LFMC_Sentinel2_export_netcdf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the following script contains portions of code used by Digital Earth Australia in their tutorials:\n",
    "# https://docs.dea.ga.gov.au/notebooks/Scientific_workflows/TSmask/TSmask.html, \n",
    "# under Apache License, Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0)\n",
    "# DEA data is under Creative Commons by Attribution 4.0 license \n",
    "# (https://creativecommons.org/licenses/by/4.0/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import datacube\n",
    "import xarray as xr\n",
    "from datacube.utils.masking import make_mask\n",
    "from datacube.drivers.netcdf import write_dataset_to_netcdf\n",
    "import numpy as np\n",
    "from numpy import inf\n",
    "#sys.path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download and load the Random Forest model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pickle5\n",
      "  Using cached pickle5-0.0.11-cp38-cp38-linux_x86_64.whl\n",
      "Installing collected packages: pickle5\n",
      "Successfully installed pickle5-0.0.11\n",
      "\u001b[33mWARNING: You are using pip version 22.0.2; however, version 22.1.2 is available.\n",
      "You should consider upgrading via the '/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip3 install pickle5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/env/lib/python3.8/site-packages/sklearn/base.py:324: UserWarning: Trying to unpickle estimator DecisionTreeRegressor from version 0.23.2 when using version 1.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(criterion='mse', max_depth=25, n_estimators=25, n_jobs=8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/env/lib/python3.8/site-packages/sklearn/base.py:324: UserWarning: Trying to unpickle estimator RandomForestRegressor from version 0.23.2 when using version 1.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pickle5 as pickle\n",
    "with open('rf_fmc.pickle', 'rb') as handle:\n",
    "    rf = pickle.load(handle)\n",
    "    print(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load DEA data for region of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc = datacube.Datacube(app='fmc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>dtype</th>\n",
       "      <th>units</th>\n",
       "      <th>nodata</th>\n",
       "      <th>aliases</th>\n",
       "      <th>flags_definition</th>\n",
       "      <th>spectral_definition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>azimuthal_exiting</th>\n",
       "      <td>azimuthal_exiting</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[azimuthal_exiting]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>azimuthal_incident</th>\n",
       "      <td>azimuthal_incident</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[azimuthal_incident]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>exiting</th>\n",
       "      <td>exiting</td>\n",
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       "      <td>[exiting]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>incident</th>\n",
       "      <td>incident</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[incident]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_azimuth</th>\n",
       "      <td>relative_azimuth</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[relative_azimuth]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_slope</th>\n",
       "      <td>relative_slope</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[relative_slope]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satellite_azimuth</th>\n",
       "      <td>satellite_azimuth</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[satellite_azimuth]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satellite_view</th>\n",
       "      <td>satellite_view</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[satellite_view]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solar_azimuth</th>\n",
       "      <td>solar_azimuth</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[solar_azimuth]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solar_zenith</th>\n",
       "      <td>solar_zenith</td>\n",
       "      <td>float32</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[solar_zenith]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terrain_shadow</th>\n",
       "      <td>terrain_shadow</td>\n",
       "      <td>uint8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[terrain_shadow]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fmask</th>\n",
       "      <td>fmask</td>\n",
       "      <td>uint8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[mask, Fmask]</td>\n",
       "      <td>{'fmask': {'bits': [0, 1, 2, 3, 4, 5, 6, 7], '...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_contiguity</th>\n",
       "      <td>nbart_contiguity</td>\n",
       "      <td>uint8</td>\n",
       "      <td>1</td>\n",
       "      <td>255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'contiguity': {'bits': [0, 1, 2, 3, 4, 5, 6, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_coastal_aerosol</th>\n",
       "      <td>nbart_coastal_aerosol</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_01, nbart_B01, nbart_Band1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.015297, 0.067133, 0.19593, 0.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_blue</th>\n",
       "      <td>nbart_blue</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_02, nbart_B02, nbart_Band2]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.001206, 0.00204, 0.002623, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_green</th>\n",
       "      <td>nbart_green</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_03, nbart_B03, nbart_Band3]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.00084, 0.016372, 0.037688, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_red</th>\n",
       "      <td>nbart_red</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_04, nbart_B04, nbart_Band4]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.002584, 0.034529, 0.14997, 0.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_red_edge_1</th>\n",
       "      <td>nbart_red_edge_1</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_05, nbart_B05, nbart_Band5]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.001187, 0.04126, 0.167712, 0.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_red_edge_2</th>\n",
       "      <td>nbart_red_edge_2</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_06, nbart_B06, nbart_Band6]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.005331, 0.085006, 0.345714, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_red_edge_3</th>\n",
       "      <td>nbart_red_edge_3</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_07, nbart_B07, nbart_Band7]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.001595, 0.014731, 0.067032, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_nir_1</th>\n",
       "      <td>nbart_nir_1</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_08, nbart_B08, nbart_Band8]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.000451, 0.007614, 0.019072, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_nir_2</th>\n",
       "      <td>nbart_nir_2</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_8A, nbart_B8A, nbart_Band8A]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.001651, 0.013242, 0.02471, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_swir_2</th>\n",
       "      <td>nbart_swir_2</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_11, nbart_B11, nbart_Band11]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [7e-06, 7e-06, 8e-06, 2.8e-05, 3....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbart_swir_3</th>\n",
       "      <td>nbart_swir_3</td>\n",
       "      <td>int16</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>[nbart_band_12, nbart_B12, nbart_Band12]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'response': [0.000639, 0.001023, 0.002885, 0....</td>\n",
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      ],
      "text/plain": [
       "                                        name    dtype units nodata  \\\n",
       "measurement                                                          \n",
       "azimuthal_exiting          azimuthal_exiting  float32     1   -999   \n",
       "azimuthal_incident        azimuthal_incident  float32     1   -999   \n",
       "exiting                              exiting  float32     1   -999   \n",
       "incident                            incident  float32     1   -999   \n",
       "relative_azimuth            relative_azimuth  float32     1   -999   \n",
       "relative_slope                relative_slope  float32     1   -999   \n",
       "satellite_azimuth          satellite_azimuth  float32     1   -999   \n",
       "satellite_view                satellite_view  float32     1   -999   \n",
       "solar_azimuth                  solar_azimuth  float32     1   -999   \n",
       "solar_zenith                    solar_zenith  float32     1   -999   \n",
       "terrain_shadow                terrain_shadow    uint8     1      0   \n",
       "fmask                                  fmask    uint8     1      0   \n",
       "nbart_contiguity            nbart_contiguity    uint8     1    255   \n",
       "nbart_coastal_aerosol  nbart_coastal_aerosol    int16     1   -999   \n",
       "nbart_blue                        nbart_blue    int16     1   -999   \n",
       "nbart_green                      nbart_green    int16     1   -999   \n",
       "nbart_red                          nbart_red    int16     1   -999   \n",
       "nbart_red_edge_1            nbart_red_edge_1    int16     1   -999   \n",
       "nbart_red_edge_2            nbart_red_edge_2    int16     1   -999   \n",
       "nbart_red_edge_3            nbart_red_edge_3    int16     1   -999   \n",
       "nbart_nir_1                      nbart_nir_1    int16     1   -999   \n",
       "nbart_nir_2                      nbart_nir_2    int16     1   -999   \n",
       "nbart_swir_2                    nbart_swir_2    int16     1   -999   \n",
       "nbart_swir_3                    nbart_swir_3    int16     1   -999   \n",
       "\n",
       "                                                        aliases  \\\n",
       "measurement                                                       \n",
       "azimuthal_exiting                           [azimuthal_exiting]   \n",
       "azimuthal_incident                         [azimuthal_incident]   \n",
       "exiting                                               [exiting]   \n",
       "incident                                             [incident]   \n",
       "relative_azimuth                             [relative_azimuth]   \n",
       "relative_slope                                 [relative_slope]   \n",
       "satellite_azimuth                           [satellite_azimuth]   \n",
       "satellite_view                                 [satellite_view]   \n",
       "solar_azimuth                                   [solar_azimuth]   \n",
       "solar_zenith                                     [solar_zenith]   \n",
       "terrain_shadow                                 [terrain_shadow]   \n",
       "fmask                                             [mask, Fmask]   \n",
       "nbart_contiguity                                            NaN   \n",
       "nbart_coastal_aerosol   [nbart_band_01, nbart_B01, nbart_Band1]   \n",
       "nbart_blue              [nbart_band_02, nbart_B02, nbart_Band2]   \n",
       "nbart_green             [nbart_band_03, nbart_B03, nbart_Band3]   \n",
       "nbart_red               [nbart_band_04, nbart_B04, nbart_Band4]   \n",
       "nbart_red_edge_1        [nbart_band_05, nbart_B05, nbart_Band5]   \n",
       "nbart_red_edge_2        [nbart_band_06, nbart_B06, nbart_Band6]   \n",
       "nbart_red_edge_3        [nbart_band_07, nbart_B07, nbart_Band7]   \n",
       "nbart_nir_1             [nbart_band_08, nbart_B08, nbart_Band8]   \n",
       "nbart_nir_2            [nbart_band_8A, nbart_B8A, nbart_Band8A]   \n",
       "nbart_swir_2           [nbart_band_11, nbart_B11, nbart_Band11]   \n",
       "nbart_swir_3           [nbart_band_12, nbart_B12, nbart_Band12]   \n",
       "\n",
       "                                                        flags_definition  \\\n",
       "measurement                                                                \n",
       "azimuthal_exiting                                                    NaN   \n",
       "azimuthal_incident                                                   NaN   \n",
       "exiting                                                              NaN   \n",
       "incident                                                             NaN   \n",
       "relative_azimuth                                                     NaN   \n",
       "relative_slope                                                       NaN   \n",
       "satellite_azimuth                                                    NaN   \n",
       "satellite_view                                                       NaN   \n",
       "solar_azimuth                                                        NaN   \n",
       "solar_zenith                                                         NaN   \n",
       "terrain_shadow                                                       NaN   \n",
       "fmask                  {'fmask': {'bits': [0, 1, 2, 3, 4, 5, 6, 7], '...   \n",
       "nbart_contiguity       {'contiguity': {'bits': [0, 1, 2, 3, 4, 5, 6, ...   \n",
       "nbart_coastal_aerosol                                                NaN   \n",
       "nbart_blue                                                           NaN   \n",
       "nbart_green                                                          NaN   \n",
       "nbart_red                                                            NaN   \n",
       "nbart_red_edge_1                                                     NaN   \n",
       "nbart_red_edge_2                                                     NaN   \n",
       "nbart_red_edge_3                                                     NaN   \n",
       "nbart_nir_1                                                          NaN   \n",
       "nbart_nir_2                                                          NaN   \n",
       "nbart_swir_2                                                         NaN   \n",
       "nbart_swir_3                                                         NaN   \n",
       "\n",
       "                                                     spectral_definition  \n",
       "measurement                                                               \n",
       "azimuthal_exiting                                                    NaN  \n",
       "azimuthal_incident                                                   NaN  \n",
       "exiting                                                              NaN  \n",
       "incident                                                             NaN  \n",
       "relative_azimuth                                                     NaN  \n",
       "relative_slope                                                       NaN  \n",
       "satellite_azimuth                                                    NaN  \n",
       "satellite_view                                                       NaN  \n",
       "solar_azimuth                                                        NaN  \n",
       "solar_zenith                                                         NaN  \n",
       "terrain_shadow                                                       NaN  \n",
       "fmask                                                                NaN  \n",
       "nbart_contiguity                                                     NaN  \n",
       "nbart_coastal_aerosol  {'response': [0.015297, 0.067133, 0.19593, 0.3...  \n",
       "nbart_blue             {'response': [0.001206, 0.00204, 0.002623, 0.0...  \n",
       "nbart_green            {'response': [0.00084, 0.016372, 0.037688, 0.0...  \n",
       "nbart_red              {'response': [0.002584, 0.034529, 0.14997, 0.4...  \n",
       "nbart_red_edge_1       {'response': [0.001187, 0.04126, 0.167712, 0.4...  \n",
       "nbart_red_edge_2       {'response': [0.005331, 0.085006, 0.345714, 0....  \n",
       "nbart_red_edge_3       {'response': [0.001595, 0.014731, 0.067032, 0....  \n",
       "nbart_nir_1            {'response': [0.000451, 0.007614, 0.019072, 0....  \n",
       "nbart_nir_2            {'response': [0.001651, 0.013242, 0.02471, 0.0...  \n",
       "nbart_swir_2           {'response': [7e-06, 7e-06, 8e-06, 2.8e-05, 3....  \n",
       "nbart_swir_3           {'response': [0.000639, 0.001023, 0.002885, 0....  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check variables names\n",
    "product = \"s2a_ard_granule\"\n",
    "measurements = dc.list_measurements()\n",
    "measurements.loc[product]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check variables names\n",
    "#product = \"s2b_ard_granule\"\n",
    "#measurements = dc.list_measurements()\n",
    "#measurements.loc[product]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load sentinel sentinel-2a, and sentinel-2b  and merge them\n",
    "query = {\n",
    "        'y': (-34.600, -35.100),\n",
    "        'x': (138.500, 138.900),\n",
    "        'crs': 'EPSG:4326',\n",
    "        'output_crs': 'EPSG:4326',\n",
    "        'resolution': (-0.0002, 0.0002),  #some bands are 10m resolution, but others are 20m\n",
    "        'time': ('2022-04-01', '2022-04-10'),# '2022-06-01'),\n",
    "        'measurements':[\"fmask\",\n",
    "                         \"nbart_blue\",\"nbart_green\",\"nbart_red\",\n",
    "                         \"nbart_red_edge_1\",\"nbart_red_edge_2\",\"nbart_red_edge_3\",\n",
    "                         \"nbart_nir_1\",\"nbart_nir_2\",\n",
    "                         \"nbart_swir_2\",\"nbart_swir_3\"], }\n",
    "\n",
    "\n",
    "s2a_ds = dc.load(product='s2a_ard_granule', group_by='solar_day', **query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2b_ds = dc.load(product='s2b_ard_granule', group_by='solar_day', **query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_nomask = xr.concat([s2a_ds, s2b_ds], dim='time').sortby('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:           (time: 4, latitude: 2500, longitude: 2000)\n",
      "Coordinates:\n",
      "  * time              (time) datetime64[ns] 2022-04-01T00:46:48.272966 ... 20...\n",
      "  * latitude          (latitude) float64 -34.6 -34.6 -34.6 ... -35.1 -35.1 -35.1\n",
      "  * longitude         (longitude) float64 138.5 138.5 138.5 ... 138.9 138.9\n",
      "    spatial_ref       int32 4326\n",
      "Data variables:\n",
      "    fmask             (time, latitude, longitude) uint8 1 1 1 1 1 ... 1 1 1 1 1\n",
      "    nbart_blue        (time, latitude, longitude) int16 836 844 835 ... 826 704\n",
      "    nbart_green       (time, latitude, longitude) int16 1112 1117 ... 1104 1047\n",
      "    nbart_red         (time, latitude, longitude) int16 1654 1653 ... 1564 1410\n",
      "    nbart_red_edge_1  (time, latitude, longitude) int16 1923 1907 ... 1838 1824\n",
      "    nbart_red_edge_2  (time, latitude, longitude) int16 2095 2085 ... 2165 2266\n",
      "    nbart_red_edge_3  (time, latitude, longitude) int16 2236 2182 ... 2290 2362\n",
      "    nbart_nir_1       (time, latitude, longitude) int16 2433 2491 ... 2554 2751\n",
      "    nbart_nir_2       (time, latitude, longitude) int16 2600 2556 ... 2749 2785\n",
      "    nbart_swir_2      (time, latitude, longitude) int16 3783 3827 ... 4293 4116\n",
      "    nbart_swir_3      (time, latitude, longitude) int16 2922 2996 ... 3043 2776\n",
      "Attributes:\n",
      "    crs:           EPSG:4326\n",
      "    grid_mapping:  spatial_ref\n",
      "['2022-04-01T00:46:48.272966000' '2022-04-04T00:56:42.200118000'\n",
      " '2022-04-06T00:46:42.958246000' '2022-04-09T00:56:37.454774000']\n"
     ]
    }
   ],
   "source": [
    "print(ds_nomask)\n",
    "print(ds_nomask.time.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add NDVI and NDII normalised indices to the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_nomask['ndvi']=((ds_nomask.nbart_nir_1-ds_nomask.nbart_red)/(ds_nomask.nbart_nir_1+ds_nomask.nbart_red))\n",
    "ds_nomask['ndii']=((ds_nomask.nbart_nir_1-ds_nomask.nbart_swir_2)/(ds_nomask.nbart_nir_1+ds_nomask.nbart_swir_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save dataset as netCDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_dataset_to_netcdf(ds_nomask, 's2_reflectance_no_mask.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read fmask flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fmask': {'bits': [0, 1, 2, 3, 4, 5, 6, 7],\n",
       "  'values': {'0': 'nodata',\n",
       "   '1': 'valid',\n",
       "   '2': 'cloud',\n",
       "   '3': 'shadow',\n",
       "   '4': 'snow',\n",
       "   '5': 'water'},\n",
       "  'description': 'Fmask'}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print sentinel 2 fmask flags (code from https://docs.dea.ga.gov.au/notebooks/Scientific_workflows/TSmask/TSmask.html,Apache License, Version 2.0 https://www.apache.org/licenses/LICENSE-2.0)\n",
    "ds_nomask.fmask.flags_definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise RGB images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('s2_reflectance_no_mask.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i in range(ds.time.data.size):\n",
    "#    ds_ = ds.isel(time=i)\n",
    "#    ds_[['nbart_red', 'nbart_green', 'nbart_blue']].to_array().plot.imshow(robust=True, figsize=(8,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open reflectance dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (time: 4, latitude: 2500, longitude: 2000)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 2022-04-01T00:46:48 ... 2022-04-0...\n",
       "  * latitude          (latitude) float64 -34.6 -34.6 -34.6 ... -35.1 -35.1 -35.1\n",
       "  * longitude         (longitude) float64 138.5 138.5 138.5 ... 138.9 138.9\n",
       "    spatial_ref       int32 -2147483647\n",
       "Data variables: (12/13)\n",
       "    fmask             (time, latitude, longitude) float32 ...\n",
       "    nbart_blue        (time, latitude, longitude) float32 ...\n",
       "    nbart_green       (time, latitude, longitude) float32 ...\n",
       "    nbart_red         (time, latitude, longitude) float32 ...\n",
       "    nbart_red_edge_1  (time, latitude, longitude) float32 ...\n",
       "    nbart_red_edge_2  (time, latitude, longitude) float32 ...\n",
       "    ...                ...\n",
       "    nbart_nir_1       (time, latitude, longitude) float32 ...\n",
       "    nbart_nir_2       (time, latitude, longitude) float32 ...\n",
       "    nbart_swir_2      (time, latitude, longitude) float32 ...\n",
       "    nbart_swir_3      (time, latitude, longitude) float32 ...\n",
       "    ndvi              (time, latitude, longitude) float64 ...\n",
       "    ndii              (time, latitude, longitude) float64 ...\n",
       "Attributes:\n",
       "    date_created:           2022-06-07T22:33:56.064279\n",
       "    Conventions:            CF-1.6, ACDD-1.3\n",
       "    history:                NetCDF-CF file created by datacube version &#x27;1.8.6...\n",
       "    geospatial_bounds:      POLYGON ((138.5 -34.6, 138.5 -35.10000000000001, ...\n",
       "    geospatial_bounds_crs:  EPSG:4326\n",
       "    geospatial_lat_min:     -35.10000000000001\n",
       "    geospatial_lat_max:     -34.6\n",
       "    geospatial_lat_units:   degrees_north\n",
       "    geospatial_lon_min:     138.5\n",
       "    geospatial_lon_max:     138.9\n",
       "    geospatial_lon_units:   degrees_east</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2c0a0a0c-c6c7-43e4-9e96-351d363c9d0c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2c0a0a0c-c6c7-43e4-9e96-351d363c9d0c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>latitude</span>: 2500</li><li><span class='xr-has-index'>longitude</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-14ce6011-6702-4783-bc13-b9e994a1625b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14ce6011-6702-4783-bc13-b9e994a1625b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-04-01T00:46:48 ... 2022-04-...</div><input id='attrs-023bcec2-1e5b-4932-b569-7043119f5530' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-023bcec2-1e5b-4932-b569-7043119f5530' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cb1a0de-7860-433b-8e8f-49b08618e21e' class='xr-var-data-in' type='checkbox'><label for='data-8cb1a0de-7860-433b-8e8f-49b08618e21e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time, unix time-stamp</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-04-01T00:46:48.000000000&#x27;, &#x27;2022-04-04T00:56:42.000000000&#x27;,\n",
       "       &#x27;2022-04-06T00:46:42.000000000&#x27;, &#x27;2022-04-09T00:56:37.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-34.6 -34.6 -34.6 ... -35.1 -35.1</div><input id='attrs-fe0ad91a-d90b-4f97-bd97-6c273201c591' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe0ad91a-d90b-4f97-bd97-6c273201c591' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-826d384e-50db-41c0-acdb-7291a92cad2a' class='xr-var-data-in' type='checkbox'><label for='data-826d384e-50db-41c0-acdb-7291a92cad2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-34.6001, -34.6003, -34.6005, ..., -35.0995, -35.0997, -35.0999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>138.5 138.5 138.5 ... 138.9 138.9</div><input id='attrs-893e612c-e63d-4fbe-9b5f-6291b6b4387a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-893e612c-e63d-4fbe-9b5f-6291b6b4387a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72201694-9193-4d39-a864-dcf7fd86bb20' class='xr-var-data-in' type='checkbox'><label for='data-72201694-9193-4d39-a864-dcf7fd86bb20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([138.5001, 138.5003, 138.5005, ..., 138.8995, 138.8997, 138.8999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6b2ae6b-900a-4ba4-a5c8-38fab3049623' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6b2ae6b-900a-4ba4-a5c8-38fab3049623' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6b6037d-dd8e-4dd6-8aec-eac88aadbf99' class='xr-var-data-in' type='checkbox'><label for='data-a6b6037d-dd8e-4dd6-8aec-eac88aadbf99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>[ 1.385e+02  2.000e-04  0.000e+00 -3.460e+01  0.000e+00 -2.000e-04]</dd></dl></div><div class='xr-var-data'><pre>array(-2147483647, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6137111-8c2b-4701-970b-20c99db6b333' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a6137111-8c2b-4701-970b-20c99db6b333' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>fmask</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3b45c084-de15-4ee6-ae7f-f5649740f2b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b45c084-de15-4ee6-ae7f-f5649740f2b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a48320e9-297d-4f65-a817-c324640777a8' class='xr-var-data-in' type='checkbox'><label for='data-a48320e9-297d-4f65-a817-c324640777a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_blue</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d5438d8-3a57-499e-8ad4-57d0b9015e67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d5438d8-3a57-499e-8ad4-57d0b9015e67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51a74b19-ca0c-41d3-8cc2-eeee73082ed9' class='xr-var-data-in' type='checkbox'><label for='data-51a74b19-ca0c-41d3-8cc2-eeee73082ed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_green</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-12bbc630-d4dc-4908-a67e-7ddd7dac1e2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12bbc630-d4dc-4908-a67e-7ddd7dac1e2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c514ac0-2698-48fe-831e-ad4c6c0c8382' class='xr-var-data-in' type='checkbox'><label for='data-0c514ac0-2698-48fe-831e-ad4c6c0c8382' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_red</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-216c0601-259b-4df4-9349-12538c5119b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-216c0601-259b-4df4-9349-12538c5119b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c6a3688-b6cf-4b7d-89ee-04fbf9b49e24' class='xr-var-data-in' type='checkbox'><label for='data-6c6a3688-b6cf-4b7d-89ee-04fbf9b49e24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_red_edge_1</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed91ffde-a6e6-41fd-947d-3a43f575cd65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed91ffde-a6e6-41fd-947d-3a43f575cd65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e45d60b9-f225-4b0c-832d-105d56cb2bc4' class='xr-var-data-in' type='checkbox'><label for='data-e45d60b9-f225-4b0c-832d-105d56cb2bc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_red_edge_2</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40dbcf6e-baa9-49ea-88ec-0885c7ca5de7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40dbcf6e-baa9-49ea-88ec-0885c7ca5de7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-813df5bd-f9b4-474a-a453-4fa28eabd905' class='xr-var-data-in' type='checkbox'><label for='data-813df5bd-f9b4-474a-a453-4fa28eabd905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_red_edge_3</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d8ce7082-46e3-46d5-b053-01d7df3dcb5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8ce7082-46e3-46d5-b053-01d7df3dcb5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44ed072f-bc87-4592-bb72-1b37dd078869' class='xr-var-data-in' type='checkbox'><label for='data-44ed072f-bc87-4592-bb72-1b37dd078869' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_nir_1</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0aa1e21-d040-4ba6-a07d-0cbde1c2b32b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0aa1e21-d040-4ba6-a07d-0cbde1c2b32b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aced192-fe42-4419-8387-f8f753a433ba' class='xr-var-data-in' type='checkbox'><label for='data-3aced192-fe42-4419-8387-f8f753a433ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_nir_2</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-33093d60-5900-44a5-a453-822dea331657' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33093d60-5900-44a5-a453-822dea331657' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51f81d37-2fd9-4d99-a731-17b0e9bf6793' class='xr-var-data-in' type='checkbox'><label for='data-51f81d37-2fd9-4d99-a731-17b0e9bf6793' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_swir_2</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b099cb2e-fd99-4b77-887b-801f186930c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b099cb2e-fd99-4b77-887b-801f186930c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5fd73fc-a63b-4176-85bb-268c151014ab' class='xr-var-data-in' type='checkbox'><label for='data-b5fd73fc-a63b-4176-85bb-268c151014ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_swir_3</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-617c6745-83dd-4add-ba71-e986d27e9a3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-617c6745-83dd-4add-ba71-e986d27e9a3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb106abf-7e3f-49b6-a270-c8429f9876ad' class='xr-var-data-in' type='checkbox'><label for='data-cb106abf-7e3f-49b6-a270-c8429f9876ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndvi</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06f335db-dbec-419d-8866-c0476b17c105' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06f335db-dbec-419d-8866-c0476b17c105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4662a856-c81d-48b8-816b-e53fb89d678c' class='xr-var-data-in' type='checkbox'><label for='data-4662a856-c81d-48b8-816b-e53fb89d678c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndii</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-617192fe-b2af-45f9-919c-28b4d934dde9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-617192fe-b2af-45f9-919c-28b4d934dde9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95707e6a-6434-42ab-bbdc-3ba97d5e0a48' class='xr-var-data-in' type='checkbox'><label for='data-95707e6a-6434-42ab-bbdc-3ba97d5e0a48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>[20000000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5443365c-1281-4a9b-b114-a99a9cf0c828' class='xr-section-summary-in' type='checkbox'  ><label for='section-5443365c-1281-4a9b-b114-a99a9cf0c828' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>date_created :</span></dt><dd>2022-06-07T22:33:56.064279</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>history :</span></dt><dd>NetCDF-CF file created by datacube version &#x27;1.8.6&#x27; at 20220607.</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON ((138.5 -34.6, 138.5 -35.10000000000001, 138.9 -35.10000000000001, 138.9 -34.6, 138.5 -34.6))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_min :</span></dt><dd>-35.10000000000001</dd><dt><span>geospatial_lat_max :</span></dt><dd>-34.6</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_min :</span></dt><dd>138.5</dd><dt><span>geospatial_lon_max :</span></dt><dd>138.9</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (time: 4, latitude: 2500, longitude: 2000)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 2022-04-01T00:46:48 ... 2022-04-0...\n",
       "  * latitude          (latitude) float64 -34.6 -34.6 -34.6 ... -35.1 -35.1 -35.1\n",
       "  * longitude         (longitude) float64 138.5 138.5 138.5 ... 138.9 138.9\n",
       "    spatial_ref       int32 ...\n",
       "Data variables: (12/13)\n",
       "    fmask             (time, latitude, longitude) float32 ...\n",
       "    nbart_blue        (time, latitude, longitude) float32 ...\n",
       "    nbart_green       (time, latitude, longitude) float32 ...\n",
       "    nbart_red         (time, latitude, longitude) float32 ...\n",
       "    nbart_red_edge_1  (time, latitude, longitude) float32 ...\n",
       "    nbart_red_edge_2  (time, latitude, longitude) float32 ...\n",
       "    ...                ...\n",
       "    nbart_nir_1       (time, latitude, longitude) float32 ...\n",
       "    nbart_nir_2       (time, latitude, longitude) float32 ...\n",
       "    nbart_swir_2      (time, latitude, longitude) float32 ...\n",
       "    nbart_swir_3      (time, latitude, longitude) float32 ...\n",
       "    ndvi              (time, latitude, longitude) float64 ...\n",
       "    ndii              (time, latitude, longitude) float64 ...\n",
       "Attributes:\n",
       "    date_created:           2022-06-07T22:33:56.064279\n",
       "    Conventions:            CF-1.6, ACDD-1.3\n",
       "    history:                NetCDF-CF file created by datacube version '1.8.6...\n",
       "    geospatial_bounds:      POLYGON ((138.5 -34.6, 138.5 -35.10000000000001, ...\n",
       "    geospatial_bounds_crs:  EPSG:4326\n",
       "    geospatial_lat_min:     -35.10000000000001\n",
       "    geospatial_lat_max:     -34.6\n",
       "    geospatial_lat_units:   degrees_north\n",
       "    geospatial_lon_min:     138.5\n",
       "    geospatial_lon_max:     138.9\n",
       "    geospatial_lon_units:   degrees_east"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset('s2_reflectance_no_mask.nc')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  float: left;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 4)&gt;\n",
       "array([&#x27;2022-04-01T00:46:48.000000000&#x27;, &#x27;2022-04-04T00:56:42.000000000&#x27;,\n",
       "       &#x27;2022-04-06T00:46:42.000000000&#x27;, &#x27;2022-04-09T00:56:37.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2022-04-01T00:46:48 ... 2022-04-09T00:...\n",
       "    spatial_ref  int32 -2147483647\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    long_name:      Time, unix time-stamp\n",
       "    axis:           T</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1d4bf980-6836-43f2-a4f8-40b8de679c20' class='xr-array-in' type='checkbox' checked><label for='section-1d4bf980-6836-43f2-a4f8-40b8de679c20' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2022-04-01T00:46:48 2022-04-04T00:56:42 ... 2022-04-09T00:56:37</span></div><div class='xr-array-data'><pre>array([&#x27;2022-04-01T00:46:48.000000000&#x27;, &#x27;2022-04-04T00:56:42.000000000&#x27;,\n",
       "       &#x27;2022-04-06T00:46:42.000000000&#x27;, &#x27;2022-04-09T00:56:37.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-e81ac77c-0023-4efd-8f95-e6c5e55aad14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e81ac77c-0023-4efd-8f95-e6c5e55aad14' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-04-01T00:46:48 ... 2022-04-...</div><input id='attrs-cd484c21-9774-4404-a838-6cafccf5c483' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd484c21-9774-4404-a838-6cafccf5c483' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7bb98e6-3827-4bd8-a6d1-226f14a4045a' class='xr-var-data-in' type='checkbox'><label for='data-d7bb98e6-3827-4bd8-a6d1-226f14a4045a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time, unix time-stamp</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-04-01T00:46:48.000000000&#x27;, &#x27;2022-04-04T00:56:42.000000000&#x27;,\n",
       "       &#x27;2022-04-06T00:46:42.000000000&#x27;, &#x27;2022-04-09T00:56:37.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647</div><input id='attrs-2b2c1c79-077b-42d6-803e-81ad18a7a266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b2c1c79-077b-42d6-803e-81ad18a7a266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5dc479d-005f-4939-891e-7d622e3d22cf' class='xr-var-data-in' type='checkbox'><label for='data-b5dc479d-005f-4939-891e-7d622e3d22cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>[ 1.385e+02  2.000e-04  0.000e+00 -3.460e+01  0.000e+00 -2.000e-04]</dd></dl></div><div class='xr-var-data'><pre>array(-2147483647, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e9db448-39bb-4af9-b544-0a81bf595a6e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e9db448-39bb-4af9-b544-0a81bf595a6e' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time, unix time-stamp</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 4)>\n",
       "array(['2022-04-01T00:46:48.000000000', '2022-04-04T00:56:42.000000000',\n",
       "       '2022-04-06T00:46:42.000000000', '2022-04-09T00:56:37.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2022-04-01T00:46:48 ... 2022-04-09T00:...\n",
       "    spatial_ref  int32 -2147483647\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    long_name:      Time, unix time-stamp\n",
       "    axis:           T"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option A: Compute LFMC from 3D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stack and reshape dataset to be compatible with the RF input\n",
    "refl = ds[['ndvi','ndii','nbart_red','nbart_green','nbart_blue','nbart_nir_1','nbart_nir_2','nbart_swir_2','nbart_swir_3']].to_array().values\n",
    "\n",
    "refl_rf = refl.reshape((9,-1)).swapaxes(0,1)\n",
    "\n",
    "# Convert infinite values to nans\n",
    "refl_rf = np.nan_to_num(refl_rf, copy=False, nan=np.nan, posinf=np.nan, neginf=np.nan) \n",
    "\n",
    "nan_mask = np.isnan(refl_rf) # nan values are not accepted by the RF model. So first they will be replaced with 0 (below), so that the model can run. Then this mask will be applied to restore the nan in the LFMC map\n",
    "nan_mask_1d = np.sum(np.where(nan_mask,1,0), axis=1) # if 0, there were no nan values in any of the reflectance bands, if a pixel is > 0 then at least one of the bands in that position was nan\n",
    "\n",
    "refl_rf = np.where(nan_mask, 0, refl_rf)\n",
    "\n",
    "# Estimate FMC values using RF model and previous reflectance input\n",
    "rf_lfmc = rf.predict(refl_rf)\n",
    "\n",
    "rf_lfmc =  np.where(nan_mask_1d>0, np.nan, rf_lfmc) #if a pixel is > 0 then at least one of the bands in that position was nan, or -inf/inf \n",
    "\n",
    "lfmc_3d = rf_lfmc.reshape(refl.shape[1:])\n",
    "\n",
    "# Apply cloud, shadow, snow, water mask\n",
    "lfmc_3d = np.where(ds.fmask.data!=1, np.nan, lfmc_3d) #1:valid, the mask has value 1 if nodata, cloud shadow, snow or water, 0 if valid\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Option A) Create new netCDF for final output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4\n",
    "import pandas as pd\n",
    "\n",
    "with netCDF4.Dataset('s2_lfmc_option_A.nc', 'w', format='NETCDF4_CLASSIC') as new_ds:\n",
    "    \n",
    "    setattr(new_ds, 'crs', 'EPSG:4326')\n",
    "    setattr(new_ds, 'fmask_legend', '0:nodata, 1:valid, 2:cloud, 3:shadow, 4:snow, 5:water')\n",
    "    \n",
    "    t_dim = new_ds.createDimension('time', ds.time.size)\n",
    "    x_dim = new_ds.createDimension('longitude', ds.longitude.size)\n",
    "    y_dim = new_ds.createDimension('latitude', ds.latitude.size)\n",
    "    \n",
    "    var = new_ds.createVariable(\"time\", \"f8\", (\"time\",))\n",
    "    var.units = \"seconds since 1970-01-01 00:00:00.0\"\n",
    "    var.calendar = \"standard\"\n",
    "    var.long_name = \"Time, unix time-stamp\"\n",
    "    var.standard_name = \"time\"\n",
    "    var[:] = [netCDF4.date2num([pd.to_datetime(i)], units=\"seconds since 1970-01-01 00:00:00.0\", calendar=\"standard\") for i in ds.time.data]\n",
    "\n",
    "    var = new_ds.createVariable(\"longitude\", \"f8\", (\"longitude\",))\n",
    "    var.units = \"degrees\"\n",
    "    var.long_name = \"longitude\"\n",
    "    var.standard_name = \"longitude\"\n",
    "    var[:] = ds.longitude.data\n",
    "\n",
    "    var = new_ds.createVariable(\"latitude\", \"f8\", (\"latitude\",))\n",
    "    var.units = \"degrees\"\n",
    "    var.long_name = \"latitude\"\n",
    "    var.standard_name = \"latitude\"\n",
    "    var[:] = ds.latitude.data\n",
    "    \n",
    "    # if the LFMC maps are not already masked, it is suggested to include fmask and RGB bands in the netCDF file, in order to be able to mask and visually check the maps later\n",
    "    \n",
    "    #var = new_ds.createVariable(\"fmask\", 'i4', (\"time\", \"latitude\", \"longitude\"))\n",
    "    #var.units = ''\n",
    "    #var.long_name = \"Quality mask\"\n",
    "    #var[:] = ds.fmask.data\n",
    "    \n",
    "    #var = new_ds.createVariable(\"nbart_red\", 'f8', (\"time\", \"latitude\", \"longitude\"))\n",
    "    #var.units = ''\n",
    "    #var.long_name = \"Red band\"\n",
    "    #var[:] = ds.nbart_red.data\n",
    "    \n",
    "    #var = new_ds.createVariable(\"nbart_green\", 'f8', (\"time\", \"latitude\", \"longitude\"))\n",
    "    #var.units = ''\n",
    "    #var.long_name = \"Green band\"\n",
    "    #var[:] = ds.nbart_green.data\n",
    "    \n",
    "    #var = new_ds.createVariable(\"nbart_blue\", 'f8', (\"time\", \"latitude\", \"longitude\"))\n",
    "    #var.units = ''\n",
    "    #var.long_name = \"Blue band\"\n",
    "    #var[:] = ds.nbart_blue.data\n",
    "\n",
    "    var = new_ds.createVariable(\"lfmc\", 'f4', (\"time\", \"latitude\", \"longitude\"), fill_value=-9999.9)\n",
    "    var.units = '%'\n",
    "    var.long_name = \"Live Fuel Moisture Content estimated with Random Forest model\"\n",
    "    var[:] = lfmc_3d\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option B: Compute LFMC with loop over dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current date:  2022-04-01T00:46:48.000000000 ... Ends at:  2022-04-09T00:56:37.000000000\n",
      "Current date:  2022-04-04T00:56:42.000000000 ... Ends at:  2022-04-09T00:56:37.000000000\n",
      "Current date:  2022-04-06T00:46:42.000000000 ... Ends at:  2022-04-09T00:56:37.000000000\n",
      "Current date:  2022-04-09T00:56:37.000000000 ... Ends at:  2022-04-09T00:56:37.000000000\n"
     ]
    }
   ],
   "source": [
    "list_lfmc_arrays = list()\n",
    "\n",
    "\n",
    "for d in ds.time.data:\n",
    "    print('Current date: ', d, '... Ends at: ', ds.time.data[-1])\n",
    "    \n",
    "    ds_2d = ds.sel(time=d)\n",
    "    \n",
    "    # Stack and reshape dataset to be compatible with the RF input\n",
    "    refl = ds_2d[['ndvi','ndii','nbart_red','nbart_green','nbart_blue','nbart_nir_1','nbart_nir_2','nbart_swir_2','nbart_swir_3']].to_array().values\n",
    "    \n",
    "    # Convert infinite values to nans\n",
    "    refl = np.nan_to_num(refl, copy=False, nan= np.nan, posinf=np.nan, neginf=np.nan)\n",
    "\n",
    "    # Check and mask values not accepted by RF model\n",
    "    nan_mask = np.isnan(refl) # nan values are not accepted by the RF model. So first they will be replaced with 0 (below), so that the model can run. Then this mask will be applied to restore the nan in the LFMC map\n",
    "    nan_mask_2d = np.sum(np.where(nan_mask,1,0), axis=0) # if 0, there were no nan values in any of the reflectance bands, if a pixel is > 0 then at least one of the bands in that position was nan\n",
    "    \n",
    "    refl = np.where(nan_mask, 0, refl) # replace nan values with 0 so that the model can run\n",
    "    \n",
    "    refl_rf = refl.reshape((9,-1)).swapaxes(0,1)\n",
    "    \n",
    "    # Estimate FMC values using RF model and previous reflectance input\n",
    "    rf_lfmc = rf.predict(refl_rf)\n",
    "    lfmc = rf_lfmc.reshape(refl.shape[1:])\n",
    "    \n",
    "    lfmc = np.where(nan_mask_2d>0, np.nan, lfmc) #if a pixel is > 0 then at least one of the bands in that position was nan, or -inf/inf \n",
    "    \n",
    "    # Apply cloud, shadow, snow, water mask\n",
    "    lfmc = np.where(ds_2d.fmask.data!=1, np.nan, lfmc)  #1:valid, the mask has value 1 if nodata, cloud shadow, snow or water, 0 if valid\n",
    "    \n",
    "    list_lfmc_arrays.append(lfmc)\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Option B) Create new netCDF for final output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4\n",
    "import pandas as pd\n",
    "\n",
    "with netCDF4.Dataset('s2_lfmc_option_B.nc', 'w', format='NETCDF4_CLASSIC') as new_ds:\n",
    "    \n",
    "    setattr(new_ds, 'crs', 'EPSG:4326')\n",
    "    setattr(new_ds, 'fmask_legend', '0:nodata, 1:valid, 2:cloud, 3:shadow, 4:snow, 5:water')\n",
    "    \n",
    "    t_dim = new_ds.createDimension('time', ds.time.size)\n",
    "    x_dim = new_ds.createDimension('longitude', ds.longitude.size)\n",
    "    y_dim = new_ds.createDimension('latitude', ds.latitude.size)\n",
    "    \n",
    "    var = new_ds.createVariable(\"time\", \"f8\", (\"time\",))\n",
    "    var.units = \"seconds since 1970-01-01 00:00:00.0\"\n",
    "    var.calendar = \"standard\"\n",
    "    var.long_name = \"Time, unix time-stamp\"\n",
    "    var.standard_name = \"time\"\n",
    "    var[:] = [netCDF4.date2num([pd.to_datetime(i)], units=\"seconds since 1970-01-01 00:00:00.0\", calendar=\"standard\") for i in ds.time.data]\n",
    "\n",
    "    var = new_ds.createVariable(\"longitude\", \"f8\", (\"longitude\",))\n",
    "    var.units = \"degrees\"\n",
    "    var.long_name = \"longitude\"\n",
    "    var.standard_name = \"longitude\"\n",
    "    var[:] = ds.longitude.data\n",
    "\n",
    "    var = new_ds.createVariable(\"latitude\", \"f8\", (\"latitude\",))\n",
    "    var.units = \"degrees\"\n",
    "    var.long_name = \"latitude\"\n",
    "    var.standard_name = \"latitude\"\n",
    "    var[:] = ds.latitude.data\n",
    "    \n",
    "    # if the LFMC maps are not already masked, it is suggested to include fmask and RGB bands in the netCDF file, in order to be able to mask and visually check the maps later\n",
    "    \n",
    "    #var = new_ds.createVariable(\"fmask\", 'i4', (\"time\", \"latitude\", \"longitude\"))\n",
    "    #var.units = ''\n",
    "    #var.long_name = \"Quality mask\"\n",
    "    #var[:] = ds.fmask.data\n",
    "    \n",
    "    #var = new_ds.createVariable(\"nbart_red\", 'f8', (\"time\", \"latitude\", \"longitude\"))\n",
    "    #var.units = ''\n",
    "    #var.long_name = \"Red band\"\n",
    "    #var[:] = ds.nbart_red.data\n",
    "    \n",
    "    #var = new_ds.createVariable(\"nbart_green\", 'f8', (\"time\", \"latitude\", \"longitude\"))\n",
    "    #var.units = ''\n",
    "    #var.long_name = \"Green band\"\n",
    "    #var[:] = ds.nbart_green.data\n",
    "    \n",
    "    #var = new_ds.createVariable(\"nbart_blue\", 'f8', (\"time\", \"latitude\", \"longitude\"))\n",
    "    #var.units = ''\n",
    "    #var.long_name = \"Blue band\"\n",
    "    #var[:] = ds.nbart_blue.data\n",
    "\n",
    "    var = new_ds.createVariable(\"lfmc\", 'f4', (\"time\", \"latitude\", \"longitude\"), fill_value=-9999.9)\n",
    "    var.units = '%'\n",
    "    var.long_name = \"Live Fuel Moisture Content estimated with Random Forest model\"\n",
    "    for i in range(ds.time.size):\n",
    "        var[i,:,:] = list_lfmc_arrays[i]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create colormap consistent with the current Australian Fuel Monitoring System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (time: 22, longitude: 2000, latitude: 2500)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2022-04-01T00:46:48 ... 2022-05-24T00:...\n",
       "  * longitude    (longitude) float64 138.5 138.5 138.5 ... 138.9 138.9 138.9\n",
       "  * latitude     (latitude) float64 -34.6 -34.6 -34.6 ... -35.1 -35.1 -35.1\n",
       "Data variables:\n",
       "    fmask        (time, latitude, longitude) int32 ...\n",
       "    nbart_red    (time, latitude, longitude) float64 ...\n",
       "    nbart_green  (time, latitude, longitude) float64 ...\n",
       "    nbart_blue   (time, latitude, longitude) float64 ...\n",
       "    lfmc         (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    crs:           EPSG:4326\n",
       "    fmask_legend:  0:nodata, 1:valid, 2:cloud, 3:shadow, 4:snow, 5:water</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-734440f5-48a1-4e0b-8447-f36bfcca5887' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-734440f5-48a1-4e0b-8447-f36bfcca5887' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 22</li><li><span class='xr-has-index'>longitude</span>: 2000</li><li><span class='xr-has-index'>latitude</span>: 2500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ed4453b0-05e8-4f1e-9f8b-6963a798d44d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed4453b0-05e8-4f1e-9f8b-6963a798d44d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-04-01T00:46:48 ... 2022-05-...</div><input id='attrs-65573155-8875-4d63-b7b3-5257cdc2a48f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65573155-8875-4d63-b7b3-5257cdc2a48f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56ee211a-e3c7-4a7d-87c0-3a087eac9508' class='xr-var-data-in' type='checkbox'><label for='data-56ee211a-e3c7-4a7d-87c0-3a087eac9508' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time, unix time-stamp</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-04-01T00:46:48.000000000&#x27;, &#x27;2022-04-04T00:56:42.000000000&#x27;,\n",
       "       &#x27;2022-04-06T00:46:42.000000000&#x27;, &#x27;2022-04-09T00:56:37.000000000&#x27;,\n",
       "       &#x27;2022-04-11T00:46:47.000000000&#x27;, &#x27;2022-04-14T00:56:43.000000000&#x27;,\n",
       "       &#x27;2022-04-16T00:46:42.000000000&#x27;, &#x27;2022-04-19T00:56:36.000000000&#x27;,\n",
       "       &#x27;2022-04-21T00:46:50.000000000&#x27;, &#x27;2022-04-24T00:56:45.000000000&#x27;,\n",
       "       &#x27;2022-04-26T00:46:39.000000000&#x27;, &#x27;2022-04-29T00:56:33.000000000&#x27;,\n",
       "       &#x27;2022-05-01T00:46:51.000000000&#x27;, &#x27;2022-05-04T00:56:45.000000000&#x27;,\n",
       "       &#x27;2022-05-06T00:46:41.000000000&#x27;, &#x27;2022-05-09T00:56:37.000000000&#x27;,\n",
       "       &#x27;2022-05-11T00:46:49.000000000&#x27;, &#x27;2022-05-14T00:56:44.000000000&#x27;,\n",
       "       &#x27;2022-05-16T00:46:45.000000000&#x27;, &#x27;2022-05-19T00:56:40.000000000&#x27;,\n",
       "       &#x27;2022-05-21T00:46:52.000000000&#x27;, &#x27;2022-05-24T00:56:47.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>138.5 138.5 138.5 ... 138.9 138.9</div><input id='attrs-46db6b72-3e8d-4ed4-8b41-70bd1ff411a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46db6b72-3e8d-4ed4-8b41-70bd1ff411a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d199d19-58ce-4fd1-9437-c31800cef9b9' class='xr-var-data-in' type='checkbox'><label for='data-3d199d19-58ce-4fd1-9437-c31800cef9b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([138.5001, 138.5003, 138.5005, ..., 138.8995, 138.8997, 138.8999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-34.6 -34.6 -34.6 ... -35.1 -35.1</div><input id='attrs-ef6fd6a2-50df-40e5-97d9-115aeafcecc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef6fd6a2-50df-40e5-97d9-115aeafcecc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebcc60c9-6b04-4354-858d-a48faff3186b' class='xr-var-data-in' type='checkbox'><label for='data-ebcc60c9-6b04-4354-858d-a48faff3186b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-34.6001, -34.6003, -34.6005, ..., -35.0995, -35.0997, -35.0999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79f758ba-11e5-4236-a23d-769a1e2087fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-79f758ba-11e5-4236-a23d-769a1e2087fb' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>fmask</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-064c001a-f23a-4b46-84fb-2589f1919536' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-064c001a-f23a-4b46-84fb-2589f1919536' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75c7dcbd-1e1d-4124-808e-0ef44bef5c19' class='xr-var-data-in' type='checkbox'><label for='data-75c7dcbd-1e1d-4124-808e-0ef44bef5c19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Quality mask</dd></dl></div><div class='xr-var-data'><pre>[110000000 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_red</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-151bce14-b626-4e0d-ba84-3f69a5115f0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-151bce14-b626-4e0d-ba84-3f69a5115f0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d584eb13-44c6-463e-8301-27592b3030ac' class='xr-var-data-in' type='checkbox'><label for='data-d584eb13-44c6-463e-8301-27592b3030ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Red band</dd></dl></div><div class='xr-var-data'><pre>[110000000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_green</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce83b6a1-06b4-47cd-800e-ff6282a9c1ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce83b6a1-06b4-47cd-800e-ff6282a9c1ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba476644-06f0-400a-833b-6bd8b3e9b13e' class='xr-var-data-in' type='checkbox'><label for='data-ba476644-06f0-400a-833b-6bd8b3e9b13e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Green band</dd></dl></div><div class='xr-var-data'><pre>[110000000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbart_blue</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a04495e-e5c5-4559-8dad-07921e09c152' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a04495e-e5c5-4559-8dad-07921e09c152' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31805300-32ba-4508-bc6a-3f45997e9485' class='xr-var-data-in' type='checkbox'><label for='data-31805300-32ba-4508-bc6a-3f45997e9485' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Blue band</dd></dl></div><div class='xr-var-data'><pre>[110000000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lfmc</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d75bd34-b422-4a6d-8551-496d590751cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d75bd34-b422-4a6d-8551-496d590751cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8eba0a81-df75-41db-9b99-2e4b3374e61b' class='xr-var-data-in' type='checkbox'><label for='data-8eba0a81-df75-41db-9b99-2e4b3374e61b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Live Fuel Moisture Content estimated with Random Forest model</dd></dl></div><div class='xr-var-data'><pre>[110000000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-010d430a-928d-417c-9bb0-9ee57da777e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-010d430a-928d-417c-9bb0-9ee57da777e3' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:4326</dd><dt><span>fmask_legend :</span></dt><dd>0:nodata, 1:valid, 2:cloud, 3:shadow, 4:snow, 5:water</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (time: 22, longitude: 2000, latitude: 2500)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2022-04-01T00:46:48 ... 2022-05-24T00:...\n",
       "  * longitude    (longitude) float64 138.5 138.5 138.5 ... 138.9 138.9 138.9\n",
       "  * latitude     (latitude) float64 -34.6 -34.6 -34.6 ... -35.1 -35.1 -35.1\n",
       "Data variables:\n",
       "    fmask        (time, latitude, longitude) int32 ...\n",
       "    nbart_red    (time, latitude, longitude) float64 ...\n",
       "    nbart_green  (time, latitude, longitude) float64 ...\n",
       "    nbart_blue   (time, latitude, longitude) float64 ...\n",
       "    lfmc         (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    crs:           EPSG:4326\n",
       "    fmask_legend:  0:nodata, 1:valid, 2:cloud, 3:shadow, 4:snow, 5:water"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfmc_final = xr.open_dataset('s2_lfmc.nc') ### change file name as needed\n",
    "lfmc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib inline\n",
    "#import matplotlib.pyplot as plt\n",
    "#from matplotlib.colors import LinearSegmentedColormap\n",
    "#import numpy as np\n",
    "\n",
    "\n",
    "#for i in range(ds.time.data.size):\n",
    "#    fmc_array = lfmc_final.lfmc.data[i,...]\n",
    "\n",
    "#    colors = [(0.87, 0, 0), (1, 1, 0.73), (0.165, 0.615, 0.957)]  # R -> G -> B\n",
    "#    cmap = LinearSegmentedColormap.from_list('fmc', colors, N=256)\n",
    "#    plt.figure(figsize=(10,10))\n",
    "#    plt.imshow(np.clip(fmc_array, 0, 136), cmap=cmap)\n",
    "#    plt.colorbar()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i in range(lfmc_final.time.data.size):\n",
    "#    ds_ = lfmc_final.isel(time=i)\n",
    "#    ds_[['nbart_red', 'nbart_green', 'nbart_blue']].to_array().plot.imshow(robust=True, figsize=(8,8))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lfmc_final_masked = xr.open_dataset('s2_lfmc_noclouds_noshadows.nc') ### change file name as needed\n",
    "\n",
    "#%matplotlib inline\n",
    "#import matplotlib.pyplot as plt\n",
    "#from matplotlib.colors import LinearSegmentedColormap\n",
    "#import numpy as np\n",
    "\n",
    "\n",
    "#for i in range(lfmc_final_masked.time.data.size):\n",
    "#    fmc_array = lfmc_final_masked.lfmc.data[i,...]\n",
    "\n",
    "#    colors = [(0.87, 0, 0), (1, 1, 0.73), (0.165, 0.615, 0.957)]  # R -> G -> B\n",
    "#    cmap = LinearSegmentedColormap.from_list('fmc', colors, N=256)\n",
    "#    plt.figure(figsize=(10,10))\n",
    "#    plt.imshow(np.clip(fmc_array, 0, 136), cmap=cmap)\n",
    "#    plt.colorbar()\n"
   ]
  }
 ],
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