tutorials/notebooks/11_download_sentinel_imagery.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download Sentinel imagery\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can download Sentinel imagery through the EOTDL.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment the following line to install eotdl if needed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install eotdl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we can look for which Sentinel sensors are supported in the EOTDL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sentinel-1-grd', 'sentinel-2-l1c', 'sentinel-2-l2a', 'dem')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from eotdl.access import SUPPORTED_SENSORS\n",
    "\n",
    "SUPPORTED_SENSORS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = (\"2020-01-01\", \"2020-01-10\")\n",
    "jaca_bounding_box = (-0.557034, 42.569409, -0.547706, 42.57571)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if you want to download images given a point and a pixel size? Very easy! It can also be done with the EOTDL: the only thing you have to give is the width and height in pixels, and the pixel size. However, for the example we will continue using the Jaca bounding box.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Important! The pixel size must be in meters!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[41.77619798649038, 12.677125011757084, 41.82380625978242, 12.722871923867354]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from eotdl.tools import bbox_from_centroid\n",
    "\n",
    "x = 12.7\n",
    "y = 41.8\n",
    "\n",
    "custom_bbox = bbox_from_centroid(x=x, y=y, pixel_size=10, width=512, height=512)\n",
    "custom_bbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can search for available Sentinel-2-L2A imagery in Jaca, Spain, between 1 to 10 January, 2020, as seen in the previous [notebook](10_search_sentinel_imagery.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 'S2B_MSIL2A_20200109T110329_N0213_R094_T30TXN_20200109T121919',\n",
       "  'properties': {'datetime': '2020-01-09T11:09:11Z', 'eo:cloud_cover': 17.46}},\n",
       " {'id': 'S2B_MSIL2A_20200109T110329_N0213_R094_T30TYN_20200109T121919',\n",
       "  'properties': {'datetime': '2020-01-09T11:09:07Z', 'eo:cloud_cover': 0.01}},\n",
       " {'id': 'S2B_MSIL2A_20200106T105339_N0213_R051_T30TXN_20200106T121433',\n",
       "  'properties': {'datetime': '2020-01-06T10:59:14Z', 'eo:cloud_cover': 0.95}}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from eotdl.access import search_sentinel_imagery\n",
    "\n",
    "r = search_sentinel_imagery(dates, jaca_bounding_box, \"sentinel-2-l2a\")\n",
    "list(r)[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> DEM data is quite especial, as it is static and we can not search for available data. If we do, we will find that the collection does not exists. On the other hand, if we want to download DEM data it is not necessary to give a time interval neither.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen, we have several available images. Let's download the first of them, from the 2020-01-04.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from eotdl.access import download_sentinel_imagery\n",
    "\n",
    "date = \"2020-01-04\"\n",
    "\n",
    "download_sentinel_imagery(\"data/jaca\", date, jaca_bounding_box, \"sentinel-2-l2a\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As easy as that! Let's take a look on the image!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/jaca/sentinel-2-l2a_2020-01-04.tif'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from glob import glob\n",
    "\n",
    "raster = glob(\"data/jaca/*.tif\")[0]\n",
    "raster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(12, 72, 75)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rasterio as rio\n",
    "\n",
    "x = rio.open(raster).read()\n",
    "x.shape # should be 512x512..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x77719696c8b0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rgb = x[[3, 2, 1], ...] / 4000\n",
    "plt.imshow(rgb.transpose(1, 2, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please restart Jupyter kernel after installation if you encounter any errors when importing leafmap.\n"
     ]
    },
    {
     "ename": "Exception",
     "evalue": "'type' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/eotdl/lib/python3.8/site-packages/leafmap/__init__.py:130\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 130\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mleafmap\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/miniconda3/envs/eotdl/lib/python3.8/site-packages/leafmap/leafmap.py:23\u001b[0m\n\u001b[1;32m     20\u001b[0m basemaps \u001b[38;5;241m=\u001b[39m Box(xyz_to_leaflet(), frozen_box\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mMap\u001b[39;00m(ipyleaflet\u001b[38;5;241m.\u001b[39mMap):\n\u001b[1;32m     24\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"The Map class inherits ipyleaflet.Map. The arguments you can pass to the Map can be found at https://ipyleaflet.readthedocs.io/en/latest/api_reference/map.html. By default, the Map will add OpenStreetMap as the basemap.\u001b[39;00m\n\u001b[1;32m     25\u001b[0m \n\u001b[1;32m     26\u001b[0m \u001b[38;5;124;03m    Returns:\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;124;03m        object: ipyleaflet map object.\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/eotdl/lib/python3.8/site-packages/leafmap/leafmap.py:1961\u001b[0m, in \u001b[0;36mMap\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1957\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(e)\n\u001b[1;32m   1959\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_colorbar\u001b[39m(\n\u001b[1;32m   1960\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m-> 1961\u001b[0m     colors: Union[\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m]\u001b[49m, \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mint\u001b[39m]],\n\u001b[1;32m   1962\u001b[0m     vmin: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   1963\u001b[0m     vmax: Optional[\u001b[38;5;28mfloat\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m,\n\u001b[1;32m   1964\u001b[0m     index: Optional[\u001b[38;5;28mlist\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1965\u001b[0m     caption: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1966\u001b[0m     categorical: Optional[\u001b[38;5;28mbool\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   1967\u001b[0m     step: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1968\u001b[0m     height: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m45px\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1969\u001b[0m     transparent_bg: Optional[\u001b[38;5;28mbool\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   1970\u001b[0m     position: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbottomright\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1971\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   1972\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1973\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Add a branca colorbar to the map.\u001b[39;00m\n\u001b[1;32m   1974\u001b[0m \n\u001b[1;32m   1975\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1986\u001b[0m \n\u001b[1;32m   1987\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'type' object is not subscriptable",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mleafmap\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mleafmap\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mleafmap\u001b[39;00m\n\u001b[1;32m      3\u001b[0m m \u001b[38;5;241m=\u001b[39m leafmap\u001b[38;5;241m.\u001b[39mMap()\n\u001b[1;32m      4\u001b[0m m\u001b[38;5;241m.\u001b[39madd_raster(raster, bands\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m3\u001b[39m], layer_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRaster\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/eotdl/lib/python3.8/site-packages/leafmap/__init__.py:140\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m    138\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease restart Jupyter kernel after installation if you encounter any errors when importing leafmap.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    139\u001b[0m     )\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(e)\n",
      "\u001b[0;31mException\u001b[0m: 'type' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "import leafmap.leafmap as leafmap\n",
    "\n",
    "m = leafmap.Map()\n",
    "m.add_raster(raster, bands=[5, 4, 3], layer_name=\"Raster\")\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, we may want to download all the available images within a time interval. This can also be handled by the EODL, using the same function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "download_sentinel_imagery(\n",
    "    output=\"data/jaca_bulk\",\n",
    "    time_interval=dates,\n",
    "    bounding_box=jaca_bounding_box,\n",
    "    sensor=\"sentinel-2-l2a\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, if we do not give the downloaded images a name, their output name will be `<sensor>_<date>`. However, we can give the images an output name to have more control. Thus, if we only download an image and name it `Jaca`, the image will be saved as `Jaca`. If, on the other hand, several images are downloaded, they will be saved as `Jaca_<date>`. Thus, we can customize the name according to our needs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will save the image as `Jaca.tif`\n",
    "download_sentinel_imagery(\n",
    "    output=\"data/jaca_bulk\",\n",
    "    time_interval=\"2020-01-04\",\n",
    "    bounding_box=jaca_bounding_box,\n",
    "    sensor=\"sentinel-2-l2a\",\n",
    "    name=\"Jaca\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will save the images as `Jaca_<date>.tif`\n",
    "download_sentinel_imagery(\n",
    "    output=\"data/jaca_bulk\",\n",
    "    time_interval=(\"2020-01-01\", \"2020-01-10\"),\n",
    "    bounding_box=jaca_bounding_box,\n",
    "    sensor=\"sentinel-2-l2a\",\n",
    "    name=\"Jaca\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we want to see a random image? We can display it and explore it interactively using [leafmap](https://leafmap.org/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/jaca_bulk/sentinel-2-l2a_2020-01-04.tif',\n",
       " 'data/jaca_bulk/sentinel-2-l2a_2020-01-06.tif',\n",
       " 'data/jaca_bulk/sentinel-2-l2a_2020-01-01.tif',\n",
       " 'data/jaca_bulk/sentinel-2-l2a_2020-01-09.tif']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from glob import glob\n",
    "\n",
    "rasters = glob(\"data/jaca_bulk/*.tif\")\n",
    "rasters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n",
      "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n",
      "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n",
      "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84f38bd47e66479b91489cb445960fce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[20, 0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text…"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import leafmap.leafmap as leafmap\n",
    "from random import choice\n",
    "\n",
    "m = leafmap.Map()\n",
    "raster = choice(rasters)\n",
    "m.add_raster(raster, bands=[5, 4, 3], layer_name=\"Raster\")\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = \"2022-08-12\"\n",
    "bbox = (12.04609, 55.83039, 12.06563, 55.84236)\n",
    "download_sentinel_imagery(\n",
    "    output=\"data/test_bulk\",\n",
    "    time_interval=date,\n",
    "    bounding_box=bbox,\n",
    "    sensor=\"sentinel-2-l2a\",\n",
    ")"
   ]
  }
 ],
 "metadata": {
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "nbconvert_exporter": "python",
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