1-wue-france.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4833f8e2-1a09-446a-89e7-f14e3bc7a693",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from pathlib import Path\n",
    "import xarray as xr\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "pd.set_option('mode.chained_assignment', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad3222e-2cd3-4d96-b05f-357cc5385311",
   "metadata": {},
   "source": [
    "# WUE on eddy-covariance data (with validation) 2016-2020\n",
    "\n",
    "This exercise has the following parts:\n",
    "1. Computation of dry-matter-productivity-based water use efficiency (WUE_DMP) from eddy-covariance flux product\n",
    "2. Approximation of yield with harvest index (HI) and computation of water use efficiency (WUE) (aka crop water productivity, CWP)\n",
    "3. Simulation of dry-matter productivity with light use efficiency (LUE) model and Sentinel-2 normalized difference vegetation index (NDVI)\n",
    "4. Scaling of Landsat-8-based evapotranspiration (ET) simulated with the two-source energy balance (TSEB) from instantaneous to seasonal values\n",
    "5. Computation of the Earth-Observation-based WUE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab076413-c713-4e4d-a2ba-5a32eb2f0169",
   "metadata": {},
   "source": [
    "# Data for this exercise \n",
    "\n",
    "FR-Lam is an ICOS eddy covariance site, flux data from 2016 until 2020 were downloaded from Warm Winter 2020 dataset (https://doi.org/10.18160/2G60-ZHAK).\n",
    "\n",
    "Data acquisition at FR-Aur and FR-Lam are mainly funded by the Institut National des Sciences de l’Univers (INSU) through the ICOS ERIC and the OSR SW observatory (https://osr.cesbio.cnrs.fr/). Facilities and staff are funded and supported by the Observatory Midi-Pyrenean, the University Paul Sabatier of Toulouse 3, CNRS (Centre National de la Recherche Scientifique), CNES (Centre National d’Etude Spatial) and IRD (Institut de Recherche pour le Développement). We are grateful to Franck Granouillac, Baptiste Lemaire, Nicole Claverie and Bartosz Zawilski for their technical support, advice, and valuable assistance in the field and site management, respectively. We also thank Tiphaine Tallec and Aurore Brut for the data processing. Special thanks to Mr. Andréoni (farmer) and Ecole d'Ingénieurs Purpan and farmers for accommodating the measurement devices.\n",
    "\n",
    "The PI, Aurore Brut, provided the information about the planting and harvest dates and the croptype (*FR_Lam_dates.csv*)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "773a324f-e781-4cbd-a87b-75d17df68dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path('1-wue-france-data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c32b65-8715-4f90-8a44-16c9b7d74597",
   "metadata": {},
   "source": [
    "# 1. Computation of dry-matter-productivity-based water use efficiency (WUE_DMP) from eddy-covariance flux product\n",
    "\n",
    "This exercise is based on daily **average** data (DD files) => first thing we do is the conversion to daily **sums**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ea9b1381-4715-4226-ad1c-664034c500a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_path / 'FR-Lam_DD.csv', index_col='date', parse_dates=True)\n",
    "df.loc['2017-03-01':'2017-06-01', 'LE_F_MDS'] = np.nan  # some quality issues\n",
    "\n",
    "df['dmp_24'] = df.GPP_NT_VUT_USTAR50 * (24*60*60) * (12+2+16) * 1e-6 * 1e-3 * 1e4 * 0.5  # kg DM ha-1 day-1\n",
    "df['et_24'] = df.LE_F_MDS * 0.035  # mm day-1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e586ae9f-9200-41d1-87c7-c125037f3bdd",
   "metadata": {},
   "source": [
    "Reading field data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e90e1295-8c9a-4143-b93a-b1e4115cbbc9",
   "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>Plot ID</th>\n",
       "      <th>Site name</th>\n",
       "      <th>Area (ha)</th>\n",
       "      <th>Season</th>\n",
       "      <th>crop</th>\n",
       "      <th>planting</th>\n",
       "      <th>harvesting</th>\n",
       "      <th>los</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>2015-09-08</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2015-10-20</td>\n",
       "      <td>2016-07-20</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>White mustard+Vetch</td>\n",
       "      <td>2016-08-17</td>\n",
       "      <td>2016-11-16</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2016-2017</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2017-2018</td>\n",
       "      <td>winter Wheat</td>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2018-2019</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>2019-09-09</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>winter Wheat</td>\n",
       "      <td>2019-10-19</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Plot ID   Site name  Area (ha)     Season                 crop   planting  \\\n",
       "0     LAM  Lamasquère         24  2014-2015       Irrigated corn 2015-05-05   \n",
       "1     LAM  Lamasquère         24  2015-2016                Wheat 2015-10-20   \n",
       "2     LAM  Lamasquère         24  2015-2016  White mustard+Vetch 2016-08-17   \n",
       "3     LAM  Lamasquère         24  2016-2017       Irrigated corn 2017-04-28   \n",
       "4     LAM  Lamasquère         24  2017-2018         winter Wheat 2017-11-16   \n",
       "5     LAM  Lamasquère         24  2018-2019       Irrigated corn 2019-04-22   \n",
       "6     LAM  Lamasquère         24  2019-2020         winter Wheat 2019-10-19   \n",
       "\n",
       "  harvesting  los  \n",
       "0 2015-09-08  126  \n",
       "1 2016-07-20  274  \n",
       "2 2016-11-16   91  \n",
       "3 2017-09-05  130  \n",
       "4 2018-07-10  236  \n",
       "5 2019-09-09  140  \n",
       "6 2020-07-03  258  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dates = pd.read_csv(data_path / 'FR-Lam_dates.csv')\n",
    "df_dates['planting'] = pd.to_datetime(df_dates['planting'], format='%d-%m-%y')\n",
    "df_dates['harvesting'] = pd.to_datetime(df_dates['harvesting'], format='%d-%m-%y')\n",
    "df_dates['los'] = (df_dates.harvesting - df_dates.planting).dt.days\n",
    "df_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cd68b2-c691-4b45-81ca-7fc1afa11df9",
   "metadata": {},
   "source": [
    "Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a5317f2e-412b-46bb-8482-7c857109c9b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x256869294c0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.dmp_24.plot()\n",
    "\n",
    "for _, row in df_dates.iterrows():\n",
    "    color = 'tab:green'\n",
    "    if 'corn' in row.crop:\n",
    "        color = 'tab:olive'\n",
    "    elif 'Vetch' in row.crop:\n",
    "        color = 'tab:red'\n",
    "    \n",
    "    ax.axvspan(row.planting, row.harvesting, color=color, alpha=0.5, label=row.crop)\n",
    "    \n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd43b01-0b93-445a-ad3a-defe78577023",
   "metadata": {},
   "source": [
    "# Q: do the declared seasons coincide with the peaks in photosynthesis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a2c4935-6ec0-464f-88ff-1df111546bd3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b79edd71-a997-419d-af3b-2f293997fd16",
   "metadata": {},
   "source": [
    "Computing WUE_DMP\n",
    "\n",
    "$WUE_{DMP} \\ \\text{[kg mm-1 ha-1]} = \\dfrac{DMP_{season}}{ET_{season}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9c9afb68-178a-43a9-9bb3-209e8c83fb71",
   "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>Plot ID</th>\n",
       "      <th>Site name</th>\n",
       "      <th>Area (ha)</th>\n",
       "      <th>Season</th>\n",
       "      <th>crop</th>\n",
       "      <th>planting</th>\n",
       "      <th>harvesting</th>\n",
       "      <th>los</th>\n",
       "      <th>dmp_sum</th>\n",
       "      <th>et_sum</th>\n",
       "      <th>wue_dmp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>2015-09-08</td>\n",
       "      <td>126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2015-10-20</td>\n",
       "      <td>2016-07-20</td>\n",
       "      <td>274</td>\n",
       "      <td>18520.225819</td>\n",
       "      <td>483.582059</td>\n",
       "      <td>38.298000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>White mustard+Vetch</td>\n",
       "      <td>2016-08-17</td>\n",
       "      <td>2016-11-16</td>\n",
       "      <td>91</td>\n",
       "      <td>858.693106</td>\n",
       "      <td>110.192943</td>\n",
       "      <td>7.792632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2016-2017</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>130</td>\n",
       "      <td>10753.378819</td>\n",
       "      <td>221.507608</td>\n",
       "      <td>48.546318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2017-2018</td>\n",
       "      <td>winter Wheat</td>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>236</td>\n",
       "      <td>13655.620142</td>\n",
       "      <td>266.338612</td>\n",
       "      <td>51.271650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2018-2019</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>2019-09-09</td>\n",
       "      <td>140</td>\n",
       "      <td>19282.141457</td>\n",
       "      <td>297.150662</td>\n",
       "      <td>64.890118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>winter Wheat</td>\n",
       "      <td>2019-10-19</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>258</td>\n",
       "      <td>14257.623345</td>\n",
       "      <td>256.637044</td>\n",
       "      <td>55.555594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Plot ID   Site name  Area (ha)     Season                 crop   planting  \\\n",
       "0     LAM  Lamasquère         24  2014-2015       Irrigated corn 2015-05-05   \n",
       "1     LAM  Lamasquère         24  2015-2016                Wheat 2015-10-20   \n",
       "2     LAM  Lamasquère         24  2015-2016  White mustard+Vetch 2016-08-17   \n",
       "3     LAM  Lamasquère         24  2016-2017       Irrigated corn 2017-04-28   \n",
       "4     LAM  Lamasquère         24  2017-2018         winter Wheat 2017-11-16   \n",
       "5     LAM  Lamasquère         24  2018-2019       Irrigated corn 2019-04-22   \n",
       "6     LAM  Lamasquère         24  2019-2020         winter Wheat 2019-10-19   \n",
       "\n",
       "  harvesting  los       dmp_sum      et_sum    wue_dmp  \n",
       "0 2015-09-08  126      0.000000    0.000000        NaN  \n",
       "1 2016-07-20  274  18520.225819  483.582059  38.298000  \n",
       "2 2016-11-16   91    858.693106  110.192943   7.792632  \n",
       "3 2017-09-05  130  10753.378819  221.507608  48.546318  \n",
       "4 2018-07-10  236  13655.620142  266.338612  51.271650  \n",
       "5 2019-09-09  140  19282.141457  297.150662  64.890118  \n",
       "6 2020-07-03  258  14257.623345  256.637044  55.555594  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, row in df_dates.iterrows():\n",
    "    df_dates.loc[i, 'dmp_sum'] = df.loc[row.planting:row.harvesting, 'dmp_24'].sum()\n",
    "    df_dates.loc[i, 'et_sum'] = df.loc[row.planting:row.harvesting, 'et_24'].sum()\n",
    "\n",
    "df_dates['wue_dmp'] = df_dates.dmp_sum / df_dates.et_sum\n",
    "df_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7da733-8ec2-4628-9680-0aa0f343ac3f",
   "metadata": {},
   "source": [
    "# 2.Approximation of yield with harvest index (HI) and computation of water use efficiency (WUE) (aka crop water productivity, CWP)\n",
    "\n",
    "$yield\\ \\text{[kg FW ha-1]} = \\dfrac{DMP * f\\_agb * HI} {(1 - \\theta)}$\n",
    "\n",
    "- DMP - dry matter productivity\n",
    "- f_agb - above-ground-biomass fraction\n",
    "- HI - harvest index (dry weight of the harvestable material / total aboveground biomass)\n",
    "- $\\theta$ - moisture content of the harvestable material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d36337e3-4523-49f3-973e-4a7804b16714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot ID</th>\n",
       "      <th>Site name</th>\n",
       "      <th>Area (ha)</th>\n",
       "      <th>Season</th>\n",
       "      <th>crop</th>\n",
       "      <th>planting</th>\n",
       "      <th>harvesting</th>\n",
       "      <th>los</th>\n",
       "      <th>dmp_sum</th>\n",
       "      <th>et_sum</th>\n",
       "      <th>wue_dmp</th>\n",
       "      <th>yield</th>\n",
       "      <th>wue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>2015-09-08</td>\n",
       "      <td>126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2015-10-20</td>\n",
       "      <td>2016-07-20</td>\n",
       "      <td>274</td>\n",
       "      <td>18520.225819</td>\n",
       "      <td>483.582059</td>\n",
       "      <td>38.298000</td>\n",
       "      <td>9369.055414</td>\n",
       "      <td>19.374282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>White mustard+Vetch</td>\n",
       "      <td>2016-08-17</td>\n",
       "      <td>2016-11-16</td>\n",
       "      <td>91</td>\n",
       "      <td>858.693106</td>\n",
       "      <td>110.192943</td>\n",
       "      <td>7.792632</td>\n",
       "      <td>434.397689</td>\n",
       "      <td>3.942155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2016-2017</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>130</td>\n",
       "      <td>10753.378819</td>\n",
       "      <td>221.507608</td>\n",
       "      <td>48.546318</td>\n",
       "      <td>7193.138534</td>\n",
       "      <td>32.473551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2017-2018</td>\n",
       "      <td>winter Wheat</td>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>236</td>\n",
       "      <td>13655.620142</td>\n",
       "      <td>266.338612</td>\n",
       "      <td>51.271650</td>\n",
       "      <td>6908.137248</td>\n",
       "      <td>25.937423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2018-2019</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>2019-09-09</td>\n",
       "      <td>140</td>\n",
       "      <td>19282.141457</td>\n",
       "      <td>297.150662</td>\n",
       "      <td>64.890118</td>\n",
       "      <td>12898.189218</td>\n",
       "      <td>43.406228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>winter Wheat</td>\n",
       "      <td>2019-10-19</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>258</td>\n",
       "      <td>14257.623345</td>\n",
       "      <td>256.637044</td>\n",
       "      <td>55.555594</td>\n",
       "      <td>7212.680045</td>\n",
       "      <td>28.104594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Plot ID   Site name  Area (ha)     Season                 crop   planting  \\\n",
       "0     LAM  Lamasquère         24  2014-2015       Irrigated corn 2015-05-05   \n",
       "1     LAM  Lamasquère         24  2015-2016                Wheat 2015-10-20   \n",
       "2     LAM  Lamasquère         24  2015-2016  White mustard+Vetch 2016-08-17   \n",
       "3     LAM  Lamasquère         24  2016-2017       Irrigated corn 2017-04-28   \n",
       "4     LAM  Lamasquère         24  2017-2018         winter Wheat 2017-11-16   \n",
       "5     LAM  Lamasquère         24  2018-2019       Irrigated corn 2019-04-22   \n",
       "6     LAM  Lamasquère         24  2019-2020         winter Wheat 2019-10-19   \n",
       "\n",
       "  harvesting  los       dmp_sum      et_sum    wue_dmp         yield  \\\n",
       "0 2015-09-08  126      0.000000    0.000000        NaN      0.000000   \n",
       "1 2016-07-20  274  18520.225819  483.582059  38.298000   9369.055414   \n",
       "2 2016-11-16   91    858.693106  110.192943   7.792632    434.397689   \n",
       "3 2017-09-05  130  10753.378819  221.507608  48.546318   7193.138534   \n",
       "4 2018-07-10  236  13655.620142  266.338612  51.271650   6908.137248   \n",
       "5 2019-09-09  140  19282.141457  297.150662  64.890118  12898.189218   \n",
       "6 2020-07-03  258  14257.623345  256.637044  55.555594   7212.680045   \n",
       "\n",
       "         wue  \n",
       "0        NaN  \n",
       "1  19.374282  \n",
       "2   3.942155  \n",
       "3  32.473551  \n",
       "4  25.937423  \n",
       "5  43.406228  \n",
       "6  28.104594  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# wheat\n",
    "f_agb = 0.86  # from FAO\n",
    "hi = 0.50 # -\n",
    "theta = 0.15  # g DW g-1 FW\n",
    "\n",
    "df_dates['yield'] = df_dates.dmp_sum * f_agb * hi / (1 - theta)\n",
    "df_dates.loc[df_dates.crop.str.contains('corn'), 'yield'] = df_dates.dmp_sum * 0.90 * 0.55 / (1 - 0.26)  # maize\n",
    "\n",
    "df_dates['wue'] = df_dates['yield'] / df_dates['et_sum']  # kg ha-1 mm-1\n",
    "df_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773a7625-4ae6-4b37-adab-558f8160e904",
   "metadata": {},
   "source": [
    "# Q: is this WUE anywhere near the literature values for corn and wheat?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "277592db-c573-4cb0-b1cb-2c0f08433e20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "facf56b3-99e9-4700-bf86-4e75a015f4a2",
   "metadata": {},
   "source": [
    "# 3. Simulation of dry-matter productivity with light use efficiency (LUE) model and Sentinel-2 normalized difference vegetation index (NDVI)\n",
    "\n",
    "$ GPP \\ \\text{[g C m-2 day-1]} = LUE_{MAX} * f(Ta) * f(VPD) * fpar * (SW\\_IN * 0.45) $\n",
    "\n",
    "- lue_max - light use efficiency, g C MJ-1\n",
    "- f(Ta) - air temperature dependence (stress function, 0 => stress, 1 => ok)\n",
    "- f(VPD) - vapour pressure deficit dependence (stress function, 0 => stress, 1 => ok)\n",
    "- fpar - fraction of absorbed photosynthetically active radiation\n",
    "- (SW_IN * 0.45) - photosynthetically active radiation (PAR) accumulated over the day, 45% of total incoming radiation\n",
    "\n",
    "The values of parameters are from He et al., 2018 https://doi.org/10.3390/rs10030372"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f73f192a-8c02-420a-ae72-a52b9e2affc4",
   "metadata": {},
   "source": [
    "reading NDVI and making it daily\n",
    "\n",
    "NDVI is from Sentinel-2 HR-VPP project https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity downloaded through WEkEO Copernicus DIAS and aggregated to field level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6674086c-cfce-49ed-bdc4-6ccf10b149a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ndvi = pd.read_csv(data_path / 'FR-Lam_NDVI.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c53cd133-c305-4a38-ada9-e4961a146e7d",
   "metadata": {},
   "source": [
    "linear interpolation is a bit jumpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "34781bf1-f243-4188-867a-140acbf73c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ndvi.NDVI_median.resample('D').mean().interpolate().plot(style='.-')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a8fea9-4b19-4b02-9231-5284682e6069",
   "metadata": {},
   "source": [
    "moving average is also a bit jumpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "71081de9-d02d-45ba-9231-f326f03c1a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ndvi.NDVI_median.rolling('30D').mean().shift(-15).interpolate().plot(style='.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9c706d69-3abd-4fa1-a1ac-5c50893925f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ndvi.NDVI_median.resample('D').mean().interpolate().rolling('30D').mean().shift(-15).plot(style='.-')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81ae13f-e3e6-43d3-955d-5fc8d12c366b",
   "metadata": {},
   "source": [
    "NDVI -> fpar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d35a5a57-bcf2-4dab-8f79-1a185a995da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndvi = df_ndvi.NDVI_median.resample('D').mean().interpolate().rolling('30D').mean().shift(-15)\n",
    "\n",
    "ndvi_max = ndvi.max()\n",
    "ndvi_min = ndvi.min()\n",
    "\n",
    "fpar_max = 0.95\n",
    "fpar_min = 0.01\n",
    "\n",
    "fpar = (ndvi - ndvi_min) / (ndvi_max - ndvi_min) * (fpar_max - fpar_min)\n",
    "fpar.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cade2b-e3db-4bfe-b557-5556de5b3353",
   "metadata": {},
   "source": [
    "stress factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "134344bb-c782-42cb-8bf6-ccd0dc8b9fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "lue_max = 2.55  # gC MJ-1\n",
    "vpd_max = 6940 * 1e-2  # hPa\n",
    "vpd_min = 1 * 1e-2  # hPa\n",
    "ta_max = 45.85  # C\n",
    "ta_min = -23.15  # C\n",
    "\n",
    "df['ta_stress'] = (df.TA_F - ta_min)/(ta_max - ta_min)\n",
    "df.loc[df.TA_F > ta_max, 'ta_stress'] = 1\n",
    "df.loc[df.TA_F < ta_min, 'ta_stress'] = 0\n",
    "\n",
    "\n",
    "df['vpd_stress'] = (vpd_max - df.VPD_F)/(vpd_max - vpd_min)\n",
    "df.loc[df.VPD_F > vpd_max, 'vpd_stress'] = 0\n",
    "df.loc[df.VPD_F < vpd_min, 'vpd_stress'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "039d859b-fdf1-4961-badb-84ffb909eb40",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(fpar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4c58487e-1bc7-4f9d-aaed-0c165f61c4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ta_stress.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "decec14c-58c2-40a1-8d36-97219eebe19f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.vpd_stress.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "639fc87c-34c0-48e9-9395-a9d3680f601c",
   "metadata": {},
   "source": [
    "# Q: Which parameter (in this model realization) affects photosynthesis stronger Ta or VPD?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6775eb6-f79c-4ee0-881a-d9283804c88c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "70ac07af-13cf-45f0-9495-5905bdd28b0a",
   "metadata": {},
   "source": [
    "computation of GPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6c186c46-1515-4086-9146-c2ce0e267b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gpp'] = lue_max * df.ta_stress * df.vpd_stress * fpar * df.SW_IN_F * (60*60*24 * 1e-6) * 0.45 # gC m-2 day-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d4951417-6cec-42ec-a34e-b1062db9f6ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gpp_umol'] = lue_max * df.ta_stress * df.vpd_stress * fpar * df.SW_IN_F * 0.45 / 12 # umolCO2 m-2 s-1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4ba9f2-b32d-4e76-9541-18d284645ee3",
   "metadata": {},
   "source": [
    "validation looks terrific, taken that no fitting was applied, pure values from the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "151cbea6-3489-4b98-84ec-df1d14a15923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2568cc67760>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot(data=df, x='GPP_NT_VUT_USTAR50', y='gpp_umol')\n",
    "lims = [0, 30]\n",
    "g.axes.flatten()[0].plot(lims, lims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "01222d13-016b-4a8b-b0a6-b668270c186e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.gpp_umol.plot()\n",
    "df.GPP_NT_VUT_USTAR50.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55144a72-e18b-4b1b-a174-5beb5fd0400f",
   "metadata": {},
   "source": [
    "# Q1: Can you compute the metrics for the fit: R2, RMSE, bias?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c700c92-1475-4e01-89b4-bb220e916ab9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "55e12761-1e1a-40f4-bedb-9a63fb2b76df",
   "metadata": {},
   "source": [
    "# Q2*: Can you suggest reparamerization of the LUE to improve the fit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5601be46-b064-486e-b906-5d5998c1e743",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "af1efe32-e4f7-48eb-8f90-1cdace072a22",
   "metadata": {},
   "source": [
    "# 4. Scaling of Landsat-8-based evapotranspiration (ET) simulated with the two-source energy balance (TSEB) from instantaneous to seasonal values\n",
    "\n",
    "TSEB model was explained to you on Tuesday\n",
    "\n",
    "There is a Google Earth Engine version of it that runs on Landsat images: https://code.earthengine.google.com/cf6e11158e3e4d5ab2acbce994882ed6?noload=true\n",
    "\n",
    "The resulting images are in the folder *GEE_esa_fr*\n",
    "\n",
    "The model returns instantaneous (at the time of satellite overpass) latent heat flux (LE)\n",
    "\n",
    "We are going to scale it to daily ET and further to fill the gaps in between satellite overpasses based on net radiation and evaporative fraction (EF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d874b5-6c93-4ae4-b310-5c4c50c7f04e",
   "metadata": {},
   "source": [
    "## what is in the image\n",
    "\n",
    "- bands of Landsat surface reflectance (SR_*), surface temperature (ST_*), quality flags\n",
    "    - computed NDVI, albedo, radiometric temperature, LAI\n",
    "- ERA5 meteorological data at the time of overpass\n",
    "    - radiation, temperature, wind speed\n",
    "- TSEB computed values\n",
    "    - soil, canopy and scene temperature, net radiation, instantaneous heat fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3131acd8-619b-45c5-87d3-7d5e3c3439ab",
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (band: 55, x: 20, y: 22)\n",
       "Coordinates:\n",
       "  * band         (band) int32 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55\n",
       "  * x            (x) float64 3.572e+05 3.573e+05 ... 3.578e+05 3.578e+05\n",
       "  * y            (y) float64 4.818e+06 4.818e+06 ... 4.817e+06 4.817e+06\n",
       "    spatial_ref  int32 0\n",
       "Data variables:\n",
       "    band_data    (band, y, x) float32 nan nan nan nan nan ... nan nan nan nan</pre><div class='xr-wrap' hidden><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-c3bba16e-9b60-4164-902a-b02fe383a1de' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c3bba16e-9b60-4164-902a-b02fe383a1de' 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'>band</span>: 55</li><li><span class='xr-has-index'>x</span>: 20</li><li><span class='xr-has-index'>y</span>: 22</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dde94313-265c-4698-b4db-e9f80215ba03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dde94313-265c-4698-b4db-e9f80215ba03' 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'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 50 51 52 53 54 55</div><input id='attrs-23226002-ae24-4a78-ac90-16d32116c317' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23226002-ae24-4a78-ac90-16d32116c317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-850b9fa2-3379-427c-999d-f5a88c488e29' class='xr-var-data-in' type='checkbox'><label for='data-850b9fa2-3379-427c-999d-f5a88c488e29' 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'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.572e+05 3.573e+05 ... 3.578e+05</div><input id='attrs-42e86941-c05c-4a4b-8665-c2d4e6fcddf1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42e86941-c05c-4a4b-8665-c2d4e6fcddf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14621711-5fc2-439b-908d-e0e43ce4795a' class='xr-var-data-in' type='checkbox'><label for='data-14621711-5fc2-439b-908d-e0e43ce4795a' 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'></dl></div><div class='xr-var-data'><pre>array([357240., 357270., 357300., 357330., 357360., 357390., 357420., 357450.,\n",
       "       357480., 357510., 357540., 357570., 357600., 357630., 357660., 357690.,\n",
       "       357720., 357750., 357780., 357810.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.818e+06 4.818e+06 ... 4.817e+06</div><input id='attrs-d907e06c-efc0-4851-b433-9fce42842b2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d907e06c-efc0-4851-b433-9fce42842b2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3a30e2e-82da-485d-af1f-dfcb98e9dbb4' class='xr-var-data-in' type='checkbox'><label for='data-b3a30e2e-82da-485d-af1f-dfcb98e9dbb4' 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'></dl></div><div class='xr-var-data'><pre>array([4817760., 4817730., 4817700., 4817670., 4817640., 4817610., 4817580.,\n",
       "       4817550., 4817520., 4817490., 4817460., 4817430., 4817400., 4817370.,\n",
       "       4817340., 4817310., 4817280., 4817250., 4817220., 4817190., 4817160.,\n",
       "       4817130.])</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-5118a7ef-f8bc-4ad2-a19b-6e279b4e6987' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5118a7ef-f8bc-4ad2-a19b-6e279b4e6987' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e823190d-740b-48ae-8647-236e8b733993' class='xr-var-data-in' type='checkbox'><label for='data-e823190d-740b-48ae-8647-236e8b733993' 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>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 31N&quot;,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;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,3],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32631&quot;]]</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>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 31N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>3.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 31N&quot;,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;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,3],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32631&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>357225.0 30.0 0.0 4817775.0 0.0 -30.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3f1b355-78f4-4aa2-8ba7-7647851b8c62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3f1b355-78f4-4aa2-8ba7-7647851b8c62' class='xr-section-summary' >Data variables: <span>(1)</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>band_data</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-545c5fcd-c218-4bc0-ba9a-eb088c968e81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-545c5fcd-c218-4bc0-ba9a-eb088c968e81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e162583-0981-4bb8-8492-dd8c25a2b572' class='xr-var-data-in' type='checkbox'><label for='data-9e162583-0981-4bb8-8492-dd8c25a2b572' 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>(&#x27;SR_B1&#x27;, &#x27;SR_B2&#x27;, &#x27;SR_B3&#x27;, &#x27;SR_B4&#x27;, &#x27;SR_B5&#x27;, &#x27;SR_B6&#x27;, &#x27;SR_B7&#x27;, &#x27;SR_QA_AEROSOL&#x27;, &#x27;ST_B10&#x27;, &#x27;ST_ATRAN&#x27;, &#x27;ST_CDIST&#x27;, &#x27;ST_DRAD&#x27;, &#x27;ST_EMIS&#x27;, &#x27;ST_EMSD&#x27;, &#x27;ST_QA&#x27;, &#x27;ST_TRAD&#x27;, &#x27;ST_URAD&#x27;, &#x27;QA_PIXEL&#x27;, &#x27;QA_RADSAT&#x27;, &#x27;albedo&#x27;, &#x27;albedo_vis&#x27;, &#x27;albedo_nir&#x27;, &#x27;NDVI&#x27;, &#x27;radiometric_temperature&#x27;, &#x27;LAI&#x27;, &#x27;surface_pressure&#x27;, &#x27;air_temperature&#x27;, &#x27;u_component_of_wind_10m&#x27;, &#x27;v_component_of_wind_10m&#x27;, &#x27;surface_solar_radiation_downwards_hourly&#x27;, &#x27;surface_thermal_radiation_downwards_hourly&#x27;, &#x27;wind_speed&#x27;, &#x27;solar_radiation&#x27;, &#x27;thermal_radiation&#x27;, &#x27;Tc&#x27;, &#x27;Ts&#x27;, &#x27;Tac&#x27;, &#x27;Hc&#x27;, &#x27;Hs&#x27;, &#x27;LEc&#x27;, &#x27;LEs&#x27;, &#x27;Ra&#x27;, &#x27;Rs&#x27;, &#x27;Rx&#x27;, &#x27;Ustar&#x27;, &#x27;alphaPT&#x27;, &#x27;iteration&#x27;, &#x27;LE&#x27;, &#x27;H&#x27;, &#x27;G&#x27;, &#x27;Rn&#x27;, &#x27;Rns&#x27;, &#x27;Rnc&#x27;, &#x27;ETd&#x27;, &#x27;EF&#x27;)</dd></dl></div><div class='xr-var-data'><pre>array([[[     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ..., 0.03012 , 0.03826 ],\n",
       "        ...,\n",
       "        [     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ...,      nan,      nan]],\n",
       "\n",
       "       [[     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ..., 0.035785, 0.042055],\n",
       "        ...,\n",
       "        [     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ...,      nan,      nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ..., 6.543853, 4.022264],\n",
       "        ...,\n",
       "        [     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ...,      nan,      nan]],\n",
       "\n",
       "       [[     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ..., 0.719093, 0.429215],\n",
       "        ...,\n",
       "        [     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ...,      nan,      nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a66d1d9-d872-413d-a6b5-be344d8d820d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a66d1d9-d872-413d-a6b5-be344d8d820d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (band: 55, x: 20, y: 22)\n",
       "Coordinates:\n",
       "  * band         (band) int32 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55\n",
       "  * x            (x) float64 3.572e+05 3.573e+05 ... 3.578e+05 3.578e+05\n",
       "  * y            (y) float64 4.818e+06 4.818e+06 ... 4.817e+06 4.817e+06\n",
       "    spatial_ref  int32 ...\n",
       "Data variables:\n",
       "    band_data    (band, y, x) float32 ..."
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tif = xr.open_dataset(data_path / 'GEE_esa_fr' / '1_2_LC08_198030_20190814_20190814T10.tif')\n",
    "tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "980f5068-3b3c-47a3-b207-a0469ecbeca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('SR_B1',\n",
       " 'SR_B2',\n",
       " 'SR_B3',\n",
       " 'SR_B4',\n",
       " 'SR_B5',\n",
       " 'SR_B6',\n",
       " 'SR_B7',\n",
       " 'SR_QA_AEROSOL',\n",
       " 'ST_B10',\n",
       " 'ST_ATRAN',\n",
       " 'ST_CDIST',\n",
       " 'ST_DRAD',\n",
       " 'ST_EMIS',\n",
       " 'ST_EMSD',\n",
       " 'ST_QA',\n",
       " 'ST_TRAD',\n",
       " 'ST_URAD',\n",
       " 'QA_PIXEL',\n",
       " 'QA_RADSAT',\n",
       " 'albedo',\n",
       " 'albedo_vis',\n",
       " 'albedo_nir',\n",
       " 'NDVI',\n",
       " 'radiometric_temperature',\n",
       " 'LAI',\n",
       " 'surface_pressure',\n",
       " 'air_temperature',\n",
       " 'u_component_of_wind_10m',\n",
       " 'v_component_of_wind_10m',\n",
       " 'surface_solar_radiation_downwards_hourly',\n",
       " 'surface_thermal_radiation_downwards_hourly',\n",
       " 'wind_speed',\n",
       " 'solar_radiation',\n",
       " 'thermal_radiation',\n",
       " 'Tc',\n",
       " 'Ts',\n",
       " 'Tac',\n",
       " 'Hc',\n",
       " 'Hs',\n",
       " 'LEc',\n",
       " 'LEs',\n",
       " 'Ra',\n",
       " 'Rs',\n",
       " 'Rx',\n",
       " 'Ustar',\n",
       " 'alphaPT',\n",
       " 'iteration',\n",
       " 'LE',\n",
       " 'H',\n",
       " 'G',\n",
       " 'Rn',\n",
       " 'Rns',\n",
       " 'Rnc',\n",
       " 'ETd',\n",
       " 'EF')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band_names = tif.band_data.attrs['long_name']\n",
    "tif.band_data.attrs['long_name'] = []\n",
    "band_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "56549a04-991a-4f8a-a7a8-196a7784d311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 49, 47, 29)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band_names.index('Rn'), band_names.index('G'), band_names.index('LE'), band_names.index('surface_solar_radiation_downwards_hourly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c7bbf06c-f186-47f2-8e4f-653bcd66b755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2568a4a2460>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tif.sel(band=48).band_data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634d9966-7b6d-41f3-bd9e-12e55d307e62",
   "metadata": {},
   "source": [
    "reading images in a loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "78c95cb6-1230-41e1-a87b-7e49a4498b43",
   "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>path</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2...</td>\n",
       "      <td>2015-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2...</td>\n",
       "      <td>2015-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2...</td>\n",
       "      <td>2016-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2...</td>\n",
       "      <td>2016-01-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2...</td>\n",
       "      <td>2016-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2...</td>\n",
       "      <td>2016-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2...</td>\n",
       "      <td>2016-03-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                path       date\n",
       "0  1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2... 2015-12-09\n",
       "1  1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2... 2015-12-25\n",
       "2  1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2... 2016-01-10\n",
       "3  1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2... 2016-01-26\n",
       "4  1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2... 2016-02-27\n",
       "5  1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2... 2016-03-14\n",
       "6  1-wue-france-data\\GEE_esa_fr\\1_2_LC08_198030_2... 2016-03-30"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_path = pd.DataFrame({'path': (data_path / 'GEE_esa_fr').glob('*.tif')})\n",
    "df_path['date'] = df_path.path.apply(lambda row: pd.to_datetime(row.name.split('_')[-1].replace('T10.tif', '')))\n",
    "df_path = df_path.sort_values('date')\n",
    "df_path.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "165f8278-d994-4a1e-81d6-a16715ad0183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1871999b0c1c4a8782805fb00d7625cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tiffs:   0%|          | 0/111 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_val = []\n",
    "\n",
    "for _, row in tqdm(list(df_path.iterrows()), 'tiffs'):\n",
    "    tif = xr.open_dataset(row.path)\n",
    "    tif.band_data.attrs = dict()\n",
    "    \n",
    "    ef = tif.sel(band=48) / (tif.sel(band=51) - tif.sel(band=50))\n",
    "    swf = tif.sel(band=48) / (tif.sel(band=30) / (60*60))\n",
    "    \n",
    "    # break\n",
    "    res_i = {\n",
    "        'date': row.date, \n",
    "        'EF': ef.band_data.mean().values,\n",
    "        'SWF': swf.band_data.mean().values \n",
    "    }\n",
    "    \n",
    "    date_val.append(res_i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6e020102-7596-4ba9-9a66-7b2bb2665626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAaQElEQVR4nO3dfZRddX3v8ffHAWqoaLCMWhIesjSA+IBe5ga9XMqDpcS2lIeiBWt9um1MV1Fva1G011UX2mW67INWsGlKJfV6r1mxxMjCaLwtVC1tr5kYKCQ8NMIFZnKvDnrRChFI+PaPvcecnJyZ2eecfc7Z+7c/r7WyMmfvPWd+vzlzvvv3+/4ejiICMzOrv2eMugBmZlYOB3Qzs0Q4oJuZJcIB3cwsEQ7oZmaJcEA3M0vEYUUukrQS+DgwBlwfEWvazh8NfAp4IfAj4G0Rcdd8z3nMMcfEiSee2EuZzcwaa/v27Y9ExHincwsGdEljwHXA+cAUsE3STRGxq+Wy9wO3R8Qlkk7Jr3/NfM974oknMjk5WbQOZmYGSHpwrnNFUi4rgN0RcX9EPAlsAC5qu+ZU4O8AIuIe4ERJz++xvGZm1oMiAX0J8HDL46n8WKs7gEsBJK0ATgCWtj+RpFWSJiVNzszM9FZiMzPrqEhAV4dj7fsFrAGOlnQ78A5gB7DvkG+KWBcRExExMT7eMQVkZmY9KjIoOgUc1/J4KbCn9YKI+AHwVgBJAh7I/5lZgjbvmOajW+9lz6N7OXbxIq664GQufmV7x92GrUgLfRuwXNIySUcAlwM3tV4gaXF+DuDXga/lQd7MErN5xzTv23Qn04/uJYDpR/fyvk13snnH9KiL1ngLttAjYp+kK4GtZNMWPxUROyWtzs+vBV4MfFrSfmAX8F8GWGYzG6GPbr2XvU/tP+jY3qf28+6NdwC4pT5CheahR8QWYEvbsbUtX/8TsLzcoplZFe15dG/H4/sjeN+mOwEH9VHxStEa2LxjmjPX3MKyq7/ImWtucdfWRurYxYvmPLf3qf18dOu9QyyNtXJArzjnK61qrrrgZBYdPjbn+bla8IPiBs8BDugVN1e+0q0gG5WLX7mEj1z6MsbUaUbz/C34srnBc7BCOXQbnblaO8NuBVl9DGpKYfvzXnHGcdy4ffqgBseiw8e46oKT+/5ZRc3X4GliHt8BnWrPqT128SKmOwTvYbaCrD5mW6yzQW62xQr9DVR2et4bt0/zy6cv4dZ7Zkb23nGD52CND+iDegOU5aoLTj6ofDD8VpDVx6BarHM97633zHDb1ef1/Lz9coPnYI3PoVc9Rz2br1yyeBEClixexEcufVklbjZWPYNqsVa1JdxpgLbJDZ6kWui9pE6q+ofa6uJXLnEAt0LKbrHOvqfaN2/q93nLMvu+qGrKdNiSCei9pk7cZStXlccjmqDMFF37e6pdVVrCbvAckEzKpdfUibts5fEUstErM0XX6T01y6m/akqmhd5r6sRdtvJ4CtlwLNQLKqvFOtd7RzDSgVCbWzIBvZ/Uibts5ajDeETdDXNWltOR9ZNMysWpk9Gb643uAFCeYc7K8nuqfpIJ6J7eN3oOAIM3zF6Q31P1k0zKBZw6GTWPRwzesNMgfk/VS1IB3UbPAWCwvHLY5uOAblYj7gXZfBob0L0AxurKvSCbSyMDetU35DIz60Uys1y6UfUNuczMetHIFroXwJilx2nUhrbQvQDGLC3eRyjTyIDuBTA2yx8wnAanUTONTLl46peBB8dT4jRqplBAl7QS+DgwBlwfEWvazj8H+AxwfP6cfxQRN5Rc1lJ56pd5d8h0eCOxzIIpF0ljwHXAa4FTgSskndp22W8BuyLiNOAc4I8lHVFyWc1K5VZdOpxGzRTJoa8AdkfE/RHxJLABuKjtmgCOkiTgWcD3gH2lltSsZB4cT4c3EssUSbksAR5ueTwFnNF2zbXATcAe4CjgVyLi6fYnkrQKWAVw/PHH91Jes9J4X5S0OI1arIWuDsfaPzP2AuB24FjgFcC1kp59yDdFrIuIiYiYGB8f77KoZuVyq85SU6SFPgUc1/J4KVlLvNVbgTUREcBuSQ8ApwDfKKWUZgPiVp2lpEgLfRuwXNKyfKDzcrL0SquHgNcASHo+cDJwf5kFNTOz+S3YQo+IfZKuBLaSTVv8VETslLQ6P78W+BCwXtKdZCma90bEIwMstw2Zl1WbVV+heegRsQXY0nZsbcvXe4CfK7doVhVegGNWD41c+m/d8bJqs3pwQLcFeQGOWT00ci8X605qy6o9HmCpcgvdFpTSsmpvs2opc0C3BaW0AMfjAZYyp1yskFQW4Hg8wFLmgG6Nktp4gHXW1HESp1ysUVIaD7DOmjxO4ha6NUoZn1bV1NZfXTT5g0sc0K1x+hkP8KrZ6mvyOIlTLmZd8CyZ6mvyB5c4oJt1ocmtv7po8jiJA7pZF5rc+quLlNZNdMs5dOtK0wcE/bF19ZDKuoluOaBbYR4QLGeWjNmgOKBbYU2eDtaqqa0/qz7n0K0wDwiaVZsDuhXmAUGzanNAt8KaPB3MrIjNO6Y5c80tLLv6i5y55pahbzfgHLoV5gFBs7lVYdKAA7p1xQOCZp1VYdKAUy5mZiWowqQBB3QzsxJUYdKAA7oZox/MsvqrwqSBQgFd0kpJ90raLenqDuevknR7/u8uSfslPbf84pqVr8kfiGDlqcIeMoqI+S+QxoD7gPOBKWAbcEVE7Jrj+guB346I8+Z73omJiZicnOyp0GZlOnPNLR0/lm7J4kXcdvW8f8ZmQydpe0RMdDpXZJbLCmB3RNyfP9kG4CKgY0AHrgA+20tBu9H0TaKsPFUYzDIrQ5GUyxLg4ZbHU/mxQ0g6ElgJ3Nh/0ebmLrKVqQqDWWZlKBLQ1eHYXHmaC4HbIuJ7HZ9IWiVpUtLkzMxM0TIewp8aY2WqwmCWWRmKpFymgONaHi8F9sxx7eXMk26JiHXAOshy6AXLeAh3ka1MXgF7KKc066lIQN8GLJe0DJgmC9pvaL9I0nOAs4E3llrCDo5dvKjjIJa7yNYrr4A9oApL2BfiG05nC6ZcImIfcCWwFbgb2BgROyWtlrS65dJLgK9ExGODKeoB7iKbDU7VU5oeQ5tbob1cImILsKXt2Nq2x+uB9WUVbD7uIpsNTtVTmlXYM6Wqars5l7vI1g130Yurekqz6jecUfLSf0ueu+jdqXpKs4xppqlu9eCAbsmrek64aqqwhH0+/d5wUr7B1zblYlaUu+jdq3JKs98xtJRz8A7olrwycsLOwVdLPzeclG/wTrmULNXcXJ25i26tUt7qwQG9RH7jV1O/OeEiOXjfyOuj6oO+/XDKpUvzdb1Tzs3V3SC76HVYWWkHpLyOxQG9Cwu9cVPOzTXZQjl438jrp8qDvv1wyqULc71x373xDjbvmE46N9dkC3XRfSO3qnBA78Jcb9D9Ebxv052ce8p4srm5JlsoB+8buVWFUy5dmKvrDVlL/dZ7ZvjIpS9LMjfXdPN10a+64OSDUnHgG7mNhgN6Fzq9cVvteXRvsrk5m1vKg2xWLw7oXZh9g7574x3s7/Dh2u5iN5dv5FYFjQvo/a74m73WXWwzq5pGBfSy5gu7i90fL6M3G4xGBfQy5wu7i92bbm6qDvxm3WnUtEXPFx69olvZehsFs+41KqB7vvDoFb2peg9zs+41KqCnvClPXRS9qbo3Zda9RgX0qn8SSxMUvam6N2XWvUYNioIHM0et6Awhr740617jArqNXpGbqqeGmnXPAd0qy70ps+40KoduZpayQgFd0kpJ90raLenqOa45R9LtknZK+mq5xTQzs4UsmHKRNAZcB5wPTAHbJN0UEbtarlkMfBJYGREPSXregMprPfKqS7P0FcmhrwB2R8T9AJI2ABcBu1queQOwKSIeAoiI75RdUOudP/PSUuBGycKKpFyWAA+3PJ7Kj7U6CTha0t9L2i7pTZ2eSNIqSZOSJmdmZnorsXXNqy6t7rwVRDFFAro6HGvfDPww4HTgF4ALgA9IOumQb4pYFxETETExPj7edWGtN151aXXnRkkxRQL6FHBcy+OlwJ4O13w5Ih6LiEeArwGnlVNE65dXXVrduVFSTJGAvg1YLmmZpCOAy4Gb2q75AnCWpMMkHQmcAdxdblGtV97DxurOjZJiFgzoEbEPuBLYShakN0bETkmrJa3Or7kb+DLwL8A3gOsj4q7BFdu64T1srO7cKClG0eGzMYdhYmIiJicnR/Kzzax+PMslI2l7REx0Ouel/wb4zWLV560gFuaAbp6nbpYI7+VinhJmlgi30M1TwvrgVJVViQO6ceziRUx3CN6eEja/QaWqfJOwXjnlYp4S1qNBpKq8xN364Ra6+dOBejSIVNV8Nwm/HvU36N5XrQK6u6Ll8u+zP4NIVXk8I13DmE1Wm5SLu6LlqvLvc/OOac5ccwvLrv4iZ665pRJl6mQQqSovcU/XMGaT1Sage2pduar6+6zyjabdILZU8HhGuobR+6pNysVd0XJV9fdZtxxy2asXPZ6RrmHMJqtNQPfUunJV9fdZ1RvNMHmJe5quuuDkg3LoUH7vqzYpF3dFy1XV36dzyJaqYex6WpsWurui5arq73MYrRizURl078vb51rleDql2dy8fa7VinPIZr2pTQ7dzMzm5xa6mVmXqpoWdEA3M+tClT8QxikXM7MuVHWVNTigm5l1pcqL3xzQzcy6UOXFbw7oZmZdqOoqa/CgqJlZV6q6yhoKBnRJK4GPA2PA9RGxpu38OcAXgAfyQ5si4pryimlmVh1VXfy2YECXNAZcB5wPTAHbJN0UEbvaLv16RPziAMpoZmYFFGmhrwB2R8T9AJI2ABcB7QHdCqjqggQzq78ig6JLgIdbHk/lx9q9WtIdkr4k6SWdnkjSKkmTkiZnZmZ6KG691enTeMysfooEdHU41r5F4zeBEyLiNOATwOZOTxQR6yJiIiImxsfHuypoCqq8IMHM6q9IymUKOK7l8VJgT+sFEfGDlq+3SPqkpGMi4pFyipmGKi9IsMFyqq2+6vTaFWmhbwOWS1om6QjgcuCm1gskvUCS8q9X5M/73bILW3dVXpBgg+NUW33V7bVbMKBHxD7gSmArcDewMSJ2SlotaXV+2WXAXZLuAP4MuDxG9ckZFVblBQk2OE611VfdXrtC89AjYguwpe3Y2pavrwWuLbdo6anyggQbHKfa6qtur51Xig5ZVRck2OAcu3gR0x0CgFNt1Ve31857uZgNmFNt9VW3184tdLMBc6qtvur22mlUY5cTExMxOTk5kp9tZlZXkrZHxESnc065mJklwgHdzCwRDuhmZolwQDczS4RnuZhZKeq050mqHNDNrG+ze57MLpOf3fMEcFAfIqdczKxvddvzJFUO6GbWt7rteZIqB3Qz65u3hq4GB3Qz61vd9jxJlQdFzaxvddvzJFUO6GZWCm8NPXpOuZiZJcIB3cwsEQ7oZmaJcEA3M0uEA7qZWSIc0M3MEuGAbmaWCM9DNzPrQ5W2DS7UQpe0UtK9knZLunqe6/6jpP2SLiuviGZm1TS7bfD0o3sJDmwbvHnH9EjKs2BAlzQGXAe8FjgVuELSqXNc94fA1rILaWZWRVXbNrhIC30FsDsi7o+IJ4ENwEUdrnsHcCPwnRLLZ2ZWWVXbNrhIQF8CPNzyeCo/9mOSlgCXAGvneyJJqyRNSpqcmZnptqxmZpVStW2DiwR0dTgWbY8/Brw3IvZ3uPbAN0Wsi4iJiJgYHx8vWEQzs2qq2rbBRWa5TAHHtTxeCuxpu2YC2CAJ4Bjg5yXti4jNZRTSrK6qNAPCyle1bYOLBPRtwHJJy4Bp4HLgDa0XRMSy2a8lrQdudjC3pvMHJzdDlbYNXjDlEhH7gCvJZq/cDWyMiJ2SVktaPegCmtVV1WZAWPoKLSyKiC3AlrZjHQdAI+It/RfLrP6qNgPC0uel/2YDUrUZEJY+B3SzAanaDAhLn/dyMRuQqs2AsPQ5oJsNUJVmQFj6nHIxM0uEA7qZWSIc0M3MEuGAbmaWCA+KDoH38zCzYXBAHzDv52Fmw+KUy4B5Pw8zGxYH9AHzfh5mNiwO6APm/TzMbFgc0AfM+3mY2bB4UHTAvJ+HmQ2LA/oQeD8PMxsGp1zMzBLhgG5mlggHdDOzRDigm5klQhExmh8szQAPlvR0xwCPlPRcddLEejexztDMejexzrBwvU+IiPFOJ0YW0MskaTIiJkZdjmFrYr2bWGdoZr2bWGfor95OuZiZJcIB3cwsEakE9HWjLsCINLHeTawzNLPeTawz9FHvJHLoZmaWTgvdzKzxHNDNzBLhgG5WAZI06jKMQlPrPSi1CeiSJiQ9b9TlGDZJPyvp9FGXY5gkPafl66a84Zu68+nhoy7AqEgaW/iq7lQ+oEt6iaR/BH4fWDzi4gyNpFdK+hLweeBFoy7PMEg6Q9IXgOslvU3ST0Tio/aSXiXpfwDXSFo+iDd5FUl6taTPAX8k6dSG1fsagIjYv9D13ap8QAfeBXw+Ii6MiPsg7VabpDFJ64C/BP4C+J/Ai/NzdXi9eiLp5cB1wN8AnwPOI/EbmaSXAp8Abga+DawC3pSfS/lv/HnAtcAWsiXu7wLelp9Lud5vBv4a+G+SXp8fK7VnVtkAkQe25wJB9uIj6RJJS4FF+ePkXvz8rv1l4KyI2AzcCJwr6ZkR8fRICzdYpwO7I+K/A/8LeCbw0OzJFF9r4FXAPRHxWbIb+OPAr0o6MSIi0ToDnAbcFxE3AH8MbAIuknRS4vWeJmuorCSrNxGxr8z6ViqgSzpb0hnw48D2OPAzwHmSPgO8Hfgw8LH8miS64631BoiITRGxN3+hnwbuA44cWQEHoL3OwBeBSyT9AXAnsBT4M0nvhTRe6w513gYcJ+mFEfEY2Wv9feA3II06A0i6WNL7Jf1Cfuh2YKKl3tuASbL3d4r1/sX80K3AtyPiK8CDkj6UHy+tlV6JgC7pKEmbyPLFb5d0NEBE/Ai4gawrvjUiVgK/B7xU0mtHVuCSzFVv5fI/7HuA15C1WGvfUp3ntf4OWcvtMOD9EfEqYD3wnyW9elTlLUOHOj83P/Ut4BvADZI2AxNk6abDJD1zJIUtkaTxvF6/A3yPrJ6XRcQMWc/zHfmljwJ/Cxwp6adHUdYydaj3pyRdkjdSZ9+/bwfeKen5EfFUWT+7EgEdeBK4BXgjsAd4Xcu5T5KlWMYBImIa+Aey1kzddax35CQ9IyKmgP8NXDZ7blSFLcmcr3VE3AOcAjycH9oOfAd4YshlLNtcr/MPI+I9wJXA+oi4ENgNvDxvzNTdC4HbIuJnImIt8G7gt/NznwVOkfSaPJX4XWAJWQ+l7jrV+yqAiHhS0lhE7CS7ea8BKKuBOrKALulNeRd0cUQ8AVxPdpe+j6w7dhJkf/Rkd/I3S3qFpN8Efhb4PyMqel+K1jsP5k/ngyb/Cjw2ulL3p2idc18BPpj3RC4HXkL2Zq+VBep8emudI+Jf8vESyHKs/1zXnlhe73MkHUl2Q/50fnwM2JX/gyyttgH4uKQXkfVCBRwx/FL3r0C978wfi2xckIj4dbK49v+B08qY9DDUvVzyyryAbObG02Rdzp8E3hURj+TXLAfeDPwoIj7c8r2/QtYlfwlZl3zn0Arep17r3RLU/xT4YUR8YCQV6EGXdX4iIj6UH1tEtjnR84Ax4J0RsevQn1A9ff59n042ULYfWBUR3xpy8Xu2UL3zFul+SW8EfikiXt/yve8BTiLrmf1GRNw9/Br0ps96nwD8KfBTwG9FxF2lFCoihvIPGMv/Pwn4TP71YWTTtm5su/YSslTLi/Jf0OH5cQ2rvBWo9zOBn6xjvXus83LgyJZrXzDqegzpdV6UH/sp4OxR16Pkem9qu+bTwOvzr1/Q8hxHjLoeQ6z3eP7/YmBF2eUa+Oq0PGVwDTAmaQvwbLJWCJFN2XknsEfS2RHx1fz45yW9mGz63rOAc4G7I/9N1EET691nnb8EPEvSuZG10v7faGrRnTJeZ0nnRdYL+epoatG9XuoN/BB4QNnCmkslrYyIqYh4chR16EVJ9f75iHiIbEC8VAPNoUs6myyfdDTZYM+HgKfI5lWvgB8P8l0DfLDl+15HNpvlVrIBotp0w6CZ9Xad+6pzLVJKs3qpd55LfhvZwrFnA+dGNuBfGyXW+6FDnrwsA+6WnAX8WsvjTwK/CbwF2J4fewZZHmojsKzl+84aVvfJ9XadXeeB1vsEspkfHwP+w6jLn3K9Bz3LZTuwUQf2abgNOD4i1pN1Wd4R2ZSlpcD+iHgAICK+HhFfH3DZBqmJ9Xadm1Fn6K7eT0fEgxHxrYj4rxHxzRGVuQyVr/dAA3pEPB4RT8SBTWjOB2byr98KvFjSzWRzUuv8Qh+kifV2nYEG1Bm6rvd2qP+COKhHvYeyZWd+Rwvg+cBN+eF/A94PvBR4ILIFQ0lpYr1d52bUGbqrd+T5iBRUud7DWlj0NNm+x48AL8/vYh8g65b8Q4p/7Lkm1tt1bkadwfWuXL2HtrBI0quAf8z/3RARfzWUHzxiTay369yMOoPrTcXqPcyAvhT4NeBPIlsK3QhNrLfr3Iw6g+tNxeo91KX/ZmY2OFXZbdHMzPrkgG5mlggHdDOzRDigm5klwgHdzCwRDujWWJI+KOl35zl/saRTh1kms344oJvN7WLAAd1qw/PQrVEk/R7wJrIPop4h20Tp+8Aqss+z3E22YOQVwM35ue8Dv5w/xXVkH1j+ONlHpt0zxOKbzcsB3Roj/9zO9cAZZBvTfRNYS7Z0+7v5NR8Gvh0Rn5C0Hrg5Iv4mP/d3wOqI+FdJZwAfiYjzhl8Ts86GstuiWUWcBXw+Ih4HkDS7U95L80C+mOyj/7a2f6OkZwH/Cfhcy46oPzHoApt1wwHdmqZTl3Q9cHFE3CHpLcA5Ha55BvBoRLxiYCUz65MHRa1JvgZcImmRpKOAC/PjRwH/V9LhwK+2XP9v+Tki4gdkH/T7Osg+uEDSacMrutnCnEO3RmkZFH0QmAJ2AY8B78mP3QkcFRFvkXQm8JfAE8BlZPtg/znw02T7YW+IiGuGXgmzOTigm5klwikXM7NEOKCbmSXCAd3MLBEO6GZmiXBANzNLhAO6mVkiHNDNzBLx7+dUZncXfa7gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ef = pd.DataFrame(date_val).set_index('date').astype(float) \n",
    "df_ef = df_ef[df_ef.EF < 1] \n",
    "df_ef.EF.plot(style='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c941a878-68bf-4092-b031-32c00329f498",
   "metadata": {},
   "source": [
    "## upscaling of instantaneous LE to daily ET\n",
    "\n",
    "This is done on the assumption of constant ratio of LE to available energy (Rn - G, net radiation minus ground heat flux) throughout a day\n",
    "\n",
    "$\\dfrac{LE_{inst}}{(RN_{inst} - G_{inst})} \\approx \\dfrac{LE_{24}}{(RN_{24} - G_{24})} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f6364b04-c4f8-49e7-96b5-270772f07c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(df_ef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4339c006-7db2-469f-99e0-1edc8add3352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LAI', 'TIMESTAMP', 'SW_IN_F', 'LW_IN_F', 'PA_F', 'TA_F', 'WS_F',\n",
       "       'VPD_F', 'CO2_F_MDS', 'RN', 'SW_IN_ERA', 'LW_IN_ERA', 'PA_ERA',\n",
       "       'TA_ERA', 'WS_ERA', 'VPD_ERA', 'P_ERA', 'SWC_F_MDS_1', 'H_F_MDS',\n",
       "       'LE_F_MDS', 'G_F_MDS', 'GPP_NT_VUT_USTAR50', 'LE_CORR', 'H_CORR',\n",
       "       'SW_OUT', 'LW_OUT', 'dmp_24', 'et_24', 'ta_stress', 'vpd_stress',\n",
       "       'NDVI_median', 'gpp', 'gpp_umol', 'EF', 'SWF'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dc4d39cc-bfbb-4cff-8541-c3ecf85827eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LE_ef'] = df.RN * df.EF\n",
    "# df['LE_swf'] = df.SW_IN_F * df.SWF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a2a8d722-d73f-4fed-b1b5-3e44706f007b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2568ce8ef40>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot(data=df, x='LE_F_MDS', y='LE_ef')\n",
    "lims = [0, 150]\n",
    "g.axes.flatten()[0].plot(lims, lims, 'k:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6a6dc438-36f7-44c8-91bd-903ae7fff7d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.LE_F_MDS.plot()\n",
    "df.LE_ef.plot(style='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f9ecfb-0aa6-43ab-acd0-f4c2ececf805",
   "metadata": {},
   "source": [
    "## gap-fill ET (on the premise of EF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2a4381b1-b356-4647-95f6-392d93a90762",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LE_ef_filled'] = df.EF.interpolate() * df.RN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "79620409-dd88-4e15-a72c-3968db60edf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.LE_F_MDS.plot()\n",
    "df.LE_ef.interpolate().plot()\n",
    "df.LE_ef_filled.interpolate().plot(alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4f80e7ee-f778-438f-9bbd-5d421e946cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x256869b98e0>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot(data=df, x='LE_F_MDS', y='LE_ef_filled')\n",
    "lims = [0, 150]\n",
    "g.axes.flatten()[0].plot(lims, lims, 'k:')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8e3c63-6320-4131-807e-ce76ebb50059",
   "metadata": {},
   "source": [
    "# 5. Computation of the Earth-Observation-based WUE\n",
    "\n",
    "same as point 2 but with simulated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b1003a85-998b-4b9a-9d3c-fe43c53ee80a",
   "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>Plot ID</th>\n",
       "      <th>Site name</th>\n",
       "      <th>Area (ha)</th>\n",
       "      <th>Season</th>\n",
       "      <th>crop</th>\n",
       "      <th>planting</th>\n",
       "      <th>harvesting</th>\n",
       "      <th>los</th>\n",
       "      <th>dmp_sum</th>\n",
       "      <th>et_sum</th>\n",
       "      <th>wue_dmp</th>\n",
       "      <th>yield</th>\n",
       "      <th>wue</th>\n",
       "      <th>dmp_lue_sum</th>\n",
       "      <th>et_tseb_sum</th>\n",
       "      <th>wue_dmp_eo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>2015-09-08</td>\n",
       "      <td>126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2015-10-20</td>\n",
       "      <td>2016-07-20</td>\n",
       "      <td>274</td>\n",
       "      <td>18520.225819</td>\n",
       "      <td>483.582059</td>\n",
       "      <td>38.298000</td>\n",
       "      <td>9369.055414</td>\n",
       "      <td>19.374282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>487.208457</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2015-2016</td>\n",
       "      <td>White mustard+Vetch</td>\n",
       "      <td>2016-08-17</td>\n",
       "      <td>2016-11-16</td>\n",
       "      <td>91</td>\n",
       "      <td>858.693106</td>\n",
       "      <td>110.192943</td>\n",
       "      <td>7.792632</td>\n",
       "      <td>434.397689</td>\n",
       "      <td>3.942155</td>\n",
       "      <td>961.103030</td>\n",
       "      <td>121.162487</td>\n",
       "      <td>7.932348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2016-2017</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>130</td>\n",
       "      <td>10753.378819</td>\n",
       "      <td>221.507608</td>\n",
       "      <td>48.546318</td>\n",
       "      <td>7193.138534</td>\n",
       "      <td>32.473551</td>\n",
       "      <td>11079.302174</td>\n",
       "      <td>254.708742</td>\n",
       "      <td>43.497927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2017-2018</td>\n",
       "      <td>winter Wheat</td>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>236</td>\n",
       "      <td>13655.620142</td>\n",
       "      <td>266.338612</td>\n",
       "      <td>51.271650</td>\n",
       "      <td>6908.137248</td>\n",
       "      <td>25.937423</td>\n",
       "      <td>12544.192651</td>\n",
       "      <td>236.514919</td>\n",
       "      <td>53.037638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2018-2019</td>\n",
       "      <td>Irrigated corn</td>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>2019-09-09</td>\n",
       "      <td>140</td>\n",
       "      <td>19282.141457</td>\n",
       "      <td>297.150662</td>\n",
       "      <td>64.890118</td>\n",
       "      <td>12898.189218</td>\n",
       "      <td>43.406228</td>\n",
       "      <td>12979.945442</td>\n",
       "      <td>502.794292</td>\n",
       "      <td>25.815618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LAM</td>\n",
       "      <td>Lamasquère</td>\n",
       "      <td>24</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>winter Wheat</td>\n",
       "      <td>2019-10-19</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>258</td>\n",
       "      <td>14257.623345</td>\n",
       "      <td>256.637044</td>\n",
       "      <td>55.555594</td>\n",
       "      <td>7212.680045</td>\n",
       "      <td>28.104594</td>\n",
       "      <td>12745.396063</td>\n",
       "      <td>369.777092</td>\n",
       "      <td>34.467782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Plot ID   Site name  Area (ha)     Season                 crop   planting  \\\n",
       "0     LAM  Lamasquère         24  2014-2015       Irrigated corn 2015-05-05   \n",
       "1     LAM  Lamasquère         24  2015-2016                Wheat 2015-10-20   \n",
       "2     LAM  Lamasquère         24  2015-2016  White mustard+Vetch 2016-08-17   \n",
       "3     LAM  Lamasquère         24  2016-2017       Irrigated corn 2017-04-28   \n",
       "4     LAM  Lamasquère         24  2017-2018         winter Wheat 2017-11-16   \n",
       "5     LAM  Lamasquère         24  2018-2019       Irrigated corn 2019-04-22   \n",
       "6     LAM  Lamasquère         24  2019-2020         winter Wheat 2019-10-19   \n",
       "\n",
       "  harvesting  los       dmp_sum      et_sum    wue_dmp         yield  \\\n",
       "0 2015-09-08  126      0.000000    0.000000        NaN      0.000000   \n",
       "1 2016-07-20  274  18520.225819  483.582059  38.298000   9369.055414   \n",
       "2 2016-11-16   91    858.693106  110.192943   7.792632    434.397689   \n",
       "3 2017-09-05  130  10753.378819  221.507608  48.546318   7193.138534   \n",
       "4 2018-07-10  236  13655.620142  266.338612  51.271650   6908.137248   \n",
       "5 2019-09-09  140  19282.141457  297.150662  64.890118  12898.189218   \n",
       "6 2020-07-03  258  14257.623345  256.637044  55.555594   7212.680045   \n",
       "\n",
       "         wue   dmp_lue_sum  et_tseb_sum  wue_dmp_eo  \n",
       "0        NaN      0.000000     0.000000         NaN  \n",
       "1  19.374282      0.000000   487.208457    0.000000  \n",
       "2   3.942155    961.103030   121.162487    7.932348  \n",
       "3  32.473551  11079.302174   254.708742   43.497927  \n",
       "4  25.937423  12544.192651   236.514919   53.037638  \n",
       "5  43.406228  12979.945442   502.794292   25.815618  \n",
       "6  28.104594  12745.396063   369.777092   34.467782  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dmp_lue_24'] = df.gpp_umol * (24*60*60) * (12+2+16) * 1e-6 * 1e-3 * 1e4 * 0.5  # kg DM ha-1 day-1\n",
    "df['et_tseb_24'] = df.LE_ef_filled * 0.035  # mm day-1\n",
    "\n",
    "for i, row in df_dates.iterrows():\n",
    "    df_dates.loc[i, 'dmp_lue_sum'] = df.loc[row.planting:row.harvesting, 'dmp_lue_24'].sum()\n",
    "    df_dates.loc[i, 'et_tseb_sum'] = df.loc[row.planting:row.harvesting, 'et_tseb_24'].sum()\n",
    "\n",
    "df_dates['wue_dmp_eo'] = df_dates.dmp_lue_sum / df_dates.et_tseb_sum\n",
    "df_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f67bd8d-05f3-4993-ab1c-62bd3ca0bbf4",
   "metadata": {},
   "source": [
    "the magic of an outlier ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "43da1a78-a988-4342-b06f-e8a5d0158fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25686bdbd30>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot(data=df_dates[df_dates.dmp_lue_sum != 0], x='wue_dmp', y='wue_dmp_eo', ci=False)\n",
    "lims = [0, 70]\n",
    "g.axes.flatten()[0].plot(lims, lims, 'k:')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}