.590   96236.60701  527386.8221   53702.73547  118559.27400   \n",
       "\n",
       "   RED_variance       SLO  SNDVI_constant    SNDVI_cos1    SNDVI_cos2  \\\n",
       "0   7633.548950  2.742257     4161206.106 -6.743411e+05  -41553.41011   \n",
       "1  16848.387460  9.004978     3358195.995 -1.564481e+06  -72991.59290   \n",
       "2   8434.396945  3.334010     3710017.096 -9.615002e+05  586382.50550   \n",
       "3  11303.387230  4.333333     3098431.934 -1.407098e+06  440039.97120   \n",
       "4  16601.639510  1.773885     2511715.280 -1.208438e+06  267676.54160   \n",
       "\n",
       "    SNDVI_mean     SNDVI_r2   SNDVI_rmse    SNDVI_sin1    SNDVI_sin2  \\\n",
       "0  4234060.549  9539888.905  281657.0740 -1.692620e+06   47555.89747   \n",
       "1  2727142.091  8575098.693  484576.1889 -5.931784e+05 -305551.68720   \n",
       "2  3114419.944  8343599.216  397338.1950 -8.475202e+05 -194991.13010   \n",
       "3  2566647.429  8792406.665  424655.3059 -7.943952e+05   39428.95177   \n",
       "4  2113631.553  9365876.246  250125.0936 -1.202947e+06    5734.40833   \n",
       "\n",
       "        SNDVI_t  SNDVI_variance  SWIR1_constant    SWIR1_cos1    SWIR1_cos2  \\\n",
       "0  -78489.34817    187369.92490     2780950.574   68921.26486  -31681.60591   \n",
       "1 -430572.25320    178474.52930     2452477.234  479652.28060  -58885.99220   \n",
       "2 -528363.93840     99023.76344     2653306.324  304328.19050 -225506.89930   \n",
       "3 -791727.74000    155079.92580     2617552.920  349642.84300  -23740.80946   \n",
       "4 -358116.25850    122492.51150     3198884.276  372289.17070 -268008.84210   \n",
       "\n",
       "    SWIR1_mean     SWIR1_r2   SWIR1_rmse   SWIR1_sin1    SWIR1_sin2  \\\n",
       "0  2898062.780        0.000  189168.4001  338548.7052   92196.19331   \n",
       "1  2642768.954  8331235.344  237987.5122  536693.0155  236267.29270   \n",
       "2  2703295.289  7106868.446  271772.2895  477940.7194   88491.87210   \n",
       "3  2738402.017  1937196.829  299123.5939  254727.2080  179708.90640   \n",
       "4  3267926.654  3186558.457  135751.9242  490262.3995   22351.75855   \n",
       "\n",
       "        SWIR1_t  SWIR1_variance  SWIR2_constant    SWIR2_cos1     SWIR2_cos2  \\\n",
       "0  256602.91310     9432.264507     1662812.244   83637.20826  -41004.889640   \n",
       "1  184742.76400    34452.740480     1552623.277  648092.78470     988.492264   \n",
       "2 -147970.27520    31064.515850     1985264.876  445003.23070 -295049.423500   \n",
       "3  158092.73620    18669.939480     1828379.731  536305.44350  -76296.884160   \n",
       "4   66274.48389    19567.153090     2699467.602  586499.69140 -335754.779900   \n",
       "\n",
       "    SWIR2_mean      SWIR2_r2   SWIR2_rmse   SWIR2_sin1    SWIR2_sin2  \\\n",
       "0  1813960.212  2.616401e+05  148482.6661  510695.9847    7949.12460   \n",
       "1  1894117.754  8.697937e+06  238299.6896  571774.9742  251199.64830   \n",
       "2  2081167.794  8.199563e+06  261912.4926  553148.8109   60210.98531   \n",
       "3  2051166.785  7.187362e+06  271888.3839  352486.3660  166861.79240   \n",
       "4  2833559.915  9.350080e+06  168176.8342  835163.2005  106029.91970   \n",
       "\n",
       "       SWIR2_t  SWIR2_variance         Tavg  \n",
       "0  365843.3685     12057.98434  2815.000000  \n",
       "1  380568.2863     44573.17974  2491.000000  \n",
       "2 -145757.2449     39670.43397  2438.000000  \n",
       "3  321106.5433     26555.78156  2636.000000  \n",
       "4  171106.6689     47862.02589  2570.302941  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8dba6274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 129)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.dropna().reset_index(drop=True)\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "da568f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    716\n",
       "0    284\n",
       "Name: MAIZELAND, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['MAIZELAND'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0a5b71d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df.drop(['GEOM_ID', 'MAIZELAND', 'YEAR'], axis=1)\n",
    "y = train_df.loc[:, 'MAIZELAND']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ab48603b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('selector',\n",
       "                                        SelectKBest(k=20,\n",
       "                                                    score_func=<function mutual_info_classif at 0x7fd42d59f0e0>)),\n",
       "                                       ('classifier',\n",
       "                                        RandomForestClassifier())]),\n",
       "             param_grid={'classifier__max_depth': [10, 15],\n",
       "                         'classifier__max_features': ['sqrt'],\n",
       "                         'classifier__min_samples_split': [50],\n",
       "                         'classifier__n_estimators': [100],\n",
       "                         'selector__k': [20, 'all']},\n",
       "             refit=False, return_train_score=True,\n",
       "             scoring={'ACCURACY': make_scorer(accuracy_score),\n",
       "                      'MCC': make_scorer(matthews_corrcoef)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_pipeline = Pipeline([\n",
    "    ('selector', SelectKBest(mutual_info_classif, k=20)), \n",
    "    ('classifier', RandomForestClassifier())\n",
    "    ])\n",
    "\n",
    "rf_params = {\n",
    "    'selector__k': [20, 'all'],\n",
    "    'classifier__max_features' : ['sqrt'],\n",
    "    'classifier__max_depth' : [10, 15],\n",
    "    'classifier__min_samples_split' : [50],\n",
    "    'classifier__n_estimators' : [100]\n",
    "}\n",
    "\n",
    "\n",
    "rf_grid_model = GridSearchCV(\n",
    "    estimator=rf_pipeline,\n",
    "    param_grid=rf_params,\n",
    "    cv=5,\n",
    "    scoring={\n",
    "        'ACCURACY': make_scorer(accuracy_score),\n",
    "        'MCC': make_scorer(matthews_corrcoef)\n",
    "    },\n",
    "    return_train_score=True,\n",
    "    refit=False,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "rf_grid_model.fit(X, y.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8d3f5186",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store results from hyperparameter tuning\n",
    "crop_results = pd.DataFrame(rf_grid_model.cv_results_)\n",
    "crop_results.to_csv('data/3_ml_model/rf_hyperparameter_tuning.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c716e8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best MCC val MCC: 0.27542578492062747\n",
      "Best MCC train MCC: 0.5748434529734913\n",
      "Best MCC params: \n",
      " - classifier__max_depth :  15\n",
      " - classifier__max_features :  sqrt\n",
      " - classifier__min_samples_split :  50\n",
      " - classifier__n_estimators :  100\n",
      " - selector__k :  20\n"
     ]
    }
   ],
   "source": [
    "metric = 'MCC'\n",
    "bestrow = crop_results.sort_values(by=[f'rank_test_{metric}'], axis=0, ascending=True).iloc[0,:]\n",
    "\n",
    "bestparams = bestrow['params']\n",
    "bestscores = {f'val_{metric}': bestrow[f'mean_test_{metric}'],\n",
    "              f'train_{metric}': bestrow[f'mean_train_{metric}']\n",
    "             }\n",
    "\n",
    "print(f'Best MCC val {metric}: '+ str(bestrow[f'mean_test_{metric}']))\n",
    "print(f'Best MCC train {metric}: '+ str(bestrow[f'mean_train_{metric}']))\n",
    "\n",
    "print(f'Best {metric} params: ')\n",
    "for k, v in bestparams.items():\n",
    "    print(' -', k, ': ',v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "35171f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best MCC val ACCURACY: 0.7449999999999999\n",
      "Best MCC train ACCURACY: 0.8695\n",
      "Best ACCURACY params: \n",
      " - classifier__max_depth :  15\n",
      " - classifier__max_features :  sqrt\n",
      " - classifier__min_samples_split :  50\n",
      " - classifier__n_estimators :  100\n",
      " - selector__k :  all\n"
     ]
    }
   ],
   "source": [
    "metric = 'ACCURACY'\n",
    "bestrow = crop_results.sort_values(by=[f'rank_test_{metric}'], axis=0, ascending=True).iloc[0,:]\n",
    "\n",
    "bestparams = bestrow['params']\n",
    "bestscores = {f'val_{metric}': bestrow[f'mean_test_{metric}'],\n",
    "              f'train_{metric}': bestrow[f'mean_train_{metric}']\n",
    "             }\n",
    "\n",
    "print(f'Best MCC val {metric}: '+ str(bestrow[f'mean_test_{metric}']))\n",
    "print(f'Best MCC train {metric}: '+ str(bestrow[f'mean_train_{metric}']))\n",
    "\n",
    "print(f'Best {metric} params: ')\n",
    "for k, v in bestparams.items():\n",
    "    print(' -', k, ': ',v)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acba4c11",
   "metadata": {},
   "source": [
    "### Step 4: Crop type inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9e2e23c1",
   "metadata": {},
   "outputs": [
    {
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       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
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       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
       "            &lt;style&gt;\n",
       "                #map_ce62c2191cec3afc7c0aadccc7a33adb {\n",
       "                    position: relative;\n",
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       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.fullscreen/1.4.2/Control.FullScreen.min.css&quot;/&gt;\n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.draw/1.0.2/leaflet.draw.js&quot;&gt;&lt;/script&gt;\n",
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       "    &lt;script src=&quot;https://unpkg.com/leaflet-control-geocoder/dist/Control.Geocoder.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://unpkg.com/leaflet-control-geocoder/dist/Control.Geocoder.css&quot;/&gt;\n",
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       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_ce62c2191cec3afc7c0aadccc7a33adb&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;    \n",
       "    \n",
       "            var map_ce62c2191cec3afc7c0aadccc7a33adb = L.map(\n",
       "                &quot;map_ce62c2191cec3afc7c0aadccc7a33adb&quot;,\n",
       "                {\n",
       "                    center: [20.0, 0.0],\n",
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       "                    zoomControl: true,\n",
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       "                    addGoogleMap: true,\n",
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       "            var tile_layer_46d1bee47d397bc71759f82485b997cf = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
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       "            ).addTo(map_ce62c2191cec3afc7c0aadccc7a33adb);\n",
       "        \n",
       "    \n",
       "            var tile_layer_30ca90f103292ba4c1de47a173d0414f = L.tileLayer(\n",
       "                &quot;https://mt1.google.com/vt/lyrs=m\\u0026x={x}\\u0026y={y}\\u0026z={z}&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Google&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 22, &quot;maxZoom&quot;: 22, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_ce62c2191cec3afc7c0aadccc7a33adb);\n",
       "        \n",
       "    \n",
       "            L.control.fullscreen(\n",
       "                {&quot;forceSeparateButton&quot;: false, &quot;position&quot;: &quot;topleft&quot;, &quot;title&quot;: &quot;Full Screen&quot;, &quot;titleCancel&quot;: &quot;Exit Full Screen&quot;}\n",
       "            ).addTo(map_ce62c2191cec3afc7c0aadccc7a33adb);\n",
       "        \n",
       "    \n",
       "            var options = {\n",
       "              position: &quot;topleft&quot;,\n",
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       "            // FeatureGroup is to store editable layers.\n",
       "            var drawnItems = new L.featureGroup().addTo(\n",
       "                map_ce62c2191cec3afc7c0aadccc7a33adb\n",
       "            );\n",
       "            options.edit.featureGroup = drawnItems;\n",
       "            var draw_control_3115bab470e26e7aef4c76678e32f93e = new L.Control.Draw(\n",
       "                options\n",
       "            ).addTo( map_ce62c2191cec3afc7c0aadccc7a33adb );\n",
       "            map_ce62c2191cec3afc7c0aadccc7a33adb.on(L.Draw.Event.CREATED, function(e) {\n",
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       "                layer.on(&#x27;click&#x27;, function() {\n",
       "                    alert(coords);\n",
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       "                });\n",
       "                drawnItems.addLayer(layer);\n",
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       "                drawnItems.addLayer(e.layer);\n",
       "            });\n",
       "            \n",
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       "    \n",
       "            L.Control.geocoder(\n",
       "                {&quot;collapsed&quot;: true, &quot;defaultMarkGeocode&quot;: true, &quot;position&quot;: &quot;topleft&quot;}\n",
       "            ).on(&#x27;markgeocode&#x27;, function(e) {\n",
       "                map_ce62c2191cec3afc7c0aadccc7a33adb.setView(e.geocode.center, 11);\n",
       "            }).addTo(map_ce62c2191cec3afc7c0aadccc7a33adb);\n",
       "\n",
       "        \n",
       "    \n",
       "            map_ce62c2191cec3afc7c0aadccc7a33adb.fitBounds(\n",
       "                [[20, 0], [20, 0]],\n",
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       "        \n",
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       "                &quot;https://mt1.google.com/vt/lyrs=s\\u0026x={x}\\u0026y={y}\\u0026z={z}&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Google&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 22, &quot;maxZoom&quot;: 22, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_ce62c2191cec3afc7c0aadccc7a33adb);\n",
       "        \n",
       "    \n",
       "            var tile_layer_96fcf1b25b0560f4d5ec6ec861519e8b = L.tileLayer(\n",
       "                &quot;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/f59363488eaef46f975c8780eff564c8-560486906a3b1b1cdb1ae822a0ed89de/tiles/{z}/{x}/{y}&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Google Earth Engine&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 24, &quot;maxZoom&quot;: 24, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1.0, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_ce62c2191cec3afc7c0aadccc7a33adb);\n",
       "        \n",
       "    \n",
       "            var tile_layer_abfdc3e60a65159b10b8542e73c7a9b6 = L.tileLayer(\n",
       "                &quot;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/9bfd15b8ecebf2291127951d1a881399-d1368312b6d6cb1a5c9ff9633ca0bd64/tiles/{z}/{x}/{y}&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Google Earth Engine&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 24, &quot;maxZoom&quot;: 24, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1.0, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_ce62c2191cec3afc7c0aadccc7a33adb);\n",
       "        \n",
       "    \n",
       "            map_ce62c2191cec3afc7c0aadccc7a33adb.fitBounds(\n",
       "                [[-13.923483838824028, 33.13368265225866], [-13.879554910380351, 33.178873699572534]],\n",
       "                {}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var layer_control_0b005decfd9643e5fbed4f7aee947afb = {\n",
       "                base_layers : {\n",
       "                    &quot;openstreetmap&quot; : tile_layer_46d1bee47d397bc71759f82485b997cf,\n",
       "                },\n",
       "                overlays :  {\n",
       "                    &quot;Google Maps&quot; : tile_layer_30ca90f103292ba4c1de47a173d0414f,\n",
       "                    &quot;Google Satellite&quot; : tile_layer_1f5dbfc947b5b3465287d809b55704bc,\n",
       "                    &quot;Malawi crop probability - 2019&quot; : tile_layer_96fcf1b25b0560f4d5ec6ec861519e8b,\n",
       "                    &quot;Malawi maize probability - 2019&quot; : tile_layer_abfdc3e60a65159b10b8542e73c7a9b6,\n",
       "                },\n",
       "            };\n",
       "            L.control.layers(\n",
       "                layer_control_0b005decfd9643e5fbed4f7aee947afb.base_layers,\n",
       "                layer_control_0b005decfd9643e5fbed4f7aee947afb.overlays,\n",
       "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
       "            ).addTo(map_ce62c2191cec3afc7c0aadccc7a33adb);\n",
       "        \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<geemap.foliumap.Map at 0x7fd44cc6a890>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are unable to share proprietary code for generating crop type maps using a trained ML model\n",
    "# However we are demonstrating the end result for the same here\n",
    "# We have a cropland map (crop vs non-crop), and a maizeland map (maize vs non-maize) for 2019\n",
    "\n",
    "crop = ee.Image('users/shruti/cega_geo4dev/cropland_2019')\n",
    "maize = ee.Image('users/shruti/cega_geo4dev/maizeland_2019')\n",
    "\n",
    "Map = geemap.foliumap.Map()\n",
    "Map.setOptions('SATELLITE');\n",
    "Map.addLayer(crop, {'min':0, 'max':100, 'palette':[\"#EDF8E9\", \"#BAE4B3\", \"#74C476\", \"#31A354\", \"#006D2C\"]}, \n",
    "             'Malawi crop probability - 2019');\n",
    "Map.addLayer(maize, {'min':0, 'max':100, 'palette':[\"#FFFFD4\", \"#FED98E\", \"#FE9929\", \"#D95F0E\", \"#993404\"]}, \n",
    "             'Malawi maize probability - 2019');\n",
    "\n",
    "Map.centerObject(maize)\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8df95154",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;head&gt;    \n",
       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
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       "        &lt;script&gt;\n",
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       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 24, &quot;maxZoom&quot;: 24, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
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       "            ).addTo(map_26feaa64c079892a9abc2acb1f5e255e);\n",
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       "            var options = {\n",
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       "            map_26feaa64c079892a9abc2acb1f5e255e.on(L.Draw.Event.CREATED, function(e) {\n",
       "                var layer = e.layer,\n",
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       "                drawnItems.addLayer(e.layer);\n",
       "            });\n",
       "            \n",
       "        \n",
       "    \n",
       "            L.Control.geocoder(\n",
       "                {&quot;collapsed&quot;: true, &quot;defaultMarkGeocode&quot;: true, &quot;position&quot;: &quot;topleft&quot;}\n",
       "            ).on(&#x27;markgeocode&#x27;, function(e) {\n",
       "                map_26feaa64c079892a9abc2acb1f5e255e.setView(e.geocode.center, 11);\n",
       "            }).addTo(map_26feaa64c079892a9abc2acb1f5e255e);\n",
       "\n",
       "        \n",
       "    \n",
       "            map_26feaa64c079892a9abc2acb1f5e255e.fitBounds(\n",
       "                [[20, 0], [20, 0]],\n",
       "                {&quot;maxZoom&quot;: 2}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var tile_layer_1f5dbfc947b5b3465287d809b55704bc = L.tileLayer(\n",
       "                &quot;https://mt1.google.com/vt/lyrs=s\\u0026x={x}\\u0026y={y}\\u0026z={z}&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Google&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 22, &quot;maxZoom&quot;: 22, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_26feaa64c079892a9abc2acb1f5e255e);\n",
       "        \n",
       "    \n",
       "            var tile_layer_5b4b592bb84301aa9095e8d6a7a66a3b = L.tileLayer(\n",
       "                &quot;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/ac144d13baf59b98c899c28a7066d832-c4df7f8cebc2e888610fcd49b0ee056c/tiles/{z}/{x}/{y}&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Google Earth Engine&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 24, &quot;maxZoom&quot;: 24, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1.0, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_26feaa64c079892a9abc2acb1f5e255e);\n",
       "        \n",
       "    \n",
       "            map_26feaa64c079892a9abc2acb1f5e255e.fitBounds(\n",
       "                [[-13.923483838824028, 33.13368265225866], [-13.879554910380351, 33.178873699572534]],\n",
       "                {}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var layer_control_c8072fde0a26db7412f8068d561f2a6b = {\n",
       "                base_layers : {\n",
       "                    &quot;openstreetmap&quot; : tile_layer_02ff08fd245e1b6936ffbc9fc2ed167b,\n",
       "                },\n",
       "                overlays :  {\n",
       "                    &quot;Google Maps&quot; : tile_layer_30ca90f103292ba4c1de47a173d0414f,\n",
       "                    &quot;Google Satellite&quot; : tile_layer_1f5dbfc947b5b3465287d809b55704bc,\n",
       "                    &quot;Maizeland maize mask 2019&quot; : tile_layer_5b4b592bb84301aa9095e8d6a7a66a3b,\n",
       "                },\n",
       "            };\n",
       "            L.control.layers(\n",
       "                layer_control_c8072fde0a26db7412f8068d561f2a6b.base_layers,\n",
       "                layer_control_c8072fde0a26db7412f8068d561f2a6b.overlays,\n",
       "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
       "            ).addTo(map_26feaa64c079892a9abc2acb1f5e255e);\n",
       "        \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<geemap.foliumap.Map at 0x7fd44cc06e50>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also produce a map by applying a probability threshold on both these maps\n",
    "crop_thresh = 50\n",
    "maize_thresh = 50\n",
    "\n",
    "Map = geemap.foliumap.Map()\n",
    "Map.setOptions('SATELLITE');\n",
    "\n",
    "Map.addLayer(maize.updateMask(crop.gte(crop_thresh)).updateMask(maize.gte(maize_thresh)), \n",
    "             {'min':0, 'max':100, 'palette':[\"#FFFFD4\", \"#FED98E\", \"#FE9929\", \"#D95F0E\", \"#993404\"]}, \n",
    "             'Maizeland maize mask 2019');\n",
    "\n",
    "Map.centerObject(maize)\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0628366c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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