GEE_mapprojections_MODIS.ipynb

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    "# EDS 220 Fall 2022\n",
    "# Mapping and map projections with Google Earth Engine\n",
    "\n",
    "Google Earth Engine is an extremely powerful tool for visualizing environmental data, making it an extremely popular choice in many contexts. However, the combination of Google Earth Engine with Cartopy is even more powerful, combining the on-the-fly ability of GEE to access and analyze data with Cartopy's strengths in map reprojection and generation of production-quality graphics.\n",
    "\n",
    "To facilitate the use of Cartopy, we'll use the `cartoee` package. Cartoee functions as a 'go-between' for Earth Engine and Cartopy, by requesting images from Earth Engine and passing them through to Cartopy. In that way, the data can be reprojected as desired using Cartopy without the user having to download all the data from Earth Engine. Because of the utility of this approach, cartoee has been incorporated into `geemap` and can be called from that package such that it can be referred to on its own.\n",
    "\n",
    "First, we import the relevant packages and initialize the Earth Engine environment:"
   ]
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    "# Import packages\n",
    "import ee\n",
    "import geemap\n",
    "import pandas as pd\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "from geemap import cartoee\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
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   "id": "b229f63b-2dbf-4281-a80a-5d3226cb9ea3",
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   "source": [
    "# Authenticate and initialize GEE\n",
    "#ee.Authenticate()\n",
    "ee.Initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9879f1-1569-4221-a471-fa10563d9b98",
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   "source": [
    "Next, let's load some data. To get some variety from the Landsat examples we used in previous class sessions, let's pick a marine example!\n",
    "\n",
    "The following example uses the _ocean color_ data collected by NASA's MODIS satellite:\n",
    "https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI#description\n",
    "\n",
    "This product is derived from reflectance at the ocean surface in wavelength bands strongly affected by chlorophyll absorption; in this way, we can get an indication of how much photosynthesis by _phytoplankton_ (algae) is happening in the surface ocean.\n",
    "\n",
    "Note that since ocean color is a derived product calculated from reflectance, it is considered a _Level 3_ data product (see description link above).\n",
    "\n",
    "Let's load the set of MODIS data into a Google Earth Engine `ImageCollection`:"
   ]
  },
  {
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   "execution_count": 39,
   "id": "27bdced5-18dd-48db-b4b8-89aa447f9185",
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   "source": [
    "# Load the MODIS ocean color data\n",
    "gdat = ee.ImageCollection(\"NASA/OCEANDATA/MODIS-Aqua/L3SMI\")"
   ]
  },
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    "The metadata for this image collection can be accessed through the Google Earth Engine web interface:\n",
    "https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI#bands\n",
    "\n",
    "or we can get a sense for things by looking at a single image and using the `getInfo` function. To do the latter, we first need to select a single image (-not- an ImageCollection) to let us display that information. "
   ]
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   ],
   "source": [
    "# Extract the first image in the MODIS image collection\n",
    "firstimg=gdat.first()\n",
    "\n",
    "# Display metadata for first MODIS image\n",
    "firstimg.getInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69825f74-1bd0-4303-8f66-fb298561a074",
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   "source": [
    "As we did in the `Landsat_colormaps_GEE` notebook, it's desirable to reduce the size of the dataset in order to create something that can actually be plotted as a single map.\n",
    "\n",
    "To accomplish this, let's do the following:\n",
    "- get rid of cloudy days by filtering on the `CLOUD_COVER` attribute of the image collection;\n",
    "- select a date range and take the time average over that range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0ae5b27d-5180-4a48-8553-c8dc3de7896a",
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   "source": [
    "# Use filter to extract all \"non-cloudy\" images: ones with less than 20% cloud cover\n",
    "dat_nocld=gdat.filter('CLOUD_COVER < 0.2')"
   ]
  },
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   "source": [
    "Now we need to figure out a relevant region to display. Here I've chosen one that gives us a sense of variability in chlorophyll through the __California Current__, which is our local current system off the Santa Barbara coast and a good example of a _western boundary current_. The California Current is an extremely _productive_ marine ecosystem because of the amount of _upwelling_ of cold subsurface ocean waters, making it rich in nutrients and therefore home to lots of different marine ecosystems.\n",
    "\n",
    "To filter the image collection, we set up a central point using `ee.Geometry.Point`, and then define a `buffer` region around that point. Here I've chosen 1000km but you can change this if you like!"
   ]
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   "id": "684bc995-5999-460a-bcb0-44aca5b9801f",
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    "# Define the location of interest as a point near Los Angeles, California\n",
    "u_lon = -118.2\n",
    "u_lat = 34.05\n",
    "u_poi = ee.Geometry.Point(u_lon, u_lat)\n",
    "\n",
    "# Define the region: everything within 1000km of LA\n",
    "roi = u_poi.buffer(1e6)"
   ]
  },
  {
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   "source": [
    "Once the region is defined, we can then use it to extract the portions of the image collection intersecting with that region, similar to what we did with Landsat for Santa Barbara. This can be accomplished using `filterBounds`, and providing the region object we created above as an argument:"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Filter to region of interest\n",
    "dat_filt = dat_nocld.filterBounds(roi)\n",
    "\n",
    "# Command to extract all data over appropriate time, perform temporal averaging\n",
    "dat_2018=gdat.filter(ee.Filter.date('2018-6-1', '2018-12-31')).mean();"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Now we have the data we need to plot! But we still have to supply some _visualization parameters_ in order to make everything display properly. Below I create an example of the data structure that Google Earth Engine needs to interpret these visualizations. \n",
    "\n",
    "Important parameters:\n",
    "- the band name (\"chlor_a\") - note that there is only ONE band here, instead of the three we were using to create RGB images with Landsat information;\n",
    "- minimum and maximum values to consider when assigning colors to the color bar later"
   ]
  },
  {
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   "source": [
    "# Visualization parameters\n",
    "visParams = {'bands': ['chlor_a'],\n",
    "             'min': 0,\n",
    "             'max': 5,\n",
    "            }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d7cf6f-e595-451f-9837-1a9e7e20b374",
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   "source": [
    "When the plot is generated, we can also use all of the functionality of Cartopy to set the plot parameters. Below, I've drawn in the coast outlines using `ax.coastlines()`, and used the `ax.gridlines` module to draw semitransparent gray dashed lat/lon grid lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1d7ad2c9-9088-4d8b-b97e-5e1fe282ba1b",
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    {
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\n",
      "text/plain": [
       "<Figure size 720x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 15))\n",
    "\n",
    "# create a new Plate-Carree projection \n",
    "projection = ccrs.PlateCarree()\n",
    "\n",
    "# plot the result with cartoee \n",
    "ax = cartoee.get_map(dat_2018, vis_params=visParams, proj=projection, cmap='coolwarm')\n",
    "\n",
    "# Define the lon/lat extent of the plot\n",
    "ax.set_extent([-130,-115,24,50])   \n",
    "\n",
    "# Draw in coastlines\n",
    "ax.coastlines()\n",
    "\n",
    "# Draw grid lines\n",
    "gl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n",
    "                  linewidth=2, alpha=0.5, linestyle='--')\n",
    "\n",
    "# Set parameters for grid lines\n",
    "gl.xlabels_top = False\n",
    "gl.ylabels_right = False\n",
    "gl.xlabel_style = {'size': 15}\n",
    "gl.ylabel_style = {'size': 15}\n",
    "\n",
    "# Set plot title\n",
    "ax.set_title(label='MODIS: Plate-Carree', fontsize=20)\n",
    "\n",
    "# Add color bar\n",
    "cartoee.add_colorbar(ax,visParams,loc=\"right\",cmap='coolwarm')\n",
    "\n",
    "# Display plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8dd0e72-cd8e-408b-8ff1-da99e6c56527",
   "metadata": {},
   "source": [
    "Now let's compare this with the visualization we'd have gotten if we used the interactive \"base map\" provided by Google Earth Engine. \n",
    "\n",
    "To do this, we need to specify a new set of visualization parameters, since we need another parameter called the color _palette_. The two codes in the palette correspond to the colors of the minimum and maximum values, and GEE will then assign colors ranging between the two specified values to all the data in between."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "77676105-cb9c-4a49-9cb6-668dcfbaffd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualization parameters\n",
    "visParams = {'bands': ['chlor_a'],\n",
    "             'min': 0,\n",
    "             'max': 5,\n",
    "             'palette': ['0000FF', 'FF0000']\n",
    "            }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60911600-2767-4d3e-973f-ce4190641e0c",
   "metadata": {},
   "source": [
    "Now we can draw the basemap and add the MODIS layer to it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0a8c9cd4-b722-46a4-9a7e-3b2ce193ad8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18762417c64d42bc9827a56956d86e51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[34.42, -124], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define parameters for GEE base map\n",
    "Map = geemap.Map(center=[34.42, -124], zoom=6)\n",
    "\n",
    "# Draw map\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9b15e3dd-afdf-4087-a4e5-9a77ba356ece",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add layer to the basemap\n",
    "Map.addLayer(dat_2018, visParams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d9224a6-6ca4-4d47-a4fe-27622eeec355",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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