diff --git a/README.md b/README.md
index f68a72cf7835a0ecc3b49b23bfa2a0e0e0886f82..e4d36aaac2b6e780d51ceaaff3dc16f48e4a5c75 100644
--- a/README.md
+++ b/README.md
@@ -6,10 +6,13 @@ Recently, we did some updates in our stacks. We corrected the error raised durin
 
 -	Be careful with the shebang. Replace #!/usr/local/bin/python by #!/opt/conda/bin/python to make it work in Jupyter.
 
-Please find attached two notebooks. 
+Please find attached notebooks. 
 -	The Compute ratio provides an example to push and retrieve data from S3 to your workspace and also ingest data in the back end in order to visualize it in the front end.
--	The “Example of Data access” allow you to select a data from the back end catalog, and to transfer a subset in your workspace.
+-	The “edav_example_of_data_access_v0.1.1” notebook in the directory edav allow you to select a data from the back end catalog, and to transfer a subset in your workspace.
     - To make it work the, you need at least to install the version 2.3.3 of GDAL.
+    
+-	The “edav_make_subset_from_catalogue_data” notebook in the directory edav allow you to make subset from all catalogues.
+   
 
 Don’t hesitate if you face any difficulties to call us.
 
diff --git a/edav/.ipynb_checkpoints/edav_example_for_all_datasets-checkpoint.ipynb b/edav/.ipynb_checkpoints/edav_example_for_all_datasets-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5b4adb97b60638671a8c836dfc57d1e269db0829
--- /dev/null
+++ b/edav/.ipynb_checkpoints/edav_example_for_all_datasets-checkpoint.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example of data access"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example of data access and calibration of P-band SAR data on the NASA MAAP."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from osgeo import gdal\n",
+    "from gdalconst import GA_ReadOnly\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.signal as sg\n",
+    "import sys\n",
+    "import requests\n",
+    "sys.path.insert(0,'/projects/demo-scripts/Scripts')\n",
+    "\n",
+    "# Increase figure size (can be modified for bigger or smaller figures):\n",
+    "plt.rcParams[\"figure.figsize\"]=20,20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Open a P-band SAR image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Open SAR image (HV polarisation) in slant range geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GET ALL AVAILABLE datasetId and correlated subDaasetId\n",
+      "[{'datasetId': 'ESACCI_Biomass_L4_AGB', 'subDatasetIds': ['ESACCI_Biomass_L4_AGB_V3_100m_2017', 'ESACCI_Biomass_L4_AGB_V3_100m_2010', 'ESACCI_Biomass_L4_AGB_V3_100m_2018']}, {'datasetId': 'AFRISAR_ONERA_geo', 'subDatasetIds': ['AFRISAR_ONERA_geo_SLC_HH_I', 'AFRISAR_ONERA_geo_SLC_HH_Q', 'AFRISAR_ONERA_geo_SLC_HV_I', 'AFRISAR_ONERA_geo_SLC_HV_Q', 'AFRISAR_ONERA_geo_SLC_VH_I', 'AFRISAR_ONERA_geo_SLC_VH_Q', 'AFRISAR_ONERA_geo_SLC_VV_I', 'AFRISAR_ONERA_geo_SLC_VV_Q', 'AFRISAR_ONERA_geo_KZ']}, {'datasetId': 'AFRISAR_ONERA', 'subDatasetIds': ['AFRISAR_ONERA_INC', 'AFRISAR_ONERA_SLC_HH', 'AFRISAR_ONERA_SLC_HV', 'AFRISAR_ONERA_SLC_VV', 'AFRISAR_ONERA_SLC_VH', 'AFRISAR_ONERA_LUT', 'AFRISAR_ONERA_KZ', 'AFRISAR_ONERA_ROI']}, {'datasetId': 'INDREX2_geo', 'subDatasetIds': ['INDREX2_geo_SLC_HH_I', 'INDREX2_geo_SLC_HH_Q', 'INDREX2_geo_SLC_HV_I', 'INDREX2_geo_SLC_HV_Q', 'INDREX2_geo_SLC_VH_I', 'INDREX2_geo_SLC_VH_Q', 'INDREX2_geo_SLC_VV_I', 'INDREX2_geo_SLC_VV_Q', 'INDREX2_geo_KZ']}, {'datasetId': 'AFRISAR_DLR_geo', 'subDatasetIds': ['AFRISAR_DLR_geo_SLC_HH_I', 'AFRISAR_DLR_geo_SLC_HH_Q', 'AFRISAR_DLR_geo_SLC_HV_I', 'AFRISAR_DLR_geo_SLC_HV_Q', 'AFRISAR_DLR_geo_SLC_VH_I', 'AFRISAR_DLR_geo_SLC_VH_Q', 'AFRISAR_DLR_geo_SLC_VV_I', 'AFRISAR_DLR_geo_SLC_VV_Q', 'AFRISAR_DLR_geo_KZ', 'AFRISAR_DLR_geo_SLC_SLC_HH', 'AFRISAR_DLR_geo_SLC_SLC_HV', 'AFRISAR_DLR_geo_SLC_SLC_VH', 'AFRISAR_DLR_geo_SLC_SLC_VV']}, {'datasetId': 'BIOSAR1', 'subDatasetIds': ['BIOSAR1_KZ', 'BIOSAR1_SLC_HH', 'BIOSAR1_SLC_HV', 'BIOSAR1_SLC_VV', 'BIOSAR1_SLC_VH', 'BIOSAR1_ROI']}, {'datasetId': 'AFRISAR_DLR', 'subDatasetIds': ['AFRISAR_DLR_SLC_HH', 'AFRISAR_DLR_SLC_HV', 'AFRISAR_DLR_SLC_VV', 'AFRISAR_DLR_SLC_VH', 'AFRISAR_DLR_INC', 'AFRISAR_DLR_LUT', 'AFRISAR_DLR_KZ', 'AFRISAR_DLR_ROI']}, {'datasetId': 'TROPISAR', 'subDatasetIds': ['TROPISAR_ROI']}, {'datasetId': 'BIOSAR3', 'subDatasetIds': ['BIOSAR3_SLC_HV', 'BIOSAR3_SLC_VV', 'BIOSAR3_SLC_VH', 'BIOSAR3_KZ', 'BIOSAR3_SLC_HH', 'BIOSAR3_LUT', 'BIOSAR3_ROI']}, {'datasetId': 'BIOSAR2', 'subDatasetIds': ['BIOSAR2_KZ', 'BIOSAR2_SLC_HH', 'BIOSAR2_SLC_HV', 'BIOSAR2_SLC_VV', 'BIOSAR2_SLC_VH', 'BIOSAR2_INC', 'BIOSAR2_LUT', 'BIOSAR2_ROI']}, {'datasetId': 'TROPISAR_geo', 'subDatasetIds': ['TROPISAR_geo_SLC_HH_I', 'TROPISAR_geo_SLC_HH_Q', 'TROPISAR_geo_SLC_HV_I', 'TROPISAR_geo_SLC_HV_Q', 'TROPISAR_geo_SLC_VH_I', 'TROPISAR_geo_SLC_VH_Q', 'TROPISAR_geo_SLC_VV_I', 'TROPISAR_geo_SLC_VV_Q', 'TROPISAR_geo_KZ']}, {'datasetId': 'INDREX2', 'subDatasetIds': ['INDREX2_SLC_VH', 'INDREX2_SLC_HV', 'INDREX2_SLC_VV', 'INDREX2_LUT', 'INDREX2_INC', 'INDREX2_SLC_HH', 'INDREX2_KZ']}, {'datasetId': 'BIOSAR3_geo', 'subDatasetIds': ['BIOSAR3_geo_SLC_HH_Q', 'BIOSAR3_geo_SLC_HV_I', 'BIOSAR3_geo_SLC_HV_Q', 'BIOSAR3_geo_SLC_VH_I', 'BIOSAR3_geo_SLC_VH_Q', 'BIOSAR3_geo_SLC_VV_I', 'BIOSAR3_geo_SLC_VV_Q', 'BIOSAR3_geo_SLC_HH_I', 'BIOSAR3_geo_KZ']}, {'datasetId': 'BIOSAR2_geo', 'subDatasetIds': ['BIOSAR2_geo_SLC_HH_I', 'BIOSAR2_geo_SLC_HH_Q', 'BIOSAR2_geo_SLC_HV_I', 'BIOSAR2_geo_SLC_HV_Q', 'BIOSAR2_geo_SLC_VH_I', 'BIOSAR2_geo_SLC_VH_Q', 'BIOSAR2_geo_SLC_VV_I', 'BIOSAR2_geo_SLC_VV_Q', 'BIOSAR2_geo_KZ']}, {'datasetId': 'BIOSAR1_geo', 'subDatasetIds': ['BIOSAR1_geo_SLC_HH_I', 'BIOSAR1_geo_SLC_HH_Q', 'BIOSAR1_geo_SLC_HH_amplitude', 'BIOSAR1_geo_SLC_HV_I', 'BIOSAR1_geo_SLC_HV_Q', 'BIOSAR1_geo_SLC_HV_amplitude', 'BIOSAR1_geo_SLC_VH_I', 'BIOSAR1_geo_SLC_VH_Q', 'BIOSAR1_geo_SLC_VH_amplitude', 'BIOSAR1_geo_SLC_VV_I', 'BIOSAR1_geo_SLC_VV_Q', 'BIOSAR1_geo_SLC_VV_amplitude']}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "endpoint=\"https://edav-das.val.esa-maap.org\"\n",
+    "print(\"GET ALL AVAILABLE datasetId and correlated subDaasetId\")\n",
+    "dat=requests.get(endpoint+\"/opensearch/datasets\")\n",
+    "if dat.status_code == 200:\n",
+    "    response=[]\n",
+    "    datasets=dat.json()[\"features\"]\n",
+    "    for feature in datasets:\n",
+    "        elem={}\n",
+    "        elem[\"datasetId\"]=feature[\"datasetId\"]\n",
+    "        elem[\"subDatasetIds\"]=[]\n",
+    "        for subdset in feature[\"subDataset\"]:\n",
+    "            elem[\"subDatasetIds\"].append(subdset[\"subDatasetId\"])\n",
+    "        response.append(elem)\n",
+    "    print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 To discover the granule for a specific datasetId & subdatasetId <br>\n",
+    "Below you see the code to discover the products/granules. \n",
+    "You can also filter using all the metadata querable of the new metadata model implemented within the post_v1 (e.g. SceneType_value, gridType, ...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'metadata': {'EPSG': None, 'date': '2016-02-10T09:32:00Z', 'grid': False, 'title': 'AFRISAR_DLR', 'source': 'https://bmap-catalogue-data.oss.eu-west-0.prod-cloud-ocb.orange-business.com/Campaign_data/afrisar_dlr/afrisar_dlr_T2-0_SLC_HV.tiff', 'Product': 'SLC', 'maxDate': '2016-02-10T09:32:00Z', 'minDate': '2016-02-10T09:32:00Z', 'geometry': '{ \"type\": \"Polygon\", \"coordinates\": [ [ [ 11.562917709350586, -0.148332044482231 ], [ 11.562917709350586, -0.253187149763107 ], [ 11.663571357727051, -0.253187149763107 ], [ 11.663571357727051, -0.148332044482231 ], [ 11.562917709350586, -0.148332044482231 ] ] ] }', 'gridType': 'Custom', 'maxValue': None, 'minValue': None, 'SceneType': 'scene', 'SubRegion': 'LA LOPE', 'datasetId': 'AFRISAR_DLR', 'geolocated': False, 'identifier': 'G1200110716-ESA_MAAP', 'timeExtent': '2016-02-10T09:32:00Z/2016-02-10T09:32:00Z', 'noDataValue': None, 'dataset_type': 'Raster', 'subDatasetId': 'AFRISAR_DLR_SLC_HV', 'Product_value': '0', 'SceneType_value': 'T2-0', 'SubRegion_value': '0', 'single_multiband': '1', 'dataset_dimension': '3', 'dataset_description': 'The  ESA  BIOMASS  mission  was  selected  in  2013  as  the  7th  Earth  Explorer  mission.  BIOMASS  will  provide estimates of forest biomass and height with full coverage over the tropical areas exploiting the penetration  capabilities  of  P-band.  In  order  to  further  support  the  BIOMASS  mission  development,  especially concerning the mission concept verification and the development of geophysical algorithms, ESA funded the AfriSAR campaign.During the AfriSAR campaign, shared between ONERA (dry season, July 2015) and DLR (wet season 2016),  Pol-InSAR  and  TomoSAR  airborne  data  set  were  collected  over  four  test  sites  of  Gabon  (Africa),  therefore  covering  different  forest  structures,  biomass  levels  and  disturbances.  Although  the  interferometric  /  tomographic  baselines  were  optimized  for  P-band  acquisitions,  L-band  data  were  collected simultaneously as well.This reports describes the test sites, the available ground measurements (carried out in a parallel field inventory  campaign  in  2016)  and  Lidar  data  for  the  validation  of  the  SAR  product  derivation  and  analysis. Furthermore, both campaigns are described in details, and data acquired and processed are listed.   The   results   of   data   quality   check   are   provided,   together   with   first   analyses   aimed   at   investigating  the  potentials  of  the  acquired  data  sets  for  generating  Level-2  products  in  terms  of  Pol-InSAR forest height and TomoSAR vertical reflectivity profiles.', 'dataset_specification': 'https://edav-das.val.esa-maap.org/loader/specs.json?datasetId=AFRISAR_DLR', 'dataset_dimension_description': 'Long Lat Time'}, 'geometry': {'type': 'Polygon', 'coordinates': [[[11.5629177093506, -0.148332044482231], [11.5629177093506, -0.253187149763107], [11.6635713577271, -0.253187149763107], [11.6635713577271, -0.148332044482231], [11.5629177093506, -0.148332044482231]]]}, 'source': ['https://bmap-catalogue-data.oss.eu-west-0.prod-cloud-ocb.orange-business.com/Campaign_data/afrisar_dlr/afrisar_dlr_T2-0_SLC_HV.tiff']}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "datasetId=\"AFRISAR_DLR\"\n",
+    "subDatasetId=\"AFRISAR_DLR_SLC_HV\"\n",
+    "SceneType_value=\"T2-0\"\n",
+    "req=requests.get(endpoint+\"/opensearch/search\",params={\"SceneType_value\":SceneType_value,\"datasetId\":datasetId,\"subDatasetId\":subDatasetId})\n",
+    "if req.status_code==200:\n",
+    "    result=req.json()[\"features\"]\n",
+    "    print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 To download the product/granule of interest <br>\n",
+    "Below you see the code to download the products/granules of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://bmap-catalogue-data.oss.eu-west-0.prod-cloud-ocb.orange-business.com/Campaign_data/afrisar_dlr/afrisar_dlr_T2-0_SLC_HV.tiff\n"
+     ]
+    }
+   ],
+   "source": [
+    "if len(result) >0:\n",
+    "        for feature in result:\n",
+    "            if feature[\"metadata\"][\"geolocated\"]:\n",
+    "                wcs_query=endpoint+\"/wcs?\"+feature[\"sourceRasterGeo\"]\n",
+    "            else:\n",
+    "                wcs_query=feature[\"source\"][0]\n",
+    "            inputFilename = feature[\"metadata\"][\"identifier\"]+\".tif\"\n",
+    "            print(wcs_query)\n",
+    "            r = requests.get(wcs_query,stream=True)\n",
+    "            if r.status_code == 200:\n",
+    "                with open(inputFilename, 'wb') as f:\n",
+    "                    for chunk in r:\n",
+    "                        f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 To access the product/granule of interest directly in the bucket<br>\n",
+    "Below you see the code to access the products/granules of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://bmap-catalogue-data.oss.eu-west-0.prod-cloud-ocb.orange-business.com/Campaign_data/afrisar_dlr/afrisar_dlr_T2-0_SLC_HV.tiff\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "if len(result) >0:\n",
+    "        for feature in result:\n",
+    "            if feature[\"metadata\"][\"geolocated\"]:\n",
+    "                wcs_query=endpoint+\"/wcs?\"+feature[\"sourceRasterGeo\"]\n",
+    "            else:\n",
+    "                wcs_query=feature[\"source\"][0]\n",
+    "print(wcs_query)\n",
+    "os.environ[\"GDAL_DISABLE_READDIR_ON_OPEN\"]=\"EMPTY_DIR\"\n",
+    "try:\n",
+    "    input_image_driver = gdal.Open(\"/vsicurl/\"+wcs_query, GA_ReadOnly)\n",
+    "except Exception as e:\n",
+    "    print(e)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display SAR image in slant range geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Open DEM and compute local incidence angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define SAR image parameters "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pixel_spacing_rg = 1.1988876\n",
+    "z_flight = 6383.36\n",
+    "z_terrain = 287.50\n",
+    "SLR_start = 6536.3352"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Project DEM from ground-range to slant-range (sensor) geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'projectors'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_5244/3271512043.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlutFile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/projects/data/afrisar_dlr/afrisar_dlr_T2-0_lut.tiff'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mprojectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprojectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGrdToSlrProj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdemGrdFile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdemSlrFile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlutFile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputFilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'projectors'"
+     ]
+    }
+   ],
+   "source": [
+    "demGrdFile = '/projects/data/afrisar_dlr/afrisar_dlr_dem_S_T2-0.tiff'\n",
+    "demSlrFile = '/projects/demSR.tiff'\n",
+    "lutFile = '/projects/data/afrisar_dlr/afrisar_dlr_T2-0_lut.tiff'\n",
+    "\n",
+    "import projectors\n",
+    "projectors.GrdToSlrProj(demGrdFile, demSlrFile, lutFile, inputFilename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Open DEM in slant range geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_driver = gdal.Open(demSlrFile, GA_ReadOnly)\n",
+    "dem = dem_driver.ReadAsArray()\n",
+    "dem_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialise structures and compute of local slope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "angle1 = np.full((RasterYSize, RasterXSize), np.NaN, dtype=dem.dtype)\n",
+    "angle2 = np.full((RasterYSize, RasterXSize), np.NaN, dtype=dem.dtype)\n",
+    "\n",
+    "angle1[:, 1:-1] = np.arctan2(dem[:, 1:-1] - dem[:, :-2], pixel_spacing_rg)\n",
+    "angle2[:, 1:-1] = np.arctan2(dem[:, 2:] - dem[:, 1:-1], pixel_spacing_rg)\n",
+    "angle = (angle1 + angle2)/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Filter angle map (boxcarFilter 5x5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = [5, 5]\n",
+    "angle = sg.convolve2d(angle, np.ones(size), 'same') / (size[0] * size[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Local incidence angle computation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta_inc_map = np.abs(np.arccos((z_flight - z_terrain) / (SLR_start + np.mgrid[:RasterYSize, :RasterXSize][1] * pixel_spacing_rg)) - angle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display incidence angle map in slant range geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(theta_inc_map)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Calibrate SAR image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calibration of the SAR data to Sigma0 (natural)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma0 = np.absolute(input_image)**2 * np.sin(theta_inc_map)\n",
+    "\n",
+    "# Close dataset to save memory:\n",
+    "theta_inc_map = None\n",
+    "\n",
+    "# Filter bad data:\n",
+    "sigma0[sigma0 <= 0] = np.NaN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example of basic multilooking: 5x5 boxcar filter (can be changed to increase number of looks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = [5, 5]\n",
+    "sigma0 = sg.convolve2d(sigma0, np.ones(size), 'same') / (size[0] * size[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Convert to dB and set a threshold to -45 dB (arbitrary value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma0 = 10 * np.log10(sigma0)\n",
+    "sigma0[sigma0 < -45] = -45"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display sigma0 in slant range geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(sigma0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save calibrated sigma0 in slant-range geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slrFile = '/projects/tempSR.tiff'\n",
+    "\n",
+    "# Save output image in slant range geometry:\n",
+    "outdriver = gdal.GetDriverByName('GTiff')\n",
+    "output_image_driver = outdriver.Create(slrFile, RasterXSize, RasterYSize, 1, gdal.GDT_Float32)\n",
+    "output_image_driver.GetRasterBand(1).WriteArray(sigma0)\n",
+    "output_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Maap",
+   "language": "python",
+   "name": "maap"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/edav/.ipynb_checkpoints/edav_make_subset_from_catalog_data-checkpoint.ipynb b/edav/.ipynb_checkpoints/edav_make_subset_from_catalog_data-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..23ce68770c6e74b52cbfb33967f01c639e328465
--- /dev/null
+++ b/edav/.ipynb_checkpoints/edav_make_subset_from_catalog_data-checkpoint.ipynb
@@ -0,0 +1,2114 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Examples of how to download subsets from all catalogues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from osgeo import gdal\n",
+    "from gdalconst import GA_ReadOnly\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "\n",
+    "# Increase figure size (can be modified for bigger or smaller figures):\n",
+    "plt.rcParams[\"figure.figsize\"]=20,20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. MAAP catalogue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.1 ESACCI_Biomass_L4_AGB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the ESACCI_Biomass_L4_AGB catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=ESACCI_Biomass_L4_AGB&subdataset=ESACCI_Biomass_L4_AGB_V3_100m_2017&format=image/tiff&scale=1&subset=E(-18.503442213375415,-10.073629233137138)&subset=N(5.046412153032291,14.73795792147569)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"ESACCI_Biomass_L4_AGB_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.2 AFRISAR_ONERA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the AFRISAR_ONERA catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=AFRISAR_ONERA_geo&subdataset=AFRISAR_ONERA_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(11.55547126285688,11.57744824012809)&subset=N(-0.17302461095001961,-0.1580452048373097)&subset=gfix(0,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"AFRISAR_ONERA_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3 INDREX2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the INDREX2 catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=INDREX2_geo&subdataset=INDREX2_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(185434.1803453705,187520.56511637475)&subset=N(9755613.06181317,9757030.829564102)&subset=gfix(30,3,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"INDREX2_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.4 AFRISAR_DLR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the AFRISAR_DLR catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=AFRISAR_DLR_geo&subdataset=AFRISAR_DLR_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(781057.6133006841,785260.3859418712)&subset=N(9981175.767962307,9983140.299606618)&subset=gfix(0,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"AFRISAR_DLR_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.5 TROPISAR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the TROPISAR catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Error Unable to initialize map layer: Error: Invalid dataset\n",
+    "data=\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"name_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.6 BIOSAR1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the BIOSAR1 catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=BIOSAR1_geo&subdataset=BIOSAR1_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(418757.0034565398,420493.27866398)&subset=N(6480439.082605348,6481692.267497467)&subset=gfix(0,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"BIOSAR1_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.7 BIOSAR2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the BIOSAR2 catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=BIOSAR2_geo&subdataset=BIOSAR2_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(440374.66908023704,442822.6655061027)&subset=N(7124049.977843571,7125834.5473589115)&subset=gfix(1,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"BIOSAR2_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.8 BIOSAR3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the BIOSAR3 catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=BIOSAR3_geo&subdataset=BIOSAR3_geo_SLC_HH_Q&format=image/tiff&scale=1&subset=E(13.597326735128647,13.642148785546611)&subset=N(58.44397335355248,58.463935835937825)&subset=gfix(0,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"BIOSAR3_geo.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. NASA Catalogue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.1 afriSAR UAVSAR Coregistered SLCs Generated Using NISAR Tools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the afriSAR UAVSAR Coregistered SLCs Generated Using NISAR Tools catalog under NASA catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Unable to download data - network error\n",
+    "data=\"http://edav-backend-mwcsnasa.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=AfriSAR_UAVSAR_Coreg_SLC&subdataset=AfriSAR_UAVSAR_Coreg_SLC_Amplitude&format=image/tiff&scale=1&subset=E(11.570941310668786,11.62526469430005)&subset=N(-0.21955930705437302,-0.18072951081086272)&subset=gfix(14051,16008)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"AfriSAR_UAVSAR_Coreg_SLC_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.2 afriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon catalog under NASA catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcsnasa.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=AfriSAR_AGB_Maps_1681&subdataset=AfriSAR_AGB_Maps_1681_AGB&format=image/tiff&scale=1&subset=E(11.574307387845574,11.620327350374728)&subset=N(-0.20981758568561937,-0.18424023158060163)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"AfriSAR_AGB_Maps_1681_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Creodias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1 Sentinel-2 T30TXQ"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Sentinel-2 T30TXQ catalog under Creodias tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://maap-creodias.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=S2A_MSIL2A_T30TXQ&subdataset=S2A_MSIL2A_T30TXQ_R10m&format=image/tiff&scale=1&subset=unix(2021-10-12T10:50:11.000Z)&subset=E(688819.5080151197,705606.3492477958)&subset=N(4904015.282523986,4915636.427466001)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"S2A_MSIL2A_T30TXQ_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2 Sentinel-2 T38UNV"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Sentinel-2 T38UNV catalog under Creodias tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://maap-creodias.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=S2B_MSIL2A_T38UNV&subdataset=S2B_MSIL2A_T38UNV_R10m&format=image/tiff&scale=1&subset=unix(2021-10-13T07:48:49.000Z)&subset=E(592684.1859859176,608602.4328974131)&subset=N(5401264.404860623,5414233.69929265)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"S2B_MSIL2A_T38UNV_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. External data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.1 GEDI Congo derived height"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the GEDI Congo derived height catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://edav-wcs.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=GEDI_icesat&format=image/tiff&scale=1&subset=band(1)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"GEDI_icesat_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.2 Tomographic biomass Onera Lop (GEO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Tomographic biomass Onera Lop (GEO) catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"name_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.3 Tomographic biomass Onera Lop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Tomographic biomass Onera Lop catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"name_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.4 Globbiomass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Tomographic biomass Onera Lop (GEO) catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://edav-wcs.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=GLOBBIOMASS_AGB_4326_0000889&format=image/tiff&scale=1&subset=E(32.94590731739739,35.06996024904108)&subset=N(57.82933588526262,58.89517721149518)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"GLOBBIOMASS_AGB_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.5 Biosar1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Tomographic biomass Onera Lop (GEO) catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://edav-wcs.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=biosar1_SLC&format=image/tiff&scale=1&subset=E(419263.2451962733,421266.27823438466)&subset=N(6480208.385313987,6482720.40567584)&subset=gfix(105,11,1)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"biosar1_SLC_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Maap",
+   "language": "python",
+   "name": "maap"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/edav/edav_make_subset_from_catalog_data.ipynb b/edav/edav_make_subset_from_catalog_data.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..23ce68770c6e74b52cbfb33967f01c639e328465
--- /dev/null
+++ b/edav/edav_make_subset_from_catalog_data.ipynb
@@ -0,0 +1,2114 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Examples of how to download subsets from all catalogues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from osgeo import gdal\n",
+    "from gdalconst import GA_ReadOnly\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "\n",
+    "# Increase figure size (can be modified for bigger or smaller figures):\n",
+    "plt.rcParams[\"figure.figsize\"]=20,20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. MAAP catalogue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.1 ESACCI_Biomass_L4_AGB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the ESACCI_Biomass_L4_AGB catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=ESACCI_Biomass_L4_AGB&subdataset=ESACCI_Biomass_L4_AGB_V3_100m_2017&format=image/tiff&scale=1&subset=E(-18.503442213375415,-10.073629233137138)&subset=N(5.046412153032291,14.73795792147569)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"ESACCI_Biomass_L4_AGB_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.2 AFRISAR_ONERA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the AFRISAR_ONERA catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=AFRISAR_ONERA_geo&subdataset=AFRISAR_ONERA_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(11.55547126285688,11.57744824012809)&subset=N(-0.17302461095001961,-0.1580452048373097)&subset=gfix(0,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"AFRISAR_ONERA_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3 INDREX2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the INDREX2 catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=INDREX2_geo&subdataset=INDREX2_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(185434.1803453705,187520.56511637475)&subset=N(9755613.06181317,9757030.829564102)&subset=gfix(30,3,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"INDREX2_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.4 AFRISAR_DLR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the AFRISAR_DLR catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=AFRISAR_DLR_geo&subdataset=AFRISAR_DLR_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(781057.6133006841,785260.3859418712)&subset=N(9981175.767962307,9983140.299606618)&subset=gfix(0,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"AFRISAR_DLR_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.5 TROPISAR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the TROPISAR catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Error Unable to initialize map layer: Error: Invalid dataset\n",
+    "data=\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"name_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.6 BIOSAR1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the BIOSAR1 catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=BIOSAR1_geo&subdataset=BIOSAR1_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(418757.0034565398,420493.27866398)&subset=N(6480439.082605348,6481692.267497467)&subset=gfix(0,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"BIOSAR1_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.7 BIOSAR2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the BIOSAR2 catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=BIOSAR2_geo&subdataset=BIOSAR2_geo_SLC_HH_I&format=image/tiff&scale=1&subset=E(440374.66908023704,442822.6655061027)&subset=N(7124049.977843571,7125834.5473589115)&subset=gfix(1,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"BIOSAR2_geo_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.8 BIOSAR3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the BIOSAR3 catalog under MAAP catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcs.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=BIOSAR3_geo&subdataset=BIOSAR3_geo_SLC_HH_Q&format=image/tiff&scale=1&subset=E(13.597326735128647,13.642148785546611)&subset=N(58.44397335355248,58.463935835937825)&subset=gfix(0,0,0)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"BIOSAR3_geo.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. NASA Catalogue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.1 afriSAR UAVSAR Coregistered SLCs Generated Using NISAR Tools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the afriSAR UAVSAR Coregistered SLCs Generated Using NISAR Tools catalog under NASA catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Unable to download data - network error\n",
+    "data=\"http://edav-backend-mwcsnasa.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=AfriSAR_UAVSAR_Coreg_SLC&subdataset=AfriSAR_UAVSAR_Coreg_SLC_Amplitude&format=image/tiff&scale=1&subset=E(11.570941310668786,11.62526469430005)&subset=N(-0.21955930705437302,-0.18072951081086272)&subset=gfix(14051,16008)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"AfriSAR_UAVSAR_Coreg_SLC_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.2 afriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon catalog under NASA catalogue tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"http://edav-backend-mwcsnasa.edav:680/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=AfriSAR_AGB_Maps_1681&subdataset=AfriSAR_AGB_Maps_1681_AGB&format=image/tiff&scale=1&subset=E(11.574307387845574,11.620327350374728)&subset=N(-0.20981758568561937,-0.18424023158060163)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"AfriSAR_AGB_Maps_1681_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Creodias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1 Sentinel-2 T30TXQ"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Sentinel-2 T30TXQ catalog under Creodias tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://maap-creodias.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=S2A_MSIL2A_T30TXQ&subdataset=S2A_MSIL2A_T30TXQ_R10m&format=image/tiff&scale=1&subset=unix(2021-10-12T10:50:11.000Z)&subset=E(688819.5080151197,705606.3492477958)&subset=N(4904015.282523986,4915636.427466001)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"S2A_MSIL2A_T30TXQ_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2 Sentinel-2 T38UNV"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Sentinel-2 T38UNV catalog under Creodias tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://maap-creodias.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=S2B_MSIL2A_T38UNV&subdataset=S2B_MSIL2A_T38UNV_R10m&format=image/tiff&scale=1&subset=unix(2021-10-13T07:48:49.000Z)&subset=E(592684.1859859176,608602.4328974131)&subset=N(5401264.404860623,5414233.69929265)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"S2B_MSIL2A_T38UNV_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. External data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.1 GEDI Congo derived height"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the GEDI Congo derived height catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://edav-wcs.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=GEDI_icesat&format=image/tiff&scale=1&subset=band(1)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"GEDI_icesat_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.2 Tomographic biomass Onera Lop (GEO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Tomographic biomass Onera Lop (GEO) catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"name_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.3 Tomographic biomass Onera Lop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Tomographic biomass Onera Lop catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"name_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.4 Globbiomass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Tomographic biomass Onera Lop (GEO) catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://edav-wcs.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=GLOBBIOMASS_AGB_4326_0000889&format=image/tiff&scale=1&subset=E(32.94590731739739,35.06996024904108)&subset=N(57.82933588526262,58.89517721149518)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"GLOBBIOMASS_AGB_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.5 Biosar1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Copy/paste data selection from front-end "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Click on Add datasets and select the Tomographic biomass Onera Lop (GEO) catalog under External data tab\n",
+    "\n",
+    "###### Zoom to dataset area\n",
+    "    Draw a bbox :\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Drow the bbox\n",
+    "        Click OK\n",
+    "    or Draw a polygon\n",
+    "        1 - Select the are of interest bbox\n",
+    "        2- choose draw bbox\n",
+    "        3- Draw the polygon and finish by a double click for the last point.\n",
+    "        Click OK\n",
+    "\n",
+    "The copy to clipboard can be done on the dataset, a subset bbox or a subset polygon. You can choose the output format and the scale.\n",
+    "Click on download and the to \"Copy download request\" and past url in the variable \"data\" in the cell below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Paste the url of the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=\"https://edav-wcs.adamplatform.eu/wcs?service=WCS&request=GetCoverage&version=2.0.0&coverageId=biosar1_SLC&format=image/tiff&scale=1&subset=E(419263.2451962733,421266.27823438466)&subset=N(6480208.385313987,6482720.40567584)&subset=gfix(105,11,1)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset=requests.get(data, stream=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Enter the name of your file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputFilename=\"biosar1_SLC_subset.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Download the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if subset.status_code==200:\n",
+    "    with open(inputFilename, 'wb') as f:\n",
+    "        for chunk in subset:\n",
+    "            f.write(chunk)\n",
+    "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+    "input_image = input_image_driver.ReadAsArray()\n",
+    "RasterXSize = input_image_driver.RasterXSize\n",
+    "RasterYSize = input_image_driver.RasterYSize\n",
+    "input_image_driver = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display the subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgplot = plt.imshow(np.absolute(input_image))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Maap",
+   "language": "python",
+   "name": "maap"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}