examples/notebooks/03_eepredefined_landsat_era5.ipynb

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        "<a href=\"https://githubtocolab.com/kaust-halo/geeet/blob/master/examples/notebooks/03_eepredefined_landsat_era5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>"
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      "source": [
        "# EE predefined: Landsat + ERA5 image collection\n",
        "\n",
        "---\n",
        "\n",
        "The `geeet.eepredefined` library contains a collection of modules that make it easy to ingest GEE data into the geeet models. \n",
        "\n",
        "This notebook demonstrates how to:\n",
        "\n",
        "1. Prepare a [merged Landsat collection](#landsat-collection-02) (Landsat 7, 8, and 9) \n",
        "2. Prepare a [joint Landsat + ERA5](#era5-climate-reanalysis-data) (climate reanalysis) image collection\n",
        "3. Map the Landsat + ERA5 collection [into an ET model](#landsat--era5--tseb) "
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# Use the following line to install geeet if needed:\n",
        "#!pip install git+https://github.com/kaust-halo/geeet\n",
        "# Use the following line to install geemap if needed:\n",
        "#!pip install geemap\n",
        "import ee\n",
        "#ee.Authenticate() # Uncomment if using Google Colab or first time using EE on this device. \n",
        "ee.Initialize()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Landsat collection 02\n",
        "\n",
        "Prepare a merged Landsat (Collection 02) surface reflectance image collection using `landsat.collection`.\n",
        "\n",
        "All surface reflectance (`SR_*`) and temperature (`ST_*`) bands are scaled and offset. The collection is also filtered by:\n",
        "\n",
        "- date: using `date_start` and `date_end` \n",
        "- bounds: using `region` (could be a dict, string indicating an asset, local shapefile/geojson, or a [geopandas.GeoDataFrame](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import geeet\n",
        "from geeet.eepredefined import landsat\n",
        "\n",
        "region = dict(type=\"Point\", coordinates=[38.25, 30.25])\n",
        "\n",
        "coll =  landsat.collection(\n",
        "    date_start = \"2023-04-01\",\n",
        "    date_end = \"2023-05-01\",\n",
        "    region = region, \n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Other filters in `landsat.collection` include: \n",
        "\n",
        "- Maximum cloud cover: Only include images with `CLOUD_COVER` less than or equal to `max_cc`. Use `max_cc` to set this value.  \n",
        "- Explicitly exclude path/rows: Use `exclude_pr` to explicitly exclude a list of path/rows (e.g. `exclude_pr=[[171,39]]` will exclude images in the path/row 171/39). \n",
        "- Explicitly limit images to a list of path/rows: Use `include_pr` to filter the collection to *only* include images within a list of path/rows.\n",
        "- Include *only* images from `sat_list`, which may be a **non-empty** subset of `[\"LANDSAT_7\", \"LANDSAT_8\", \"LANDSAT_9\"]`. Defaults to using all three collections. \n",
        "\n",
        "> Since the collections are merged, the `system:index` loses its original value. However, you can still find in in a new `LANDSAT_INDEX` property:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "coll.aggregate_array(\"LANDSAT_INDEX\").getInfo()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "All other properties and original band names remain intact:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "coll.first().bandNames().getInfo()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note however, that by default four new bands were added. These are:\n",
        "\n",
        "- `NDVI` (normalized difference vegetation index): calculated using the corresponding NIR and red bands. Use `ndvi=False` to exclude this band.\n",
        "- `albedo`: calculated using one of two parameterizations ([Liang, 2001](https://www.sciencedirect.com/science/article/pii/S0034425700002054) by default). Use `albedo=None` to exclude this band. \n",
        "- `radiometric_temperature`: This is a copy of either the `ST_B6` (Landsat 7) or `ST_10` band (Landsat 8 and 9). Use `rad_temp=False` to exclude this band.\n",
        "- `cloud_cover`: A byte band indicating 1 for pixels identified as cloud/cloud shadows, and 0 otherwise. Use `cfmask=False` to exclude this band. \n",
        "\n",
        "> The images are *not* masked using `cloud_cover`. However, you can use the helper function `landsat.cfmask` to update the mask of the desired bands to mask. \n",
        "\n",
        "Other optional bands include:\n",
        "\n",
        "- lai: Leaf area index; calculated using one of two methods. By default `lai=None`. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a69CuP5Q6OI7"
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      "source": [
        "## ERA5 climate reanalysis data\n",
        "\n",
        "*geeet.eepredefined* also includes some tools to work with [ERA5 climate reanalysis](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY#bands) data, specifically the `LAND_HOURLY` product. \n",
        "\n",
        "The `landsat.collection` tool also includes an option to include this data, specifically adding the following bands:\n",
        "\n",
        "- `surface_pressure`: surface pressure, in Pa\n",
        "- `air_temperature`: air temperature, in K\n",
        "- `wind_speed`: wind speed, in m/s (also included are the `u` and `v` components as separate bands).\n",
        "- `solar_radiation`: downward shortwave radiation, in W/m² (also included is the hourly accumulated radiation (`surface_solar_radiation_downwards_hourly`) band, in J/m²).\n",
        "- `thermal_radiation`: downward longwave radiation, in W/m² (also included is the hourly accumulated radiation (`surface_thermal_radiation_downwards_hourly`) band, in J/m²)."
      ]
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        "coll_with_era5 = landsat.collection(\n",
        "    date_start = \"2023-04-01\",\n",
        "    date_end = \"2023-05-01\",\n",
        "    region = region, \n",
        "    era5 = True,\n",
        "    timeZone=\"Asia/Riyadh\" # the time property will be set in local time (UTC +3 for this example)\n",
        ")\n",
        "coll_with_era5.first().bandNames().getInfo()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Landsat + ERA5 + TSEB"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This collection is ready to be mapped to an ET model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "landsat_era5_tseb_collection = coll_with_era5.map(geeet.tseb.tseb_series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "landsat_era5_tseb_collection.first().bandNames().getInfo()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can see now that the ET model outputs have been added as additional bands. \n",
        "\n",
        "You can also use the `landsat.mapped_collection` helper function to map a sequence of functions to a `landsat.collection`, e.g. the example above would become:\n",
        "\n",
        "```python\n",
        "landsat_era5_tseb_collection = landsat.mapped_collection(\n",
        "    [geeet.tseb.tseb_series],    # List of mappable functions, here only tseb. \n",
        "    date_start = \"2023-04-01\",\n",
        "    date_end = \"2023-05-01\",\n",
        "    region = region, \n",
        "    era5 = True, \n",
        "    timeZone = \"Asia/Riyadh\"\n",
        ")\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "## Visualization\n",
        "\n",
        "Let's generate a small visualization by clipping one of the images to a small region. \n",
        "\n",
        "> You will need [geemap](geemap.org) for this visualization. Alternatively, open [this script in the code editor](https://code.earthengine.google.com/f71913e4516d36de0da2c0bce4f03d39). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import geemap\n",
        "import geemap.colormaps as cm\n",
        "\n",
        "# Select one image by LANDSAT_INDEX: \n",
        "image = (\n",
        "    landsat_era5_tseb_collection\n",
        "    .filter(ee.Filter.eq(\"LANDSAT_INDEX\", \n",
        "    \"LC09_172039_20230421\"\n",
        "    ))\n",
        "    .first()\n",
        "    .clip(ee.Geometry(region)\n",
        "        .buffer(10000)   # 10km area around the point\n",
        "        .bounds()        \n",
        "    )\n",
        ")\n",
        "\n",
        "Map = geemap.Map()\n",
        "Map.setCenter(*region[\"coordinates\"], zoom=12)\n",
        "ndvi_pal = cm.palettes.ndvi\n",
        "red_pal = cm.palettes.YlOrRd\n",
        "blue_pal = cm.palettes.YlGnBu\n",
        "\n",
        "Map.addLayer(image, {'bands':['SR_B4', 'SR_B3', 'SR_B2'], 'min':0, 'max': 0.3}, 'Image')\n",
        "Map.addLayer(image.select('albedo'), {'min':0, 'max':0.35}, 'Albedo (shortwave; Liang 2001)', False)\n",
        "Map.addLayer(image.select('NDVI'), {'min':0, 'max':1, 'palette':ndvi_pal, 'opacity': 0.8}, 'NDVI')\n",
        "Map.addLayer(image.select('radiometric_temperature'), {'min':20+273, 'max':50+273, 'palette':red_pal}, 'Landsat radiometric temperature (K)')\n",
        "Map.addLayer(image.select('Rn'), {'min':0, 'max':500, 'palette':red_pal}, 'Net radiation (W/m²)', False)\n",
        "Map.addLayer(image.select('LEc'), {'min':0, 'max':300, 'palette':blue_pal}, 'Latent heat flux (W/m²) from the canopy source', True)\n",
        "\n",
        "Map"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here's a preview of how the map should look like:\n",
        "\n",
        "![image](https://github.com/kaust-halo/geeet/assets/14804652/8379c468-1264-47ff-bdfb-89a7f9d8f61b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4ydf3BzJZ40"
      },
      "source": [
        "## Export\n",
        "\n",
        "This cell was used to export the image as an asset. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "35-T8FN0Jnps"
      },
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      "source": [
        "asset_id =  \"\"  # replace with your asset\n",
        "\n",
        "roi = (ee.Geometry(region)\n",
        "        .buffer(10000)   # 10km area around the point\n",
        "        .bounds()        \n",
        ")\n",
        "task = ee.batch.Export.image.toAsset(image=image,\n",
        "                                     description='landsat-era5-sample',\n",
        "                                     assetId=asset_id,\n",
        "                                     region=roi,\n",
        "                                     crs=\"EPSG:32637\",\n",
        "                                     crsTransform=[30,0,296685,0,-30,3470115],\n",
        "                                     )\n",
        "#task.start() # Uncomment to submit the task."
      ]
    }
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