{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73093fd7-88df-41e9-a530-4b0b30570e42",
   "metadata": {
    "tags": []
   },
   "source": [
    "# MODIS Level 3 - Burned Area Monthly - 500m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736b4328-2822-418f-96d1-a36f9c77da79",
   "metadata": {},
   "source": [
    "```{hint} \n",
    "<a href='https://fire.ltpy.adamplatform.eu/hub/user-redirect/lab/tree/med_part3_workflows/figure1_MODIS_BurnedArea_Monthly.ipynb' target='_blank'>Execute the notebook on the training platform >></a>\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd512f1-7274-48e6-a752-00f6079b6180",
   "metadata": {},
   "source": [
    "This notebook provides you an introduction to data from the [Moderate Resolution Imaging Spectroradiometer (MODIS)](https://modis.gsfc.nasa.gov/about/). It uses MODIS as a proxy dataset for [METImage](https://www.eumetsat.int/eps-sg-metimage) on EPS-SG, which is a multi-spectral (visible and IR) imaging passive radiometer which will provide detailed information on clouds, wind, aerosols and surface properties which are essential for meteorological and climate applications. \n",
    "\n",
    "The events featured in this notebook are the wildfires in Italy and Greece in summer 2021. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964160b4-1f43-4477-9c90-946089e5a7a9",
   "metadata": {},
   "source": [
    "```{admonition} Basic Facts\n",
    "**Spatial resolution**: `500 m at nadir`<br>\n",
    "**Spatial coverage**: `Global` <br>\n",
    "**Time step**: `Monthly` <br>\n",
    "**Data availability**: `since 2000`\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b379317-5d89-4dd6-a5f9-71a517f25ad5",
   "metadata": {},
   "source": [
    "```{admonition} How to access the data\n",
    "This notebook uses the MODIS MCD64A1 dataset from the Terra and Aqua platforms.  This data can be ordered via the [LAADS DAAC](https://ladsweb.modaps.eosdis.nasa.gov/search/order/2/MCD64A1--6) and are distributed in `HDF4-EOS` format, which is based on `HDF4`. \n",
    "\n",
    "You need to [register for an Earthdata account](https://urs.earthdata.nasa.gov/) before being able to download data.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032ecb34-13db-4a7d-a32a-9279f46d4596",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecff1c8d-cd59-4e85-ba12-1aeeeeb44c3f",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Load required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d2b5995-0a3c-4d78-a185-d06280a530d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "from osgeo import gdal,osr\n",
    "import pyproj\n",
    "import pandas as pd\n",
    "import glob\n",
    "\n",
    "# Python libraries for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "from matplotlib.axes import Axes\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "from cartopy.mpl.geoaxes import GeoAxes\n",
    "GeoAxes._pcolormesh_patched = Axes.pcolormesh\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter(action = \"ignore\", category = RuntimeWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17afd4ec-12d6-4750-ae01-60660c566ae8",
   "metadata": {},
   "source": [
    "**Load helper functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c921c23-2adb-4368-acb0-38b0a5011616",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run ../functions.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c09368-89bd-46a9-98f0-476ed1b8a1e9",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a57d1d0-c587-4829-9bb0-77bc0b4af3f5",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load and browse MODIS Aerosol Product data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560eb454-f5ee-4071-bc1a-67db0fcb07d9",
   "metadata": {},
   "source": [
    "You will use the Python library `gdal` to open a HDF4 data file from 8th September 2015. Read more about `gdal` [here](https://gdal.org/). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f4b3b3-2adc-4555-ace0-16359dd5e1ba",
   "metadata": {},
   "source": [
    "### Inspect the structure of one MODIS MOD04 data file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59436c64-88e3-4bf7-923f-944680c6bb71",
   "metadata": {},
   "source": [
    "The data is from August 2020 and is stored in the folder `../data/modis/level3/burnedarea/2020/08/`. You can use the function `gdal.Open(file_path)` to load one single file to better understand the data structure. The results in a `gdal.Dataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ea99594-7194-413d-bb47-4fe75d1c0abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<osgeo.gdal.Dataset; proxy of <Swig Object of type 'GDALDatasetShadow *' at 0x7f358467b990> >"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = '../data/modis/level3/burnedarea/2021/08/MCD64A1.A2021213.h19v05.061.2021309115552.hdf'\n",
    "\n",
    "hdf_handle = gdal.Open(file_path)\n",
    "hdf_handle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac711b8f-db34-4f3e-8476-19659bafcf5e",
   "metadata": {},
   "source": [
    "Next, you can print a list of all of the datasets within the hdf file using the function `.GetSubDatasets()` from the gdal library. There are several sub-datasets printed:\n",
    "\n",
    "- `Burn Date`: the burn date in ordinal days\n",
    "- `Burn Date Uncertainty`: the uncertainty value for the burn date\n",
    "- `QA`: the quality assurance dataset\n",
    "- `First Day`: the first day of burn\n",
    "- `Last Day`: the last day of burn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2941d084-1ee9-407f-b539-8ae25dd41299",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('HDF4_EOS:EOS_GRID:\"../data/modis/level3/burnedarea/2021/08/MCD64A1.A2021213.h19v05.061.2021309115552.hdf\":MOD_Grid_Monthly_500m_DB_BA:\"Burn Date\"', '[2400x2400] Burn Date MOD_Grid_Monthly_500m_DB_BA (16-bit integer)')\n",
      "('HDF4_EOS:EOS_GRID:\"../data/modis/level3/burnedarea/2021/08/MCD64A1.A2021213.h19v05.061.2021309115552.hdf\":MOD_Grid_Monthly_500m_DB_BA:\"Burn Date Uncertainty\"', '[2400x2400] Burn Date Uncertainty MOD_Grid_Monthly_500m_DB_BA (8-bit unsigned integer)')\n",
      "('HDF4_EOS:EOS_GRID:\"../data/modis/level3/burnedarea/2021/08/MCD64A1.A2021213.h19v05.061.2021309115552.hdf\":MOD_Grid_Monthly_500m_DB_BA:QA', '[2400x2400] QA MOD_Grid_Monthly_500m_DB_BA (8-bit unsigned integer)')\n",
      "('HDF4_EOS:EOS_GRID:\"../data/modis/level3/burnedarea/2021/08/MCD64A1.A2021213.h19v05.061.2021309115552.hdf\":MOD_Grid_Monthly_500m_DB_BA:\"First Day\"', '[2400x2400] First Day MOD_Grid_Monthly_500m_DB_BA (16-bit integer)')\n",
      "('HDF4_EOS:EOS_GRID:\"../data/modis/level3/burnedarea/2021/08/MCD64A1.A2021213.h19v05.061.2021309115552.hdf\":MOD_Grid_Monthly_500m_DB_BA:\"Last Day\"', '[2400x2400] Last Day MOD_Grid_Monthly_500m_DB_BA (16-bit integer)')\n"
     ]
    }
   ],
   "source": [
    "sds_list = hdf_handle.GetSubDatasets()\n",
    "\n",
    "for sds in sds_list:\n",
    "    print(sds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f2769f-78ff-45a5-8d2b-9f8f774057e9",
   "metadata": {},
   "source": [
    "Next, you open the sub-datasets for the burn date and QA using the function `gdal.Open()`. \n",
    "\n",
    "The subdatasets are organized as an array. You can use `sds_list[D][0]` to select the dataset of interest, the relevant values of D are:\n",
    "- `0`: Burn Date\n",
    "- `1`: Burn Date Uncertainty\n",
    "- `2`: QA\n",
    "- `3`: First Day\n",
    "- `4`: Last Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "004acf1f-f348-4f39-8e50-6da055dfbb94",
   "metadata": {},
   "outputs": [],
   "source": [
    "burndate_handle = gdal.Open(sds_list[0][0])\n",
    "qa_handle = gdal.Open(sds_list[2][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0736a6f4-e41a-47c6-b0d6-b28ff35ac6fa",
   "metadata": {},
   "source": [
    "Next, you can use the function `.ReadAsArray()` from the gdal library to read in the data. You also use the numpy library to set the type as float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "467b94b0-ad56-48da-aaa6-bfe52c486230",
   "metadata": {},
   "outputs": [],
   "source": [
    "burndate_data = burndate_handle.ReadAsArray().astype(np.float64)\n",
    "qa_data = qa_handle.ReadAsArray().astype(np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7518d72e-ccf7-44ea-a19a-3110592b2e50",
   "metadata": {},
   "source": [
    "Now, you can use the numpy library to take a look at the shape and type of the data array. You can inspect the shape using `np.shape()` and get the data type using `.dtype`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd71cb87-9536-4d26-abd2-473d5164ef18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2400, 2400)\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(burndate_data))\n",
    "print(qa_data.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c41f98-6391-4892-84be-d4194db73c86",
   "metadata": {},
   "source": [
    "The `burndate_data` variable contains an array of the burn date values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fea32324-3354-458b-afb9-c0710120245f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2., -2., -2., ..., -2., -2., -2.],\n",
       "       [-2., -2., -2., ..., -2., -2., -2.],\n",
       "       [-2., -2., -2., ..., -2., -2., -2.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "burndate_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c11cc17-a5e7-471c-916c-d54debdf74e9",
   "metadata": {},
   "source": [
    "The maximum value of the burn dates can be viewed using the function `.max()` from the numpy library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4602e5f7-110e-438f-9d99-d079b441c913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "burndate_data.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4573e8-bea9-437c-991e-96b8e867304c",
   "metadata": {},
   "source": [
    "### Retrieve attributes fom the metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81526bd-69a8-4f08-8bf5-538c216196f0",
   "metadata": {},
   "source": [
    "Next, you need to retrieve attributes from the dataset for plotting purposes using the function `.GetMetadata()`. The first step is to store the global attributes dictionary from the dataset as a variable called `meta`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c23d5728-6ffe-4026-92d4-f3c87f9f41e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "meta = burndate_handle.GetMetadata()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34871d20-028b-4999-a013-46dcc6235c19",
   "metadata": {},
   "source": [
    "The attributes dictionary includes a few useful attributes including `long_name`, `_FillValue`, `_WaterValue`, and `valid_range`. You can define variables for these for use in visualizing the data later. It is also important to assign the float data type using `np.float` to the `_FillValue`, `_WaterValue` and`valid_range`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0ec41e1-07f6-4fb4-8f86-8894e0f0843a",
   "metadata": {},
   "outputs": [],
   "source": [
    "_FillValue = np.float(meta['_FillValue'])\n",
    "long_name = meta['long_name']\n",
    "_WaterValue = np.float(meta['water'])\n",
    "valid_range = [np.float(x) for x in meta['valid_range'].split(', ')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d6136f-2b3c-4689-a5ab-73fc817a96eb",
   "metadata": {},
   "source": [
    "### Replace fill, water and unburned value pixels with nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3c8ca5-9ebc-4e2a-a7ef-1ff5e50322ed",
   "metadata": {},
   "source": [
    "Next, you can replace all data which has the value of `-1`, which is the fill value, with `nan` which stands for \"Not a Number\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a1561ca-3b8a-40b1-9168-c28085fb21fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2., -2., -2., ..., -2., -2., -2.],\n",
       "       [-2., -2., -2., ..., -2., -2., -2.],\n",
       "       [-2., -2., -2., ..., -2., -2., -2.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "burndate_data[burndate_data == _FillValue] = np.nan\n",
    "burndate_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6269a4d3-c9e8-4a1e-93b1-37195c3dbf63",
   "metadata": {},
   "source": [
    "Then, let's replace all data which has the value of `-2`, which is the water value, with `nan` which stands for \"Not a Number\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c6b41d46-c68a-438b-b911-16f0b8ae5cea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "burndate_data[burndate_data == _WaterValue] = np.nan\n",
    "burndate_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8684b732-d3f7-49f9-9f11-a1cff79b04bd",
   "metadata": {},
   "source": [
    "Then, let's replace all data which has the value of `0`, which is the unburned value, with `nan` which stands for \"Not a Number\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "055314c5-272e-4104-ac13-bfc5f84af44f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "burndate_data[burndate_data == 0] = np.nan\n",
    "burndate_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa6c53c-1fb1-4ef9-becb-690cff1163e1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Construct a grid and georeference the burned area data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20306dbe-7690-476d-b10b-4e59dddc7880",
   "metadata": {},
   "source": [
    "### Construct the grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68fd203-a7ef-43c3-89e6-98798b8f1dfd",
   "metadata": {},
   "source": [
    "Next, you can construct a grid to plot the burned area data. First, use the `.GetGeoTransform()` function from the gdal library to obtain six coefficients which map the pixel coordinates of the data. This is also referred to as the [affine transform](https://gdal.org/user/raster_data_model.html#affine-geotransform).\n",
    "\n",
    "You can store the following coefficients as variables: \n",
    "\n",
    "- `xinc`: the pixel width\n",
    "- `yinc`: the pixel height\n",
    "- `x0` and `y0`: the x and y-coordinates of the top left corner of the top left pixel of the raster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a1975ab-a09c-470e-a381-993d9ef8b55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x0, xinc, _, y0, _, yinc = burndate_handle.GetGeoTransform()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e7eff3-3489-44f0-b2a1-2dd843a03913",
   "metadata": {},
   "source": [
    "Next, store the size of the raster along the x and y-axes as two separate variables, `nx` and `ny`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f31e34ee-3517-402d-85b6-e8a031e4b9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "nx, ny = (burndate_handle.RasterXSize, burndate_handle.RasterYSize)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ba2457-8cfc-4c9b-9328-b16cf4ea982a",
   "metadata": {},
   "source": [
    "The function `.linspace()` from the numpy library returns evenly spaced numbers over a specified interval. You can use the variables you have already defined to generate the coordinate vectors that will be used to define the grid. Finally, you can use the function `.meshgrid()` from the numpy library to create coordinate matrices from x and y coordinate vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "296fd7f2-fe9a-4285-b0db-daa194200338",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(x0, x0 + xinc*nx, nx)\n",
    "y = np.linspace(y0, y0 + yinc*ny, ny)\n",
    "xv, yv = np.meshgrid(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c17992f-d895-46f2-89a3-61d2bdd7e1d7",
   "metadata": {},
   "source": [
    "### Georeference the data by transforming the grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe97f87-e20a-4290-a513-8922f88cc1a9",
   "metadata": {},
   "source": [
    "Next, you can define the two projection systems, namely the sinusoidal projection and WGS84, using the Python library called [pyproj](https://pyproj4.github.io/pyproj/stable/). You are then able to transform the grid you created earlier into longitude and latitude values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9462d7a3-8b3b-43f4-9598-1ff9746aec22",
   "metadata": {},
   "outputs": [],
   "source": [
    "sinu = pyproj.Proj(\"+proj=sinu +R=6371007.181 +nadgrids=@null +wktext\")\n",
    "wgs84 = pyproj.Proj(\"+init=EPSG:4326\") \n",
    "lon, lat= pyproj.transform(sinu, wgs84, xv, yv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef919e4-a308-45fb-873a-05cc6266db5c",
   "metadata": {},
   "source": [
    "If you print the variables `lon` and `lat`, which contain the longitude and latitude values respectively, you will see the following arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6361d541-59bf-4c14-a055-8b7b209b5b45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.05407289, 13.05951436, 13.06495582, ..., 26.09726285,\n",
       "        26.10270432, 26.10814578],\n",
       "       [13.05327607, 13.0587172 , 13.06415833, ..., 26.09566987,\n",
       "        26.10111101, 26.10655214],\n",
       "       [13.05247941, 13.05792021, 13.06336101, ..., 26.09407723,\n",
       "        26.09951803, 26.10495883],\n",
       "       ...,\n",
       "       [11.54797562, 11.55278928, 11.55760294, ..., 23.08632391,\n",
       "        23.09113757, 23.09595124],\n",
       "       [11.54749045, 11.55230391, 11.55711737, ..., 23.08535398,\n",
       "        23.09016744, 23.0949809 ],\n",
       "       [11.54700538, 11.55181864, 11.5566319 , ..., 23.08438425,\n",
       "        23.08919751, 23.09401076]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c89a4366-e7a7-4d22-9ebf-60b3e31cfe6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[40.        , 40.        , 40.        , ..., 40.        ,\n",
       "        40.        , 40.        ],\n",
       "       [39.99583159, 39.99583159, 39.99583159, ..., 39.99583159,\n",
       "        39.99583159, 39.99583159],\n",
       "       [39.99166319, 39.99166319, 39.99166319, ..., 39.99166319,\n",
       "        39.99166319, 39.99166319],\n",
       "       ...,\n",
       "       [30.0083368 , 30.0083368 , 30.0083368 , ..., 30.0083368 ,\n",
       "        30.0083368 , 30.0083368 ],\n",
       "       [30.0041684 , 30.0041684 , 30.0041684 , ..., 30.0041684 ,\n",
       "        30.0041684 , 30.0041684 ],\n",
       "       [30.        , 30.        , 30.        , ..., 30.        ,\n",
       "        30.        , 30.        ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2947a4-209f-4133-85cc-1079879e81c6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visualize MODIS burned area data with Cartopy features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca8dc93-5af8-4405-a240-0193b49e3b90",
   "metadata": {},
   "source": [
    "For visualization, you can use the function [](functions:visualize_pcolormesh) to visualize the data. The following keyword arguments have to be defined:\n",
    "* `data_array`\n",
    "* `longitude`\n",
    "* `latitude`\n",
    "* `projection`\n",
    "* `color palette`\n",
    "* `unit`\n",
    "* `long_name`\n",
    "* `vmin`, \n",
    "* `vmax`\n",
    "* `extent (lonmin, lonmax, latmin, latmax)`\n",
    "* `set_global`\n",
    "\n",
    "We set the `vmin` and `vmax` values to 214 and 244 respectively, which are the ordinal days for the first and last day of the month of August 2020. We set the extent of the plot to highlight burned areas in southern Italy and in Greece."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "239bbaae-abab-4eba-b39e-47d1b0eeb0a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1440x720 with 2 Axes>,\n",
       " <GeoAxesSubplot:title={'center':'MODIS/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid \\nordinal day of burn in August 2021'}>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_pcolormesh(data_array=burndate_data,\n",
    "                     longitude=lon,\n",
    "                     latitude=lat,\n",
    "                     projection=ccrs.PlateCarree(),\n",
    "                     color_scale='rainbow_r',\n",
    "                     unit='Ordinal Day',\n",
    "                     long_name='MODIS/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid \\n' + long_name + ' in August 2021',\n",
    "                     vmin=213, \n",
    "                     vmax=243,\n",
    "                     lonmin=lon.min(),\n",
    "                     lonmax=lon.max(),\n",
    "                     latmin=35,\n",
    "                     latmax=lat.max(),\n",
    "                     set_global=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96770ee8-4f89-4817-9fd7-16bfe42cb3b9",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccf5ee76-f7da-4690-a27f-a305a1be046f",
   "metadata": {},
   "source": [
    "### References\n",
    "* Giglio, L., Justice, C., Boschetti, L., Roy, D. (2021). MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD64A1.061\n",
    "\n",
    "* Some code in this notebook was adapted from the following source:\n",
    "    * origin: https://hdfeos.org/zoo/LPDAAC/MCD43A3_A2013305_h12v11_005_2013322102420.py\n",
    "    * copyright: 2014, John Evans\n",
    "    * license: Public Domain\n",
    "    * retrieved: 2022-06-28 by Sabrina Szeto"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f76d7c-6d21-4e51-8dee-b198460067d8",
   "metadata": {},
   "source": [
    "```{admonition} Return to the case study\n",
    "Assessing post-fire impacts with next-generation satellites: Mediterranean Fires Case Study <br>\n",
    "[](med_part3_fig1)\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}