Wapor_NPP_TBP.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/Title_ex_6.4.2.png)\n",
    "\n",
    "# 1. Introduction\n",
    "The FAO Water Productivity Open-access portal (WaPOR) is a public and free available database that contains earth observation products to monitor agricultural water productivity over Africa. The product portfolio contains information on weather conditions (e.g. Precipitation), water availability (e.g. Evapotranspiration) and land charachteristics (e.g. Land Cover and Primary/Biomass Production). The datasets are available from 2009 onwards at three different levels: \n",
    "- Level I at continental level (250 m ground resolution): Africa & Middle East\n",
    "- Level II at country and river basin level (100 m ground resolution): Selection of African countries\n",
    "- Level III at irrigation scheme and sub-basin level (30 m ground resolution): Selection \n",
    "More information on the WaPOR database and data availability can be found here: \n",
    "https://wapor.apps.fao.org/catalog/WAPOR_2/1\n",
    "\n",
    "The WaPOR database can be accessed via webservices. In this excercise we will explore API acccess via a Jupyter Notebook. More explanation and examples can be found here: https://github.com/wateraccounting/WAPORWP \n",
    "\n",
    "\n",
    "The two WaPOR products which we will investigate in this Notebook are Net Primary Production (NPP) and Total Biomass Production (TBP). \n",
    "- Net Primary Production (NPP) is a fundamental ecosystem variable that represent the part of Gross Primary Production (GPP or photosynthesis) that is available for biomass growth. In WaPOR it is derived from a Light Use Efficiency (LUE) model that combines information on fAPAR, solar radiation and temperature. To account for drought stressed NPP, a soil moisture reduction factor is added to the model. NPP is available at 10-daily timestep and can be used to track seasonal dynamics of biomass production.\n",
    "- Total biomass production (TBP) is defined as the sum of the dry matter produced during the growing season. The delineation of the growing season is derived at pixel level from time series of satellite based vegetation indices. TBP can be related to seasonal crop production or pasture availability. \n",
    "\n",
    "The detailed product and algorithm description is provided in the Methodology Report of WaPOR (Chapter 2.1.6, avialable here https://www.fao.org/3/ca9894en/CA9894EN.pdf). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Loading libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. General exploration of FAO workspace & WAPOR catalog\n",
    "## 2.1 Exploration of available FAO workspaces\n",
    "We will first connect to the FAO webservices, select the WAPOR catalog and explore the products. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>caption</th>\n",
       "      <th>description</th>\n",
       "      <th>links</th>\n",
       "      <th>additionalInfo</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>AQUAMAPS</td>\n",
       "      <td>Global spatial database on water and agriculture</td>\n",
       "      <td>Regional and global spatial datasets on water ...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ASIS</td>\n",
       "      <td>Agriculture Stress Index System</td>\n",
       "      <td>ASIS allows countries to fine-tune parameters ...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>C2ATLAS</td>\n",
       "      <td>Climate Change ATLAS</td>\n",
       "      <td>C2ATLAS (Climate Change ATLAS - CSIC).The Clim...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>C3S</td>\n",
       "      <td>Copernicus Climate Change Service</td>\n",
       "      <td>The Copernicus Climate Change Service (C3S) wi...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>CHIRPS</td>\n",
       "      <td>Climate Hazard group InfraRed Precipitation wi...</td>\n",
       "      <td>Climate Hazard group InfraRed Precipitation wi...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>CROPWATCH</td>\n",
       "      <td>CropWatch</td>\n",
       "      <td>Cropwatch is China's leading agricultural moni...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'creationDate': '1998-01-01', 'link': 'https:...</td>\n",
       "      <td>[{'name': 'CROP'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>CRTB</td>\n",
       "      <td>Climate Risk Toolbox (CRTB)</td>\n",
       "      <td>Climate Risk Toolbox (CRTB)</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'CRTB'}, {'name': 'ASSESSMENT'}, {'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>DLMF</td>\n",
       "      <td>Desert Locust Monitoring and Forecasting</td>\n",
       "      <td>DLMF-Desert Locust monitoring and forecasting....</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'LOCUST'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>FAOSTAT</td>\n",
       "      <td>FAO Corporate Statistical Database</td>\n",
       "      <td>FAOSTAT provides free access to food and agric...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>FROM_GLC</td>\n",
       "      <td>Finer Resolution Observation and Monitoring of...</td>\n",
       "      <td>Global land cover data are key sources of info...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'LCC'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>GAEZ_2015</td>\n",
       "      <td>Global Agro-Ecological Zones (2015)</td>\n",
       "      <td>The Global Agro-Ecological Zones (GAEZ) databa...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>GAEZ_V4</td>\n",
       "      <td>Global Agro-Ecological Zones (2021)</td>\n",
       "      <td>The Global Agro-Ecological Zones (GAEZ) databa...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'created': '2021 April 29', 'site': 'https://...</td>\n",
       "      <td>[{'name': 'GAEZ'}, {'name': 'AEZ'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>GISMGR_TEST</td>\n",
       "      <td>FAO GIS MANAGER (GISMGR) - Test and training w...</td>\n",
       "      <td>FAO GISMGR Workspace for geospatial datasets</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'created': '2021 Oct 15', 'wiki': 'https://en...</td>\n",
       "      <td>[{'name': 'TEST'}, {'name': 'L1', 'index': 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>GLEAM3</td>\n",
       "      <td>Global Livestock Environmental Assessment Mode...</td>\n",
       "      <td>Global Livestock Environmental Assessment Model</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'GLEAM'}, {'name': 'LIVESTOCK'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>GLW</td>\n",
       "      <td>Gridded Livestock of the World</td>\n",
       "      <td>Gridded Livestock of the World (GLW) is a peer...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>GLW3</td>\n",
       "      <td>Gridded Livestock of the World (GLW3, 2010)</td>\n",
       "      <td>This database contains peer-reviewed spatial d...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'LIVESTOCK'}, {'name': 'GLW3'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>GLW4</td>\n",
       "      <td>Gridded Livestock of the World (GLW4, 2015)</td>\n",
       "      <td>This database contains peer-reviewed spatial d...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'LIVESTOCK'}, {'name': 'GLW3'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>NASA</td>\n",
       "      <td>National Aeronautics and Space Administration ...</td>\n",
       "      <td>The National Aeronautics and Space Administrat...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>NATURAL_EARTH</td>\n",
       "      <td>Natural Earth</td>\n",
       "      <td>Natural Earth is a public domain map dataset a...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>NMME</td>\n",
       "      <td>North American Multi-Model Ensemble (NMME)</td>\n",
       "      <td>The North American Multi-Model Ensemble (NMME)...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>RDMS</td>\n",
       "      <td>Regional Drought Monitoring System</td>\n",
       "      <td>Regional Drought Monitoring System for Middle ...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>RICCAR</td>\n",
       "      <td>Regional Arab Climate Change Assessment Report</td>\n",
       "      <td>The Regional Initiative for the Assessment of ...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'tagContexts': {'LEVELS_LIST': [{'name': 'L1'...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>RICCAR_2</td>\n",
       "      <td>Regional Arab Climate Change Assessment Report</td>\n",
       "      <td>The Regional Initiative for the Assessment of ...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>RSCROP</td>\n",
       "      <td>Crop Pest and Disease Monitoring and Forecasti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'site': 'http://en.rscrop.com/'}</td>\n",
       "      <td>[{'name': 'CROP'}, {'name': 'PEST'}, {'name': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>RVF</td>\n",
       "      <td>Rift Valley Fever</td>\n",
       "      <td>Rift Valley Fever</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>FAO Water Productivity Open-access portal (WaPOR)</td>\n",
       "      <td>The FAO Water Productivity Open-access portal ...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>{'tagContexts': {'levels': [{'name': 'L1', 'la...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>WATER</td>\n",
       "      <td>Water Data</td>\n",
       "      <td>Water data, including water resources, agricul...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'WATER'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>WPOP</td>\n",
       "      <td>WorldPop project</td>\n",
       "      <td>The WorldPop project was initiated in October ...</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             code                                            caption  \\\n",
       "0        AQUAMAPS   Global spatial database on water and agriculture   \n",
       "1            ASIS                    Agriculture Stress Index System   \n",
       "2         C2ATLAS                               Climate Change ATLAS   \n",
       "3             C3S                  Copernicus Climate Change Service   \n",
       "4          CHIRPS  Climate Hazard group InfraRed Precipitation wi...   \n",
       "5       CROPWATCH                                          CropWatch   \n",
       "6            CRTB                        Climate Risk Toolbox (CRTB)   \n",
       "7            DLMF           Desert Locust Monitoring and Forecasting   \n",
       "8         FAOSTAT                 FAO Corporate Statistical Database   \n",
       "9        FROM_GLC  Finer Resolution Observation and Monitoring of...   \n",
       "10      GAEZ_2015                Global Agro-Ecological Zones (2015)   \n",
       "11        GAEZ_V4                Global Agro-Ecological Zones (2021)   \n",
       "12    GISMGR_TEST  FAO GIS MANAGER (GISMGR) - Test and training w...   \n",
       "13         GLEAM3  Global Livestock Environmental Assessment Mode...   \n",
       "14            GLW                     Gridded Livestock of the World   \n",
       "15           GLW3        Gridded Livestock of the World (GLW3, 2010)   \n",
       "16           GLW4        Gridded Livestock of the World (GLW4, 2015)   \n",
       "17           NASA  National Aeronautics and Space Administration ...   \n",
       "18  NATURAL_EARTH                                      Natural Earth   \n",
       "19           NMME         North American Multi-Model Ensemble (NMME)   \n",
       "20           RDMS                 Regional Drought Monitoring System   \n",
       "21         RICCAR     Regional Arab Climate Change Assessment Report   \n",
       "22       RICCAR_2     Regional Arab Climate Change Assessment Report   \n",
       "23         RSCROP  Crop Pest and Disease Monitoring and Forecasti...   \n",
       "24            RVF                                  Rift Valley Fever   \n",
       "25        WAPOR_2  FAO Water Productivity Open-access portal (WaPOR)   \n",
       "26          WATER                                         Water Data   \n",
       "27           WPOP                                   WorldPop project   \n",
       "\n",
       "                                          description  \\\n",
       "0   Regional and global spatial datasets on water ...   \n",
       "1   ASIS allows countries to fine-tune parameters ...   \n",
       "2   C2ATLAS (Climate Change ATLAS - CSIC).The Clim...   \n",
       "3   The Copernicus Climate Change Service (C3S) wi...   \n",
       "4   Climate Hazard group InfraRed Precipitation wi...   \n",
       "5   Cropwatch is China's leading agricultural moni...   \n",
       "6                         Climate Risk Toolbox (CRTB)   \n",
       "7   DLMF-Desert Locust monitoring and forecasting....   \n",
       "8   FAOSTAT provides free access to food and agric...   \n",
       "9   Global land cover data are key sources of info...   \n",
       "10  The Global Agro-Ecological Zones (GAEZ) databa...   \n",
       "11  The Global Agro-Ecological Zones (GAEZ) databa...   \n",
       "12       FAO GISMGR Workspace for geospatial datasets   \n",
       "13    Global Livestock Environmental Assessment Model   \n",
       "14  Gridded Livestock of the World (GLW) is a peer...   \n",
       "15  This database contains peer-reviewed spatial d...   \n",
       "16  This database contains peer-reviewed spatial d...   \n",
       "17  The National Aeronautics and Space Administrat...   \n",
       "18  Natural Earth is a public domain map dataset a...   \n",
       "19  The North American Multi-Model Ensemble (NMME)...   \n",
       "20  Regional Drought Monitoring System for Middle ...   \n",
       "21  The Regional Initiative for the Assessment of ...   \n",
       "22  The Regional Initiative for the Assessment of ...   \n",
       "23                                                NaN   \n",
       "24                                  Rift Valley Fever   \n",
       "25  The FAO Water Productivity Open-access portal ...   \n",
       "26  Water data, including water resources, agricul...   \n",
       "27  The WorldPop project was initiated in October ...   \n",
       "\n",
       "                                                links  \\\n",
       "0   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "1   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "2   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "3   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "4   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "5   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "6   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "7   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "8   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "9   [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "10  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "11  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "12  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "13  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "14  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "15  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "16  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "17  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "18  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "19  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "20  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "21  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "22  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "23  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "24  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "25  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "26  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "27  [{'rel': 'self', 'href': 'https://io.apps.fao....   \n",
       "\n",
       "                                       additionalInfo  \\\n",
       "0                                                 NaN   \n",
       "1                                                 NaN   \n",
       "2                                                 NaN   \n",
       "3                                                 NaN   \n",
       "4                                                 NaN   \n",
       "5   {'creationDate': '1998-01-01', 'link': 'https:...   \n",
       "6                                                 NaN   \n",
       "7                                                 NaN   \n",
       "8                                                 NaN   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11  {'created': '2021 April 29', 'site': 'https://...   \n",
       "12  {'created': '2021 Oct 15', 'wiki': 'https://en...   \n",
       "13                                                NaN   \n",
       "14                                                NaN   \n",
       "15                                                NaN   \n",
       "16                                                NaN   \n",
       "17                                                NaN   \n",
       "18                                                NaN   \n",
       "19                                                NaN   \n",
       "20                                                NaN   \n",
       "21  {'tagContexts': {'LEVELS_LIST': [{'name': 'L1'...   \n",
       "22                                                NaN   \n",
       "23                  {'site': 'http://en.rscrop.com/'}   \n",
       "24                                                NaN   \n",
       "25  {'tagContexts': {'levels': [{'name': 'L1', 'la...   \n",
       "26                                                NaN   \n",
       "27                                                NaN   \n",
       "\n",
       "                                                 tags  \n",
       "0                                                 NaN  \n",
       "1                                                 NaN  \n",
       "2                                                 NaN  \n",
       "3                                                 NaN  \n",
       "4                                                 NaN  \n",
       "5                                  [{'name': 'CROP'}]  \n",
       "6   [{'name': 'CRTB'}, {'name': 'ASSESSMENT'}, {'n...  \n",
       "7                                [{'name': 'LOCUST'}]  \n",
       "8                                                 NaN  \n",
       "9                                   [{'name': 'LCC'}]  \n",
       "10                                                NaN  \n",
       "11                [{'name': 'GAEZ'}, {'name': 'AEZ'}]  \n",
       "12  [{'name': 'TEST'}, {'name': 'L1', 'index': 1, ...  \n",
       "13         [{'name': 'GLEAM'}, {'name': 'LIVESTOCK'}]  \n",
       "14                                                NaN  \n",
       "15          [{'name': 'LIVESTOCK'}, {'name': 'GLW3'}]  \n",
       "16          [{'name': 'LIVESTOCK'}, {'name': 'GLW3'}]  \n",
       "17                                                NaN  \n",
       "18                                                NaN  \n",
       "19                                                NaN  \n",
       "20                                                NaN  \n",
       "21                                                NaN  \n",
       "22                                                NaN  \n",
       "23  [{'name': 'CROP'}, {'name': 'PEST'}, {'name': ...  \n",
       "24                                                NaN  \n",
       "25                                                NaN  \n",
       "26                                [{'name': 'WATER'}]  \n",
       "27                                                NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In this cell a connection is made to the FAO API services and a list is printed of all available workspaces. \n",
    "\n",
    "workspace='WAPOR_2'\n",
    "\n",
    "overview=False\n",
    "paged=False\n",
    "overview=False\n",
    "paged=False\n",
    "url_workspaces= f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces?overview={overview}&paged={paged}'\n",
    "resp_workspaces=requests.get(url_workspaces) #Get response\n",
    "resp_dict_workspaces=resp_workspaces.json() #read response JSON string \n",
    "df_workspaces=pd.DataFrame.from_dict(resp_dict_workspaces['response'])\n",
    "df_workspaces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 WAPOR Catalog exploration\n",
    "Next we will list all available products in the WaPOR catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>caption</th>\n",
       "      <th>description</th>\n",
       "      <th>additionalInfo</th>\n",
       "      <th>tags</th>\n",
       "      <th>workspaceCode</th>\n",
       "      <th>dataType</th>\n",
       "      <th>index</th>\n",
       "      <th>operation</th>\n",
       "      <th>hidden</th>\n",
       "      <th>links</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>L1_GBWP_A</td>\n",
       "      <td>Gross Biomass Water Productivity</td>\n",
       "      <td>The annual Gross Biomass Water Productivity ex...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/m³ is...</td>\n",
       "      <td>[{'name': 'L1', 'index': 1, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>101</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>L1_NBWP_A</td>\n",
       "      <td>Net Biomass Water Productivity</td>\n",
       "      <td>The annual Net Biomass Water Productivity expr...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/m³ is...</td>\n",
       "      <td>[{'name': 'L1', 'index': 2, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>102</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>L1_AETI_A</td>\n",
       "      <td>Actual EvapoTranspiration and Interception (An...</td>\n",
       "      <td>The actual EvapoTranspiration and Interception...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
       "      <td>[{'name': 'L1', 'index': 3, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>103</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>L1_AETI_M</td>\n",
       "      <td>Actual EvapoTranspiration and Interception (Mo...</td>\n",
       "      <td>The actual EvapoTranspiration and Interception...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
       "      <td>[{'name': 'L1', 'index': 4, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>104</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>L1_AETI_D</td>\n",
       "      <td>Actual EvapoTranspiration and Interception (De...</td>\n",
       "      <td>The actual EvapoTranspiration and Interception...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'mm', 'da...</td>\n",
       "      <td>[{'name': 'L1', 'index': 5, 'context': 'LEVELS...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>105</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>280</td>\n",
       "      <td>L3_GEZ_QUAL_NDVI_LT</td>\n",
       "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
       "      <td>The long-term NDVI quality layer is produced a...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
       "      <td>[{'name': 'L3', 'index': 270, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30270</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>281</td>\n",
       "      <td>L3_KOG_QUAL_NDVI_LT</td>\n",
       "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
       "      <td>The long-term NDVI quality layer is produced a...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
       "      <td>[{'name': 'L3', 'index': 271, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30271</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>282</td>\n",
       "      <td>L3_LAM_QUAL_NDVI_LT</td>\n",
       "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
       "      <td>The long-term NDVI quality layer is produced a...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
       "      <td>[{'name': 'L3', 'index': 273, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30273</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>283</td>\n",
       "      <td>L3_ODN_QUAL_NDVI_LT</td>\n",
       "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
       "      <td>The long-term NDVI quality layer is produced a...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
       "      <td>[{'name': 'L3', 'index': 276, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30276</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>284</td>\n",
       "      <td>L3_ZAN_QUAL_NDVI_LT</td>\n",
       "      <td>Quality of Normalized Difference Vegetation In...</td>\n",
       "      <td>The long-term NDVI quality layer is produced a...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': '%', 'dat...</td>\n",
       "      <td>[{'name': 'L3', 'index': 280, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30280</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>285 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    code                                            caption  \\\n",
       "0              L1_GBWP_A                   Gross Biomass Water Productivity   \n",
       "1              L1_NBWP_A                     Net Biomass Water Productivity   \n",
       "2              L1_AETI_A  Actual EvapoTranspiration and Interception (An...   \n",
       "3              L1_AETI_M  Actual EvapoTranspiration and Interception (Mo...   \n",
       "4              L1_AETI_D  Actual EvapoTranspiration and Interception (De...   \n",
       "..                   ...                                                ...   \n",
       "280  L3_GEZ_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
       "281  L3_KOG_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
       "282  L3_LAM_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
       "283  L3_ODN_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
       "284  L3_ZAN_QUAL_NDVI_LT  Quality of Normalized Difference Vegetation In...   \n",
       "\n",
       "                                           description  \\\n",
       "0    The annual Gross Biomass Water Productivity ex...   \n",
       "1    The annual Net Biomass Water Productivity expr...   \n",
       "2    The actual EvapoTranspiration and Interception...   \n",
       "3    The actual EvapoTranspiration and Interception...   \n",
       "4    The actual EvapoTranspiration and Interception...   \n",
       "..                                                 ...   \n",
       "280  The long-term NDVI quality layer is produced a...   \n",
       "281  The long-term NDVI quality layer is produced a...   \n",
       "282  The long-term NDVI quality layer is produced a...   \n",
       "283  The long-term NDVI quality layer is produced a...   \n",
       "284  The long-term NDVI quality layer is produced a...   \n",
       "\n",
       "                                        additionalInfo  \\\n",
       "0    {'format': 'Raster Dataset', 'unit': 'kg/m³ is...   \n",
       "1    {'format': 'Raster Dataset', 'unit': 'kg/m³ is...   \n",
       "2    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
       "3    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
       "4    {'format': 'Raster Dataset', 'unit': 'mm', 'da...   \n",
       "..                                                 ...   \n",
       "280  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
       "281  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
       "282  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
       "283  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
       "284  {'format': 'Raster Dataset', 'unit': '%', 'dat...   \n",
       "\n",
       "                                                  tags workspaceCode dataType  \\\n",
       "0    [{'name': 'L1', 'index': 1, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "1    [{'name': 'L1', 'index': 2, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "2    [{'name': 'L1', 'index': 3, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "3    [{'name': 'L1', 'index': 4, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "4    [{'name': 'L1', 'index': 5, 'context': 'LEVELS...       WAPOR_2   RASTER   \n",
       "..                                                 ...           ...      ...   \n",
       "280  [{'name': 'L3', 'index': 270, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "281  [{'name': 'L3', 'index': 271, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "282  [{'name': 'L3', 'index': 273, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "283  [{'name': 'L3', 'index': 276, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "284  [{'name': 'L3', 'index': 280, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "\n",
       "     index                                          operation  hidden  \\\n",
       "0      101  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "1      102  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "2      103  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "3      104  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "4      105  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "..     ...                                                ...     ...   \n",
       "280  30270       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "281  30271       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "282  30273       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "283  30276       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "284  30280       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "\n",
       "                                                 links  \n",
       "0    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "1    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "2    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "3    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "4    [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "..                                                 ...  \n",
       "280  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "281  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "282  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "283  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "284  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "\n",
       "[285 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This cell prints an overview of the first products and writes the entire list of products to a CSV file. \n",
    "\n",
    "workspace='WAPOR_2'\n",
    "overview=False\n",
    "paged=False\n",
    "\n",
    "url_catalog= f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes?overview={overview}&paged={paged}'\n",
    "resp_catalog=requests.get(url_catalog).json()\n",
    "df_catalog=pd.DataFrame.from_dict(resp_catalog['response'])\n",
    "outdir = './results'\n",
    "if not os.path.exists(outdir):\n",
    "    os.mkdir(outdir)\n",
    "df_catalog.to_csv(f'./results/{workspace}.csv')\n",
    "df_catalog\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 WAPOR Catalog filtering\n",
    "Next we will filter the WaPOR catalog for products that contain either NPP or TBP. The list of remaining products contains the products from three levels L1_ (250m continental), L2_ (100m national) & L3_ (30m areas). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <th>caption</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>L1_NPP_M</td>\n",
       "      <td>Net Primary Production (Monthly)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L1', 'index': 12, 'context': 'LEVEL...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>112</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
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       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>L1_NPP_D</td>\n",
       "      <td>Net Primary Production (Dekadal)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L1', 'index': 12, 'context': 'LEVEL...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>112</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>L1_TBP_A</td>\n",
       "      <td>Total Biomass Production (Annual)</td>\n",
       "      <td>The annual Total Biomass Production expresses ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L1', 'index': 13, 'context': 'LEVEL...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>113</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>L2_NPP_M</td>\n",
       "      <td>Net Primary Production (Monthly)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L2', 'index': 12, 'context': 'LEVEL...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>212</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>L2_NPP_D</td>\n",
       "      <td>Net Primary Production (Dekadal)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L2', 'index': 12, 'context': 'LEVEL...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>212</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>L2_TBP_S</td>\n",
       "      <td>Total Biomass Production (Seasonal)</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L2', 'index': 13, 'context': 'LEVEL...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>213</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>L3_AWA_NPP_M</td>\n",
       "      <td>Net Primary Production (Awash, Ethiopia - Mont...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 155, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30155</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>L3_BKA_NPP_M</td>\n",
       "      <td>Net Primary Production (Bekaa, Lebanon - Monthly)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 156, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30156</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>L3_BUS_NPP_M</td>\n",
       "      <td>Net Primary Production (Busia, Kenya - Monthly)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 157, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30157</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>L3_GEZ_NPP_M</td>\n",
       "      <td>Net Primary Production (Gezira, Sudan - Monthly)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 158, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30158</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>L3_KOG_NPP_M</td>\n",
       "      <td>Net Primary Production (Koga, Ethiopia - Monthly)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 159, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30159</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>L3_LAK_NPP_M</td>\n",
       "      <td>Net Primary Production (Lower Akagera, Rwanda ...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 160, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30160</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>L3_LAM_NPP_M</td>\n",
       "      <td>Net Primary Production (Lamego, Mozambique - M...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 161, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30161</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>L3_MAL_NPP_M</td>\n",
       "      <td>Net Primary Production (Malwathu Oya West Sub ...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 162, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30162</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>L3_MUV_NPP_M</td>\n",
       "      <td>Net Primary Production (Muvumba catchment, Rwa...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 163, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30163</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>L3_ODN_NPP_M</td>\n",
       "      <td>Net Primary Production (Office du Niger, Mali ...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 164, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30164</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>L3_SED_NPP_M</td>\n",
       "      <td>Net Primary Production (Senegal Delta, Senegal...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 166, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30166</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>L3_YAN_NPP_M</td>\n",
       "      <td>Net Primary Production (Yanze catchment, Rwand...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 167, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30167</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>201</td>\n",
       "      <td>L3_ZAN_NPP_M</td>\n",
       "      <td>Net Primary Production (Zankalon, Egypt - Mont...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 168, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30168</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>202</td>\n",
       "      <td>L3_AWA_NPP_D</td>\n",
       "      <td>Net Primary Production (Awash, Ethiopia - Deka...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 169, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30169</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>L3_BKA_NPP_D</td>\n",
       "      <td>Net Primary Production (Bekaa, Lebanon - Dekadal)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 170, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30170</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>L3_BUS_NPP_D</td>\n",
       "      <td>Net Primary Production (Busia, Kenya - Dekadal)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 171, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30171</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>205</td>\n",
       "      <td>L3_GEZ_NPP_D</td>\n",
       "      <td>Net Primary Production (Gezira, Sudan - Dekadal)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 172, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30172</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>206</td>\n",
       "      <td>L3_KOG_NPP_D</td>\n",
       "      <td>Net Primary Production (Koga, Ethiopia - Dekadal)</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 173, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30173</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>207</td>\n",
       "      <td>L3_LAK_NPP_D</td>\n",
       "      <td>Net Primary Production (Lower Akagera, Rwanda ...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 174, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30174</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>208</td>\n",
       "      <td>L3_LAM_NPP_D</td>\n",
       "      <td>Net Primary Production (Lamego, Mozambique - D...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 175, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30175</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>209</td>\n",
       "      <td>L3_MAL_NPP_D</td>\n",
       "      <td>Net Primary Production (Malwathu Oya West Sub ...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 176, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30176</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>L3_MUV_NPP_D</td>\n",
       "      <td>Net Primary Production (Muvumba catchment, Rwa...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 177, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30177</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>211</td>\n",
       "      <td>L3_ODN_NPP_D</td>\n",
       "      <td>Net Primary Production (Office du Niger, Mali ...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 178, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30178</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>212</td>\n",
       "      <td>L3_SAN_NPP_D</td>\n",
       "      <td>Net Primary Production (Sanaa basin, Yemen - D...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 179, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30179</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213</td>\n",
       "      <td>L3_SED_NPP_D</td>\n",
       "      <td>Net Primary Production (Senegal Delta, Senegal...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 180, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30180</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>214</td>\n",
       "      <td>L3_YAN_NPP_D</td>\n",
       "      <td>Net Primary Production (Yanze catchment, Rwand...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 181, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30181</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>215</td>\n",
       "      <td>L3_ZAN_NPP_D</td>\n",
       "      <td>Net Primary Production (Zankalon, Egypt - Deka...</td>\n",
       "      <td>Net Primary Production (NPP) is a fundamental ...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'gC/m²', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 182, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30182</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStatsTimeSeries...</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>216</td>\n",
       "      <td>L3_AWA_TBP_S</td>\n",
       "      <td>Total Biomass Production (Awash, Ethiopia - Se...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 183, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30183</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>217</td>\n",
       "      <td>L3_BKA_TBP_S</td>\n",
       "      <td>Total Biomass Production (Bekaa, Lebanon - Sea...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 184, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30184</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>L3_BUS_TBP_S</td>\n",
       "      <td>Total Biomass Production (Busia, Kenya - Seaso...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 185, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30185</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>L3_GEZ_TBP_S</td>\n",
       "      <td>Total Biomass Production (Gezira, Sudan - Seas...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 186, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30186</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>L3_KOG_TBP_S</td>\n",
       "      <td>Total Biomass Production (Koga, Ethiopia - Sea...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 187, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30187</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>L3_LAM_TBP_S</td>\n",
       "      <td>Total Biomass Production (Lamego, Mozambique -...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 189, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30189</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>L3_MAL_TBP_S</td>\n",
       "      <td>Total Biomass Production (Malwathu Oya West Su...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 190, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30190</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>L3_MUV_TBP_S</td>\n",
       "      <td>Total Biomass Production (Muvumba catchment, R...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 191, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30191</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>L3_ODN_TBP_S</td>\n",
       "      <td>Total Biomass Production (Office du Niger, Mal...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 192, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30192</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>225</td>\n",
       "      <td>L3_SED_TBP_S</td>\n",
       "      <td>Total Biomass Production (Senegal Delta, Seneg...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 194, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30194</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>226</td>\n",
       "      <td>L3_YAN_TBP_S</td>\n",
       "      <td>Total Biomass Production (Yanze catchment, Rwa...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 195, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30195</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>227</td>\n",
       "      <td>L3_ZAN_TBP_S</td>\n",
       "      <td>Total Biomass Production (Zankalon, Egypt - Se...</td>\n",
       "      <td>Total Biomass Production (TBP) is defined as t...</td>\n",
       "      <td>{'format': 'Raster Dataset', 'unit': 'kg/ha', ...</td>\n",
       "      <td>[{'name': 'L3', 'index': 196, 'context': 'LEVE...</td>\n",
       "      <td>WAPOR_2</td>\n",
       "      <td>RASTER</td>\n",
       "      <td>30196</td>\n",
       "      <td>{'pixelTimeSeries': True, 'areaStats': True}</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'rel': 'self', 'href': 'https://io.apps.fao....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             code                                            caption  \\\n",
       "11       L1_NPP_M                   Net Primary Production (Monthly)   \n",
       "12       L1_NPP_D                   Net Primary Production (Dekadal)   \n",
       "13       L1_TBP_A                  Total Biomass Production (Annual)   \n",
       "36       L2_NPP_M                   Net Primary Production (Monthly)   \n",
       "37       L2_NPP_D                   Net Primary Production (Dekadal)   \n",
       "38       L2_TBP_S                Total Biomass Production (Seasonal)   \n",
       "189  L3_AWA_NPP_M  Net Primary Production (Awash, Ethiopia - Mont...   \n",
       "190  L3_BKA_NPP_M  Net Primary Production (Bekaa, Lebanon - Monthly)   \n",
       "191  L3_BUS_NPP_M    Net Primary Production (Busia, Kenya - Monthly)   \n",
       "192  L3_GEZ_NPP_M   Net Primary Production (Gezira, Sudan - Monthly)   \n",
       "193  L3_KOG_NPP_M  Net Primary Production (Koga, Ethiopia - Monthly)   \n",
       "194  L3_LAK_NPP_M  Net Primary Production (Lower Akagera, Rwanda ...   \n",
       "195  L3_LAM_NPP_M  Net Primary Production (Lamego, Mozambique - M...   \n",
       "196  L3_MAL_NPP_M  Net Primary Production (Malwathu Oya West Sub ...   \n",
       "197  L3_MUV_NPP_M  Net Primary Production (Muvumba catchment, Rwa...   \n",
       "198  L3_ODN_NPP_M  Net Primary Production (Office du Niger, Mali ...   \n",
       "199  L3_SED_NPP_M  Net Primary Production (Senegal Delta, Senegal...   \n",
       "200  L3_YAN_NPP_M  Net Primary Production (Yanze catchment, Rwand...   \n",
       "201  L3_ZAN_NPP_M  Net Primary Production (Zankalon, Egypt - Mont...   \n",
       "202  L3_AWA_NPP_D  Net Primary Production (Awash, Ethiopia - Deka...   \n",
       "203  L3_BKA_NPP_D  Net Primary Production (Bekaa, Lebanon - Dekadal)   \n",
       "204  L3_BUS_NPP_D    Net Primary Production (Busia, Kenya - Dekadal)   \n",
       "205  L3_GEZ_NPP_D   Net Primary Production (Gezira, Sudan - Dekadal)   \n",
       "206  L3_KOG_NPP_D  Net Primary Production (Koga, Ethiopia - Dekadal)   \n",
       "207  L3_LAK_NPP_D  Net Primary Production (Lower Akagera, Rwanda ...   \n",
       "208  L3_LAM_NPP_D  Net Primary Production (Lamego, Mozambique - D...   \n",
       "209  L3_MAL_NPP_D  Net Primary Production (Malwathu Oya West Sub ...   \n",
       "210  L3_MUV_NPP_D  Net Primary Production (Muvumba catchment, Rwa...   \n",
       "211  L3_ODN_NPP_D  Net Primary Production (Office du Niger, Mali ...   \n",
       "212  L3_SAN_NPP_D  Net Primary Production (Sanaa basin, Yemen - D...   \n",
       "213  L3_SED_NPP_D  Net Primary Production (Senegal Delta, Senegal...   \n",
       "214  L3_YAN_NPP_D  Net Primary Production (Yanze catchment, Rwand...   \n",
       "215  L3_ZAN_NPP_D  Net Primary Production (Zankalon, Egypt - Deka...   \n",
       "216  L3_AWA_TBP_S  Total Biomass Production (Awash, Ethiopia - Se...   \n",
       "217  L3_BKA_TBP_S  Total Biomass Production (Bekaa, Lebanon - Sea...   \n",
       "218  L3_BUS_TBP_S  Total Biomass Production (Busia, Kenya - Seaso...   \n",
       "219  L3_GEZ_TBP_S  Total Biomass Production (Gezira, Sudan - Seas...   \n",
       "220  L3_KOG_TBP_S  Total Biomass Production (Koga, Ethiopia - Sea...   \n",
       "221  L3_LAM_TBP_S  Total Biomass Production (Lamego, Mozambique -...   \n",
       "222  L3_MAL_TBP_S  Total Biomass Production (Malwathu Oya West Su...   \n",
       "223  L3_MUV_TBP_S  Total Biomass Production (Muvumba catchment, R...   \n",
       "224  L3_ODN_TBP_S  Total Biomass Production (Office du Niger, Mal...   \n",
       "225  L3_SED_TBP_S  Total Biomass Production (Senegal Delta, Seneg...   \n",
       "226  L3_YAN_TBP_S  Total Biomass Production (Yanze catchment, Rwa...   \n",
       "227  L3_ZAN_TBP_S  Total Biomass Production (Zankalon, Egypt - Se...   \n",
       "\n",
       "                                           description  \\\n",
       "11   Net Primary Production (NPP) is a fundamental ...   \n",
       "12   Net Primary Production (NPP) is a fundamental ...   \n",
       "13   The annual Total Biomass Production expresses ...   \n",
       "36   Net Primary Production (NPP) is a fundamental ...   \n",
       "37   Net Primary Production (NPP) is a fundamental ...   \n",
       "38   Total Biomass Production (TBP) is defined as t...   \n",
       "189  Net Primary Production (NPP) is a fundamental ...   \n",
       "190  Net Primary Production (NPP) is a fundamental ...   \n",
       "191  Net Primary Production (NPP) is a fundamental ...   \n",
       "192  Net Primary Production (NPP) is a fundamental ...   \n",
       "193  Net Primary Production (NPP) is a fundamental ...   \n",
       "194  Net Primary Production (NPP) is a fundamental ...   \n",
       "195  Net Primary Production (NPP) is a fundamental ...   \n",
       "196  Net Primary Production (NPP) is a fundamental ...   \n",
       "197  Net Primary Production (NPP) is a fundamental ...   \n",
       "198  Net Primary Production (NPP) is a fundamental ...   \n",
       "199  Net Primary Production (NPP) is a fundamental ...   \n",
       "200  Net Primary Production (NPP) is a fundamental ...   \n",
       "201  Net Primary Production (NPP) is a fundamental ...   \n",
       "202  Net Primary Production (NPP) is a fundamental ...   \n",
       "203  Net Primary Production (NPP) is a fundamental ...   \n",
       "204  Net Primary Production (NPP) is a fundamental ...   \n",
       "205  Net Primary Production (NPP) is a fundamental ...   \n",
       "206  Net Primary Production (NPP) is a fundamental ...   \n",
       "207  Net Primary Production (NPP) is a fundamental ...   \n",
       "208  Net Primary Production (NPP) is a fundamental ...   \n",
       "209  Net Primary Production (NPP) is a fundamental ...   \n",
       "210  Net Primary Production (NPP) is a fundamental ...   \n",
       "211  Net Primary Production (NPP) is a fundamental ...   \n",
       "212  Net Primary Production (NPP) is a fundamental ...   \n",
       "213  Net Primary Production (NPP) is a fundamental ...   \n",
       "214  Net Primary Production (NPP) is a fundamental ...   \n",
       "215  Net Primary Production (NPP) is a fundamental ...   \n",
       "216  Total Biomass Production (TBP) is defined as t...   \n",
       "217  Total Biomass Production (TBP) is defined as t...   \n",
       "218  Total Biomass Production (TBP) is defined as t...   \n",
       "219  Total Biomass Production (TBP) is defined as t...   \n",
       "220  Total Biomass Production (TBP) is defined as t...   \n",
       "221  Total Biomass Production (TBP) is defined as t...   \n",
       "222  Total Biomass Production (TBP) is defined as t...   \n",
       "223  Total Biomass Production (TBP) is defined as t...   \n",
       "224  Total Biomass Production (TBP) is defined as t...   \n",
       "225  Total Biomass Production (TBP) is defined as t...   \n",
       "226  Total Biomass Production (TBP) is defined as t...   \n",
       "227  Total Biomass Production (TBP) is defined as t...   \n",
       "\n",
       "                                        additionalInfo  \\\n",
       "11   {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "12   {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "13   {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "36   {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "37   {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "38   {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "189  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "190  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "191  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "192  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "193  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "194  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "195  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "196  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "197  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "198  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "199  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "200  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "201  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "202  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "203  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "204  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "205  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "206  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "207  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "208  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "209  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "210  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "211  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "212  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "213  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "214  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "215  {'format': 'Raster Dataset', 'unit': 'gC/m²', ...   \n",
       "216  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "217  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "218  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "219  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "220  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "221  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "222  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "223  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "224  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "225  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "226  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "227  {'format': 'Raster Dataset', 'unit': 'kg/ha', ...   \n",
       "\n",
       "                                                  tags workspaceCode dataType  \\\n",
       "11   [{'name': 'L1', 'index': 12, 'context': 'LEVEL...       WAPOR_2   RASTER   \n",
       "12   [{'name': 'L1', 'index': 12, 'context': 'LEVEL...       WAPOR_2   RASTER   \n",
       "13   [{'name': 'L1', 'index': 13, 'context': 'LEVEL...       WAPOR_2   RASTER   \n",
       "36   [{'name': 'L2', 'index': 12, 'context': 'LEVEL...       WAPOR_2   RASTER   \n",
       "37   [{'name': 'L2', 'index': 12, 'context': 'LEVEL...       WAPOR_2   RASTER   \n",
       "38   [{'name': 'L2', 'index': 13, 'context': 'LEVEL...       WAPOR_2   RASTER   \n",
       "189  [{'name': 'L3', 'index': 155, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "190  [{'name': 'L3', 'index': 156, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "191  [{'name': 'L3', 'index': 157, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "192  [{'name': 'L3', 'index': 158, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "193  [{'name': 'L3', 'index': 159, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "194  [{'name': 'L3', 'index': 160, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "195  [{'name': 'L3', 'index': 161, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "196  [{'name': 'L3', 'index': 162, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "197  [{'name': 'L3', 'index': 163, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "198  [{'name': 'L3', 'index': 164, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "199  [{'name': 'L3', 'index': 166, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "200  [{'name': 'L3', 'index': 167, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "201  [{'name': 'L3', 'index': 168, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "202  [{'name': 'L3', 'index': 169, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "203  [{'name': 'L3', 'index': 170, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "204  [{'name': 'L3', 'index': 171, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "205  [{'name': 'L3', 'index': 172, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "206  [{'name': 'L3', 'index': 173, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "207  [{'name': 'L3', 'index': 174, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "208  [{'name': 'L3', 'index': 175, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "209  [{'name': 'L3', 'index': 176, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "210  [{'name': 'L3', 'index': 177, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "211  [{'name': 'L3', 'index': 178, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "212  [{'name': 'L3', 'index': 179, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "213  [{'name': 'L3', 'index': 180, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "214  [{'name': 'L3', 'index': 181, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "215  [{'name': 'L3', 'index': 182, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "216  [{'name': 'L3', 'index': 183, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "217  [{'name': 'L3', 'index': 184, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "218  [{'name': 'L3', 'index': 185, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "219  [{'name': 'L3', 'index': 186, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "220  [{'name': 'L3', 'index': 187, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "221  [{'name': 'L3', 'index': 189, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "222  [{'name': 'L3', 'index': 190, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "223  [{'name': 'L3', 'index': 191, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "224  [{'name': 'L3', 'index': 192, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "225  [{'name': 'L3', 'index': 194, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "226  [{'name': 'L3', 'index': 195, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "227  [{'name': 'L3', 'index': 196, 'context': 'LEVE...       WAPOR_2   RASTER   \n",
       "\n",
       "     index                                          operation  hidden  \\\n",
       "11     112  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "12     112  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "13     113  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "36     212  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "37     212  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "38     213  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "189  30155  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "190  30156  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "191  30157  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "192  30158  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "193  30159  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "194  30160  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "195  30161  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "196  30162  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "197  30163  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "198  30164  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "199  30166  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "200  30167  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "201  30168  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "202  30169  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "203  30170  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "204  30171  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "205  30172  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "206  30173  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "207  30174  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "208  30175  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "209  30176  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "210  30177  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "211  30178  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "212  30179  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "213  30180  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "214  30181  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "215  30182  {'pixelTimeSeries': True, 'areaStatsTimeSeries...   False   \n",
       "216  30183       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "217  30184       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "218  30185       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "219  30186       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "220  30187       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "221  30189       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "222  30190       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "223  30191       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "224  30192       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "225  30194       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "226  30195       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "227  30196       {'pixelTimeSeries': True, 'areaStats': True}   False   \n",
       "\n",
       "                                                 links  \n",
       "11   [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "12   [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "13   [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "36   [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "37   [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "38   [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "189  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "190  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "191  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "192  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "193  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "194  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "195  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "196  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "197  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "198  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "199  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "200  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "201  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "202  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "203  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "204  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "205  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "206  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "207  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "208  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "209  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "210  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "211  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "212  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "213  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "214  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "215  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "216  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "217  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "218  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "219  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "220  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "221  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "222  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "223  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "224  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "225  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "226  [{'rel': 'self', 'href': 'https://io.apps.fao....  \n",
       "227  [{'rel': 'self', 'href': 'https://io.apps.fao....  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_lst = ['NPP','TBP']\n",
    "\n",
    "\n",
    "filter_str  = '|'.join([str(elem) for elem in filter_lst])\n",
    "filter_fn_str  = '_'.join([str(elem) for elem in filter_lst])\n",
    "\n",
    "df_catalog_filtered = (df_catalog[df_catalog['code'].str.contains(filter_str)])\n",
    "df_catalog_filtered.to_csv(f'./results/{workspace}_{filter_fn_str}.csv')\n",
    "df_catalog_filtered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 WAPOR product selection & meta-data exploration\n",
    "We will select the Level 1 product of dekadal NPP (L1_NPP_D) and look at the associated meta-data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'requestId': 'b3400d83-f7f5-464f-ba4e-9d776f6be55b',\n",
       " 'timestamp': 1663068222089,\n",
       " 'runtime': 3,\n",
       " 'status': 200,\n",
       " 'message': 'OK',\n",
       " 'response': {'code': 'L1_NPP_D',\n",
       "  'caption': 'Net Primary Production (Dekadal)',\n",
       "  'description': 'Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad.',\n",
       "  'additionalInfo': {'format': 'Raster Dataset',\n",
       "   'unit': 'gC/m²',\n",
       "   'dataType': 'Int16 (16bit Integer)',\n",
       "   'conversionFactor': 'the pixel value in the downloaded data must be multiplied by 0.001',\n",
       "   'noDataValue': -9999,\n",
       "   'spatialResolution': '250m (0.00223 degree)',\n",
       "   'spatialExtent': 'Africa and Near East',\n",
       "   'spatialReferenceSystem': 'EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)',\n",
       "   'temporalResolution': 'from January 2009 to present',\n",
       "   'temporalExtent': 'Dekadal (approximately every 10 days)',\n",
       "   'nearRealTime': 'New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations.',\n",
       "   'methodology': 'The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs).  The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress).\\n\\nThe following data is used to calculate NPP:\\n\\n- Daily:incoming solar radiation and temperature data (Tmin/Tmax);\\n\\n- Dekadal:fAPAR and soil moisture stress;\\n\\n- Seasonal:Land Cover.\\n\\nData component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/'},\n",
       "  'tags': [{'name': 'L1', 'index': 12, 'context': 'LEVELS_LIST'},\n",
       "   {'name': 'LAND', 'index': 1, 'context': 'CATEGORIES_LIST'},\n",
       "   {'name': 'L1', 'index': 12, 'context': 'levels'},\n",
       "   {'name': 'C3', 'index': 1, 'context': 'categories'}],\n",
       "  'workspaceCode': 'WAPOR_2',\n",
       "  'dataType': 'RASTER',\n",
       "  'index': 112,\n",
       "  'operation': {'pixelTimeSeries': True, 'areaStatsTimeSeries': True},\n",
       "  'hidden': False,\n",
       "  'links': [{'rel': 'self',\n",
       "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L1_NPP_D'},\n",
       "   {'rel': 'dimensions',\n",
       "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L1_NPP_D/dimensions'},\n",
       "   {'rel': 'measures',\n",
       "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L1_NPP_D/measures'},\n",
       "   {'rel': 'styles',\n",
       "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L1_NPP_D/styles'},\n",
       "   {'rel': 'rasters',\n",
       "    'href': 'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/WAPOR_2/cubes/L1_NPP_D/rasters'}]}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This cell prints the meta-data in the original dictionary format. \n",
    "\n",
    "catalog_code='L1_NPP_D'\n",
    "\n",
    "workspace='WAPOR_2'\n",
    "url_catalog=f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{catalog_code}'\n",
    "resp_code=requests.get(url_catalog).json()\n",
    "resp_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meta-data publication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################# Thematic Details ###################################\n",
      "Code                        :  L1_NPP_D\n",
      "Variable                    :  Net Primary Production (Dekadal)\n",
      "Description                 :  Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad.\n",
      "Unit                        :  gC/m²\n",
      "\n",
      "################# Data Type & Scaling ################################\n",
      "Data Type                   :  Int16 (16bit Integer)\n",
      "conversion Factor           :  the pixel value in the downloaded data must be multiplied by 0.001\n",
      "No Data Value               :  -9999\n",
      "\n",
      "################# Coverage & Spatial Information ######################\n",
      "Coverage                    :  Africa and Near East\n",
      "Spatial Resolution          :  250m (0.00223 degree)\n",
      "Reference system            :  EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)\n",
      "\n",
      "################# Archive Availability & Periodicity ##################\n",
      "Periodicity                 :  from January 2009 to present\n",
      "Archive Availability        :  Dekadal (approximately every 10 days)\n",
      "Near Real Time Availibility :  New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations.\n",
      "################# Methodology ##################\n",
      "Methodology                 :  The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs).  The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress).\n",
      "\n",
      "The following data is used to calculate NPP:\n",
      "\n",
      "- Daily:incoming solar radiation and temperature data (Tmin/Tmax);\n",
      "\n",
      "- Dekadal:fAPAR and soil moisture stress;\n",
      "\n",
      "- Seasonal:Land Cover.\n",
      "\n",
      "Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/\n"
     ]
    }
   ],
   "source": [
    "#This cell prints the key charactersitics from the meta-data dictionary in a more structured manner. \n",
    "\n",
    "print('################# Thematic Details ###################################')\n",
    "print('Code                        : ',resp_code['response']['code'])\n",
    "print('Variable                    : ',resp_code['response']['caption'])\n",
    "print('Description                 : ',resp_code['response']['description'])\n",
    "print('Unit                        : ',resp_code['response']['additionalInfo']['unit'])\n",
    "print('')\n",
    "print('################# Data Type & Scaling ################################')\n",
    "print('Data Type                   : ',resp_code['response']['additionalInfo']['dataType'])\n",
    "print('conversion Factor           : ',resp_code['response']['additionalInfo']['conversionFactor'])\n",
    "print('No Data Value               : ',resp_code['response']['additionalInfo']['noDataValue'])\n",
    "print('')\n",
    "print('################# Coverage & Spatial Information ######################')\n",
    "print('Coverage                    : ',resp_code['response']['additionalInfo']['spatialExtent'])\n",
    "print('Spatial Resolution          : ',resp_code['response']['additionalInfo']['spatialResolution'])\n",
    "print('Reference system            : ',resp_code['response']['additionalInfo']['spatialReferenceSystem'])\n",
    "print('')\n",
    "print('################# Archive Availability & Periodicity ##################')\n",
    "print('Periodicity                 : ',resp_code['response']['additionalInfo']['temporalResolution'])\n",
    "print('Archive Availability        : ',resp_code['response']['additionalInfo']['temporalExtent'])\n",
    "print('Near Real Time Availibility : ',resp_code['response']['additionalInfo']['nearRealTime'])\n",
    "print('################# Methodology ##################')\n",
    "print('Methodology                 : ',resp_code['response']['additionalInfo']['methodology'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Net Primary Production  (NPP) time series extraction\n",
    "\n",
    "Now that we have explored the product details we will extract and plot a time series of dekadal NPP data for a specific location. \n",
    "\n",
    "Use the following function to download a time series for a single pixel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pixel_timeseries(workspace, product_code,\n",
    "                         coordinates, crs,\n",
    "                         start_date, end_date):\n",
    "    '''\n",
    "    Function to extract one or multiple pixel time series from the FAO portal.\n",
    "    Parameters:\n",
    "    - workspace: workspace in the FAO catalog where the product of interest is located.\n",
    "    - product_code: specific product you wish to download.\n",
    "    - coordinates: dictionary with keys representing label of the point\n",
    "        and values the x,y coordinate pair in the CRS defined by the next parameter.\n",
    "        In case of Geographic CRS, the order should be [lat, lon].\n",
    "    - crs: EPSG code of projection system (int)\n",
    "    - start_date: start date of the time series.\n",
    "        Should be str in the following format: \"yyyy-mm-dd\".\n",
    "    - end_date: end date of the time series.\n",
    "        Should be str in the following format: \"yyyy-mm-dd\".\n",
    "        \n",
    "    The function will return a dataframe with time as index and each column\n",
    "    representing the query result for an individual point.\n",
    "    '''\n",
    "    \n",
    "    print('Making some preparations...')\n",
    "    \n",
    "    path_query = r'https://io.apps.fao.org/gismgr/api/v1/query/'\n",
    "    \n",
    "    crs = f'EPSG:{crs}'\n",
    "    \n",
    "    # get datacube measure\n",
    "    cube_url = f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{product_code}/measures'\n",
    "    resp = requests.get(cube_url).json()\n",
    "    measure = resp['response']['items'][0]['code']\n",
    "    print('MEASURE: ', measure)\n",
    "    \n",
    "    # get datacube time dimension\n",
    "    cube_url = f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{product_code}/dimensions'\n",
    "    resp = requests.get(cube_url).json()\n",
    "    items = pd.DataFrame.from_dict(resp['response']['items'])\n",
    "    dimension = items[items.type=='TIME']['code'].values[0]\n",
    "    print('DIMENSION: ', dimension)\n",
    "    \n",
    "    print('Sending query for each point...')\n",
    "    npoints = len(coordinates.keys())\n",
    "    i = 0    \n",
    "    for label, coo in coordinates.items():\n",
    "        \n",
    "        print(f'Processing point {i+1} / {npoints}...')\n",
    "        query = {\n",
    "        \"type\": \"PixelTimeSeries\",\n",
    "        \"params\": {\n",
    "            \"cube\": {\n",
    "            \"code\": product_code,\n",
    "            \"workspaceCode\": workspace,\n",
    "            \"language\": \"en\"\n",
    "            },\n",
    "            \"dimensions\": [\n",
    "            {\n",
    "                \"code\": dimension,\n",
    "                \"range\": f\"[{start_date},{end_date})\"\n",
    "            }\n",
    "            ],\n",
    "            \"measures\": [\n",
    "            measure\n",
    "            ],\n",
    "            \"point\": {\n",
    "            \"crs\": crs,            \n",
    "            \"x\":coo[1],\n",
    "            \"y\":coo[0]\n",
    "            }\n",
    "        }\n",
    "        }\n",
    "        \n",
    "        resp_query = requests.post(path_query, json=query)\n",
    "        resp = resp_query.json()\n",
    "        results = resp['response']\n",
    "        df = pd.DataFrame(results['items'], columns=['Time', label])\n",
    "        df.index = pd.to_datetime(df['Time'], format='%Y-%m-%d')\n",
    "        df = df.drop(columns=['Time'])\n",
    "        if i == 0:\n",
    "            df_fin = df\n",
    "        else:\n",
    "            df_fin = pd.merge(df_fin, df, left_index=True, right_index=True)  \n",
    "        \n",
    "        i += 1\n",
    "        print('Query successful!')\n",
    "        \n",
    "    return df_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making some preparations...\n",
      "MEASURE:  NPP\n",
      "DIMENSION:  DEKAD\n",
      "Sending query for each point...\n",
      "Processing point 1 / 1...\n",
      "Query successful!\n"
     ]
    },
    {
     "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>test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2009-01-10</td>\n",
       "      <td>0.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-01-20</td>\n",
       "      <td>0.304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-01-31</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-02-10</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-02-20</td>\n",
       "      <td>0.171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             test\n",
       "Time             \n",
       "2009-01-10  0.413\n",
       "2009-01-20  0.304\n",
       "2009-01-31  0.190\n",
       "2009-02-10  0.167\n",
       "2009-02-20  0.171"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify parameters for API query\n",
    "point = {\"test\": [14.801460,-13.616014]}\n",
    "# coordinate reference system\n",
    "crs = 4326 \n",
    "product_code = \"L1_NPP_D\"\n",
    "workspace = 'WAPOR_2'\n",
    "start_date = \"2009-01-01\"\n",
    "end_date = \"2009-12-31\"\n",
    "\n",
    "output = get_pixel_timeseries(workspace, product_code,\n",
    "                              point, crs,\n",
    "                              start_date, end_date)\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Plot time series\n",
    "Based on the Pandas dataframe the NPP time series is visualized in a line graph. Execute the cell and interpret the time series for your point. Have a look at the unit of the axis and the absolute values. Is there a clear growing season present? Is there a period with zero NPP? When is the peak and what does this mean? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NPP time series example')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cell that generates a line graph from the Pandas dataframe. \n",
    "\n",
    "figs, ax = plt.subplots(1, figsize=(12, 6))\n",
    "\n",
    "output.plot(ax=ax, color='orange', marker='*')\n",
    "ax.set_ylabel('NPP [gC/m2/day]')\n",
    "ax.legend(loc='upper right', ncol=5)\n",
    "ax.set_title('NPP time series example')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. NPP product time series exploration\n",
    "The previous extraction was focused on a single location. Next we will run a script for multiple locations or pixel extractions and visualize the outputs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making some preparations...\n",
      "MEASURE:  NPP\n",
      "DIMENSION:  DEKAD\n",
      "Sending query for each point...\n",
      "Processing point 1 / 4...\n",
      "Query successful!\n",
      "Processing point 2 / 4...\n",
      "Query successful!\n",
      "Processing point 3 / 4...\n",
      "Query successful!\n",
      "Processing point 4 / 4...\n",
      "Query successful!\n"
     ]
    },
    {
     "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>Nigeria_south_agriculture</th>\n",
       "      <th>Nigeria_south_forest</th>\n",
       "      <th>Nigeria_north_agriculture</th>\n",
       "      <th>Nigeria_north_forest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2015-01-10</td>\n",
       "      <td>2.981</td>\n",
       "      <td>2.739</td>\n",
       "      <td>2.755</td>\n",
       "      <td>3.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-20</td>\n",
       "      <td>2.605</td>\n",
       "      <td>2.304</td>\n",
       "      <td>2.339</td>\n",
       "      <td>3.837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>1.706</td>\n",
       "      <td>1.448</td>\n",
       "      <td>1.613</td>\n",
       "      <td>3.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-02-10</td>\n",
       "      <td>1.523</td>\n",
       "      <td>1.192</td>\n",
       "      <td>1.341</td>\n",
       "      <td>2.629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-02-20</td>\n",
       "      <td>1.479</td>\n",
       "      <td>1.079</td>\n",
       "      <td>1.360</td>\n",
       "      <td>2.185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nigeria_south_agriculture  Nigeria_south_forest  \\\n",
       "Time                                                          \n",
       "2015-01-10                      2.981                 2.739   \n",
       "2015-01-20                      2.605                 2.304   \n",
       "2015-01-31                      1.706                 1.448   \n",
       "2015-02-10                      1.523                 1.192   \n",
       "2015-02-20                      1.479                 1.079   \n",
       "\n",
       "            Nigeria_north_agriculture  Nigeria_north_forest  \n",
       "Time                                                         \n",
       "2015-01-10                      2.755                 3.680  \n",
       "2015-01-20                      2.339                 3.837  \n",
       "2015-01-31                      1.613                 3.145  \n",
       "2015-02-10                      1.341                 2.629  \n",
       "2015-02-20                      1.360                 2.185  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define a dictionary of geo-locations in Lat/Lon coordinates. \n",
    "#You can give each pixel a name that will be used in the time series plot. \n",
    "#The extraction can take a few moments (usually < 2 min)\n",
    "\n",
    "points_dict = {'Nigeria_south_agriculture': [6.408084,6.240997],\n",
    "               'Nigeria_south_forest':[6.472100,6.271589],\n",
    "               'Nigeria_north_agriculture':[7.195370,11.562504],\n",
    "               'Nigeria_north_forest':[6.990481,11.628498]}\n",
    "\n",
    "crs = 4326  # lat/lon\n",
    "product_code = \"L1_NPP_D\"\n",
    "workspace = 'WAPOR_2'\n",
    "\n",
    "start_date = '2015-01-01'\n",
    "end_date = '2017-12-31'\n",
    "\n",
    "output = get_pixel_timeseries(workspace, product_code,\n",
    "                              points_dict, crs,\n",
    "                              start_date, end_date)\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NPP time series example')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all time series and interpret the outcomes:\n",
    "# - Are there big differences amongst the locations? And can you explain them?\n",
    "# - Which is the most productive and least productive site? \n",
    "\n",
    "figs, ax = plt.subplots(1, figsize=(12, 6))\n",
    "output.plot(ax=ax, marker='*')\n",
    "ax.set_ylabel('NPP [gC/m2/day]')\n",
    "ax.legend(loc='upper right', ncol=5)\n",
    "ax.set_title('NPP time series example')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Total Biomass Production (TBP) product exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################# Thematic Details ###################################\n",
      "Code                        :  L1_TBP_A\n",
      "Variable                    :  Total Biomass Production (Annual)\n",
      "Description                 :  The annual Total Biomass Production expresses the total amount of dry matter produced over the year. It is calculated by dekad and summarized as annual total. Each pixel represents the annual amount of dry matter in kg per hectare.\n",
      "Unit                        :  kg/ha\n",
      "\n",
      "################# Data Type & Scaling ################################\n",
      "Data Type                   :  Int32 (32bit Integer)\n",
      "No Data Value               :  -9999\n",
      "\n",
      "################# Coverage & Spatial Information ######################\n",
      "Coverage                    :  Africa and Near East\n",
      "Spatial Resolution          :  250m (0.00223 degree)\n",
      "Reference system            :  EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)\n",
      "\n",
      "################# Archive Availability & Periodicity ##################\n",
      "Periodicity                 :  from January 2009 to present\n",
      "Archive Availability        :  Annual\n",
      "Near Real Time Availibility :  New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later.\n",
      "################# Methodology ##################\n",
      "Methodology                 :  Annual TBP is the sum of the TBP by dekad in the year of reference (TBP by dekad is the average daily value in a dekad multiplied by number of days in each dekad). TBP is calculated by applying a unit conversion factor to net primary production (NPP) that converts carbon grams per m² into dry matter (DM) per hectare. In the previous version of the database, a fixed shoot/root ratio of 0.65 was applied to distribute DM into below ground and above ground production. For Version 2, also based on the findings of the Quality assessment, we decided to release the total biomass (above and below ground) instead, and leave users the possibility to apply a shoot/root ratio that better suits their analysis.\n"
     ]
    }
   ],
   "source": [
    "# This cell prints the key charactersitics from the meta-data dictionary in a more structured manner. \n",
    "# The TBP meta-data does not contain information on the conversion factor. \n",
    "#Based on the WaPOR documentation we know that the digital values represent the physical values in kg DM/ha. \n",
    "#So slope = 1 and offset = 0 in this case.\n",
    "\n",
    "\n",
    "catalog_code = 'L1_TBP_A'\n",
    "workspace = 'WAPOR_2'\n",
    "url_catalog = f'https://io.apps.fao.org/gismgr/api/v1/catalog/workspaces/{workspace}/cubes/{catalog_code}'\n",
    "resp_code = requests.get(url_catalog).json()\n",
    "\n",
    "print('################# Thematic Details ###################################')\n",
    "print('Code                        : ',resp_code['response']['code'])\n",
    "print('Variable                    : ',resp_code['response']['caption'])\n",
    "print('Description                 : ',resp_code['response']['description'])\n",
    "print('Unit                        : ',resp_code['response']['additionalInfo']['unit'])\n",
    "print('')\n",
    "print('################# Data Type & Scaling ################################')\n",
    "print('Data Type                   : ',resp_code['response']['additionalInfo']['dataType'])\n",
    "print('No Data Value               : ',resp_code['response']['additionalInfo']['noDataValue'])\n",
    "print('')\n",
    "print('################# Coverage & Spatial Information ######################')\n",
    "print('Coverage                    : ',resp_code['response']['additionalInfo']['spatialExtent'])\n",
    "print('Spatial Resolution          : ',resp_code['response']['additionalInfo']['spatialResolution'])\n",
    "print('Reference system            : ',resp_code['response']['additionalInfo']['spatialReferenceSystem'])\n",
    "print('')\n",
    "print('################# Archive Availability & Periodicity ##################')\n",
    "print('Periodicity                 : ',resp_code['response']['additionalInfo']['temporalResolution'])\n",
    "print('Archive Availability        : ',resp_code['response']['additionalInfo']['temporalExtent'])\n",
    "print('Near Real Time Availibility : ',resp_code['response']['additionalInfo']['nearRealTime'])\n",
    "print('################# Methodology ##################')\n",
    "print('Methodology                 : ',resp_code['response']['additionalInfo']['methodology'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making some preparations...\n",
      "MEASURE:  LPR\n",
      "DIMENSION:  YEAR\n",
      "Sending query for each point...\n",
      "Processing point 1 / 2...\n",
      "Query successful!\n",
      "Processing point 2 / 2...\n",
      "Query successful!\n"
     ]
    },
    {
     "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>point1</th>\n",
       "      <th>point2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>25992.0</td>\n",
       "      <td>6124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>23882.0</td>\n",
       "      <td>5348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>23708.0</td>\n",
       "      <td>4225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>23737.0</td>\n",
       "      <td>5925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>22298.0</td>\n",
       "      <td>4565.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             point1  point2\n",
       "Time                       \n",
       "2009-01-01  25992.0  6124.0\n",
       "2010-01-01  23882.0  5348.0\n",
       "2011-01-01  23708.0  4225.0\n",
       "2012-01-01  23737.0  5925.0\n",
       "2013-01-01  22298.0  4565.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify parameters for API query\n",
    "points = {'point1': [7.89534, 37.95883206252312],\n",
    "         'point2': [14.801460, -13.616014]}\n",
    "# coordinate reference system\n",
    "crs = 4326 \n",
    "\n",
    "product_code = \"L1_TBP_A\"\n",
    "workspace = 'WAPOR_2'\n",
    "\n",
    "start_date = \"2009-01-01\"\n",
    "end_date = \"2018-12-31\"\n",
    "\n",
    "output = get_pixel_timeseries(workspace, product_code,\n",
    "                              points, crs,\n",
    "                              start_date, end_date)\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize yearly TBP values and interpret the inter-annual differences.\n",
    "# How productive is your selected site? Which factor do you need to apply to convert to ton per hectare?\n",
    "# Which years are most and least productive? What could be underlying factors? Think about weather conditions, drought, precipitation,...\n",
    "# On agricultural sites also crop rotation and management practices can play an important role.\n",
    "\n",
    "figs, ax = plt.subplots(1, figsize=(12, 6))\n",
    "output.plot.bar(ax=ax)\n",
    "ax.set_ylabel('DMP [kg/ha]')\n",
    "ax.legend(loc='upper right', ncol=5)\n",
    "ax.set_title('TBP yearly time series example')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit",
   "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": "3.6.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}