{
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   "metadata": {},
   "source": [
    ">\n",
    ">\n",
    ">\n",
    ">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multispectral Functions Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: This example notebook covers the basics for how to calculate multispectral indices and does not cover image visualization. \n",
    ">\n",
    "> If you would like to learn how to visualize multispectral index products, [please follow the `Complete_ReadMe_Example.ipynb` or `Palettes_and_Visualization.ipynb` example notebooks on GitHub](https://github.com/radwinskis/RadGEEToolbox/tree/main/Example%20Notebooks)\n",
    ">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee\n",
    "from RadGEEToolbox import LandsatCollection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Earth Engine initialized successfully.\n"
     ]
    }
   ],
   "source": [
    "# Store name of Google Cloud Project assosiated with Earth Engine - replace with your project ID/name\n",
    "PROJECT_ID = 'your-cloud-project-id'\n",
    "# Attempt to initialize Earth Engine\n",
    "try:\n",
    "    ee.Initialize(project=PROJECT_ID)\n",
    "    print(\"Earth Engine initialized successfully.\")\n",
    "except Exception as e:\n",
    "    print(\"Initialization failed, attempting authentication...\")\n",
    "    try:\n",
    "        ee.Authenticate()\n",
    "        ee.Initialize(project=PROJECT_ID)\n",
    "        print(\"Authentication and initialization successful.\")\n",
    "    except Exception as auth_error:\n",
    "        print(\"Authentication failed. Error details:\", auth_error)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`RadGEEToolbox` has many available spectral index calculations for both Landsat and Sentinel-2 data**\n",
    "\n",
    "Such indices follow, but are not limited to: NDWI (water), NDVI (vegetation), NDTI (water turbidity), relative chlorophyll-a concentrations, \n",
    "and other indices for mapping evaporites (established by the author of this package - see Radwin & Bowen, 2021). Additionally, Land Surface \n",
    "Temperature (LST) can be easily calculcated for Landsat imagery.\n",
    "\n",
    "**In addition to multispectral indices, there are easy ways to temporally reduce image collections to mean, median, min, or max images**\n",
    "\n",
    "**In the below cell are examples of how to quickly process an entire image collection to the available spectral indices, using the available attribute functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we need an image collection\n",
    "col = LandsatCollection(start_date='2023-06-01', end_date='2023-06-30', tile_row=32, tile_path=38)\n",
    "\n",
    "#Then it is as easy as calling the desired attribute for multispectral indices\n",
    "NDWI = col.ndwi\n",
    "NDVI = col.ndvi\n",
    "NDTI = col.turbidity\n",
    "halite = col.halite\n",
    "gypsum = col.gypsum\n",
    "chlorophyll = col.chlorophyll\n",
    "surface_temperature = col.LST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______\n",
    "\n",
    "**It is also very easy to perform temporal reductions on an entire collection, as shown below**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temporal reductions of image collections\n",
    "mean_img = col.mean\n",
    "min_img = col.min\n",
    "median_img = col.median\n",
    "max_img = col.max\n",
    "\n",
    "#Temporal reductions can be applied to multispectral indices as well\n",
    "mean_chlorophyll = chlorophyll.mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The above approach calculates spectral indices but does not perform binary threshold masking / classification**\n",
    "\n",
    "_______\n",
    "\n",
    "\n",
    "**If you wish to perform binary thresholding, there are alternative functions for calculating spectral indices and masking based on a floating point threshold value, where values below the threshold are masked as nodata pixels -- as shown below**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using methods to calculate multispectral indices\n",
    "\n",
    "NDWI = col.ndwi_collection(threshold=0)\n",
    "NDVI = col.ndvi_collection(threshold=0.3)\n",
    "turbidity = col.turbidity_collection(threshold=0)\n",
    "\n",
    "#If you will be using Landsat 5 and Landsat 8 or 9 imagery together in a collection, you can specify different thresholds \n",
    "# for TM vs OLI sensors where ng_threshold means \"next generation\" threshold for newer sensors\n",
    "\n",
    "NDWI = col.ndwi_collection(threshold=0, ng_threshold=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**By directly calling static method functions, `RadGEEToolbox` provides the freedom to use almost any of the core functions, including spectral index calculations, on GEE objects**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example using static method function to calculate NDWI for all images in an ee.ImageCollection object\n",
    "#Note the need for lambda to map functions with arguments other than the input image\n",
    "\n",
    "NDWI = col.collection.map(lambda image: LandsatCollection.landsat_ndwi_fn(image, threshold=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Please refer to the [RadGEEToolbox documentation](https://radgeetoolbox.readthedocs.io/en/latest/) for more information and a comprehensive list of available functionality*"
   ]
  }
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