Intro to Google Earth Engine

Intro to Google Earth Engine#

To run the code:

Make sure you have this notebook open in Google Colab. If you are starting from the digital textbook, click

Each block of code is called a cell. To run a cell, hover over it and click the arrow in the top left of the cell, or click inside of the cell and press Shift + Enter.

Note: When you run a block of code for the first time, Google Colab will say Warning: This notebook was not authored by Google. Please click Run Anyway.

You need a Google Earth Engine project in order to run this code. If you haven’t already, please go to the previous lesson Get Google Earth Engine to set up your project.

A great feature of Google Colab is that you are able to write Python code and see the output directly on your browser. Let’s go through the basics below:

import ee

# Authenticate Google Earth Engine
ee.Authenticate()

# Change "emerge-lessons" to your project ID if it is different
ee.Initialize(project="emerge-lessons")

In the code above, change “emerge-lessons” to your own project ID. For example, if your ID is “emerge-34956,” you can change it to the following:

ee.Initialize(project="emerge-34956")

Basic Terminology - Using Collections in GEE#

Features are geometric objects with a list of properties

ee.Feature

Images are similar to features, but can have several bands

ee.Image

Collections are groups of features or images

ee.FeatureCollection or ee.ImageCollection

When you go into the details of an individual dataset, you can find the snippet of how to import that specific dataset

For this example, we’ll be using the MODIS Terra Land Surface Temperature (LST) dataset.

# Import the MODIS land surface temperature collection
lst = ee.ImageCollection('MODIS/061/MOD11A1')

Datasets include several sets of information across several bands, with some datasets containing daily imagery in a 1km resolution (like the MODIS LST dataset we’re using) to one image a year in a 30m resolution for others.

The LST Collection uses these bands

LST_Day_1km: Daytime Land Surface Temperature
Day_view_time: Local time of day observation
LST_Night_1km: Nighttime Land Surface Temperature

With so many images within the collection, we need to filter those images using dates

Use either of these for filtering

filterDate()
select()

# Initial date of interest (inclusive)
i_date = '2024-01-01'

# Final date of interest (exclusive)
f_date = '2024-01-31'

# Selection of appropriate bands and dates for LST
lst = lst.select('LST_Day_1km', 'QC_Day').filterDate(i_date, f_date)

Next, we’ll define two points of interest (POIs) in relation to what we want to look at. Since we’re looking at Land Surface Emissivity (land temp), we’ll make a comparison of the temperature in an urban area versus a rural area.

# Define the urban location of interest as a point near Miami, Florida
urban_lon = -80.196432
urban_lat = 25.779766
urban_poi = ee.Geometry.Point(urban_lon, urban_lat)

# Define the rural location of interest as a point away from the city in Homestead, Florida
rural_lon = -80.4998113
rural_lat = 25.3933527
rural_poi = ee.Geometry.Point(rural_lon, rural_lat)

The MODIS emissivity dataset that we’re using should be corrected by a scale of 0.02 to get units of Kelvin. You can find this in the Google Earth Engine catalogue at this link.

Remember to convert your values for a final answer!

We need the average land temperature of the two POIs we’re looking at, so we’ll use mean()

# Scale in meters
scale = 1000

# Function to convert LST to Celsius and get mean LST
def get_mean_lst(poi, scale):
    mean_lst = lst.mean().sample(poi, scale).first().get('LST_Day_1km').getInfo()
    return round(mean_lst * 0.02 - 273.15, 2)  # Convert to Celsius

# Get mean LST for both urban and rural points
urban_lst = get_mean_lst(urban_poi, scale)
rural_lst = get_mean_lst(rural_poi, scale)

print ("The mean land surface temperature for the urban point is", urban_lst, "°C")
print ("The rural land surface temperature for the urban point is", rural_lst, "°C")

The mean land surface temperature for the urban point is 23.15 °C
The rural land surface temperature for the urban point is 20.9 °C

Interactive Maps#

import folium        # Create interactive maps in Python
import geemap        # Another option for mapping Google Earth Engine data

Create an empty map zoomed to Florida. You can click and drag to move around the map, as well as zoom in and out.

map = folium.Map(location=[28.263363, -83.497652], tiles="Cartodb dark_matter", zoom_start=7)
display(map)

Make this Notebook Trusted to load map: File -> Trust Notebook

Next, we define a function (from this tutorial) to add Google Earth Engine data to a map in a way that allows it to be interactively displayed.

def add_ee_layer(self, ee_image_object, vis_params, name):
    """Adds a method for displaying Earth Engine image tiles to folium map."""
    map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)
    folium.raster_layers.TileLayer(
        tiles=map_id_dict['tile_fetcher'].url_format,
        attr='Map Data &copy; <a href="https://earthengine.google.com/">Google Earth Engine</a>',
        name=name,
        overlay=True,
        control=True
    ).add_to(self)

folium.Map.add_ee_layer = add_ee_layer

# Visualization parameters used to set the colors for the data
lst_vis = {
    'min': 13000.0,
    'max': 16500.0,
    'palette': [
    '040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
    '0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
    '3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 'd6e21f',
    'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
    'ff0000', 'de0101', 'c21301', 'a71001', '911003'
  ],
}

# Add the data to the map
map.add_ee_layer(lst.mean().select('LST_Day_1km'), lst_vis, "LST")

display(map)

Make this Notebook Trusted to load map: File -> Trust Notebook

References

Intro to Google Earth Engine

Contents

Intro to Google Earth Engine#

Basic Terminology - Using Collections in GEE#

Interactive Maps#