A python package that extends Google Earth Engine

eemont

Google Earth Engine is a cloud-based service for geospatial processing of vector and raster data. The Earth Engine platform has a JavaScript and a Python API with different methods to process geospatial objects. Google Earth Engine also provides a HUGE PETABYTE-SCALE CATALOG of raster and vector data that users can process online (e.g. Landsat Missions Image Collections, Sentinel Missions Image Collections, MODIS Products Image Collections, World Database of Protected Areas, etc.). The eemont package extends the Google Earth Engine Python API with pre-processing and processing tools for the most used satellite platforms by adding utility methods for different Earth Engine Objects that are friendly with the Python method chaining.

How does it work?

The eemont python package extends the following Earth Engine classes:

• ee.Feature
• ee.FeatureCollection
• ee.Geometry
• ee.Image
• ee.ImageCollection

New utility methods and constructors are added to above-mentioned classes in order to create a more fluid code by being friendly with the Python method chaining. These methods are mandatory for some pre-processing and processing tasks (e.g. clouds masking, shadows masking, image scaling, spectral indices computation, etc.), and they are presented as simple functions that give researchers, students and analysts the chance to analyze data with far fewer lines of code.

Look at this simple example where a Sentinel-2 Surface Reflectance Image Collection is pre-processed and processed in just one step:

import ee, eemont

ee.Authenticate()
ee.Initialize()

point = ee.Geometry.PointFromQuery('Cali, Colombia',user_agent = 'eemont-example') # Extended constructor

S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
    .filterBounds(point)
    .closest('2020-10-15') # Extended (pre-processing)
    .maskClouds(prob = 70) # Extended (pre-processing)
    .scale() # Extended (pre-processing)
    .index(['NDVI','NDWI','BAIS2'])) # Extended (processing)

And just like that, the collection was pre-processed, processed and ready to be analyzed!

Installation

Install the latest eemont version from PyPI by running:

pip install eemont

Features

Let's see some of the main features of eemont and how simple they are compared to the GEE Python API original methods:

Overloaded Operators

The following operators are overloaded: +, -, *, /, //, %, **, <<, >>, &, |, <, <=, ==, !=, >, >=, -, ~. (and you can avoid the ee.Image.expression() method!)

Clouds and Shadows Masking

Masking clouds and shadows can be done using eemont with just one method: maskClouds()!

Image Scaling and Offsetting

Scaling and offsetting can also be done using eemont with just one method: scale()!

Spectral Indices

Do you need to compute several spectral indices? Use the index() method! A lot of built-in vegetation, burn, water, snow, drought and kernel indices can be computed:

The list of available indices can be retrieved by running:

eemont.listIndices()

Information about the indices can also be checked:

indices = eemont.indices()
indices.BAIS2.formula
indices.BAIS2.reference

Closest Image to a Specific Date

Struggling to get the closest image to a specific date? Here is the solution: the closest() method!

Time Series By Regions

The JavaScript API has a method for time series extraction (included in the ui.Chart module), but this method is missing in the Python API... so, here it is!

PD: Actually, there are two methods that you can use: getTimeSeriesByRegion() and getTimeSeriesByRegions()!

f1 = ee.Feature(ee.Geometry.Point([3.984770,48.767221]).buffer(50),{'ID':'A'})
f2 = ee.Feature(ee.Geometry.Point([4.101367,48.748076]).buffer(50),{'ID':'B'})
fc = ee.FeatureCollection([f1,f2])

S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
   .filterBounds(fc)
   .filterDate('2020-01-01','2021-01-01')
   .maskClouds()
   .scale()
   .index(['EVI','NDVI']))

# By Region
ts = S2.getTimeSeriesByRegion(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
                              geometry = fc,
                              bands = ['EVI','NDVI'],
                              scale = 10)

# By Regions
ts = S2.getTimeSeriesByRegions(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
                               collection = fc,
                               bands = ['EVI','NDVI'],
                               scale = 10)

Constructors by Queries

Don't you have the coordinates of a place? You can construct them by using queries!

usr = 'my-eemont-query-example'

seattle_bbox = ee.Geometry.BBoxFromQuery('Seattle',user_agent = usr)
cali_coords = ee.Feature.PointFromQuery('Cali, Colombia',user_agent = usr)
amazonas_river = ee.FeatureCollection.MultiPointFromQuery('Río Amazonas',user_agent = usr)

Supported Platforms

The Supported Platforms for each method can be found in the eemont documentation.

• Masking clouds and shadows supports Sentinel Missions (Sentinel-2 SR and Sentinel-3), Landsat Missions (SR products) and some MODIS Products. Check all details in User Guide > Masking Clouds and Shadows > Supported Platforms.
• Image scaling supports Sentinel Missions (Sentinel-2 and Sentinel-3), Landsat Missions and most MODIS Products. Check all details in User Guide > Image Scaling > Supported Platforms.
• Spectral indices computation supports Sentinel-2 and Landsat Missions. Check all details in User Guide > Spectral Indices > Supported Platforms.
• Getting the closest image to a specific date and time series supports all image collections with the system:time_start property.

GitHub

https://github.com/davemlz/eemont