Welcome to eemont!¶

The eemont package extends Google Earth Engine with pre-processing and processing tools for the most used satellite platforms.

How does it work?¶

Earth Engine classes, such as ee.Image and ee.ImageCollection, are extended with eemont. New methods are added to these classes to make the code more fluid.

Look at this simple example where a Sentinel-2 collection is pre-processed and processed in just one step:

import ee, eemont

ee.Authenticate()
ee.Initialize()

point = ee.Geometry.Point([-76.21, 3.45])

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 and processed!

Installation¶

Install the latest eemont version from PyPI by running:

pip install eemont

Features¶

The following features are extended through eemont:

point = ee.Geometry.Point([-76.21, 3.45]) # Example ROI

Overloaded operators (+, -, *, /, //, %, **, <<, >>, &, |, <, <=, ==, !=, >, >=, -, ~):

S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
    .filterBounds(point)
    .sort('CLOUDY_PIXEL_PERCENTAGE')
    .first()
    .maskClouds()
    .scale())

N = S2.select('B8')
R = S2.select('B4')
B = S2.select('B2')

EVI = 2.5 * (N - R) / (N + 6.0 * R - 7.5 * B + 1.0) # Overloaded operators

Clouds and shadows masking:

S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
    .maskClouds(prob = 65, cdi = -0.5, buffer = 300) # Clouds and shadows masking
    .first())

Image scaling:

MOD13Q1 = ee.ImageCollection('MODIS/006/MOD13Q1').scale() # Image scaling

Spectral indices computation (vegetation, burn, water and snow indices):

L8 = (ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
    .filterBounds(point)
    .maskClouds()
    .scale()
    .index(['GNDVI','NDWI','BAI','NDSI'])) # Indices computation

Closest image to a specific date:

S5NO2 = (ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')
    .filterBounds(point)
    .closest('2020-10-15')) # Closest image to a date

Methods¶

The above-mentioned features extends both ee.Image and ee.ImageCollection classes:

ee.Image¶

`index`(self[, index, G, C1, C2, L])	Computes one or more spectral indices (indices are added as bands) for an image.
`maskClouds`(self[, method, prob, maskCirrus, …])	Masks clouds and shadows in an image (valid just for Surface Reflectance products).
`scale`(self)	Scales bands on an image.

ee.ImageCollection¶

`closest`(self, date[, tolerance, unit])	Gets the closest image (or set of images if the collection intersects a region that requires multiple scenes) to the specified date.
`index`(self[, index, G, C1, C2, L])	Computes one or more spectral indices (indices are added as bands) for an image collection.
`maskClouds`(self[, method, prob, maskCirrus, …])	Masks clouds and shadows in an image collection (valid just for Surface Reflectance products).
`scale`(self)	Scales bands on an image collection.

Non-Earth Engine classes such as pd.DataFrame are also extended:

pd.DataFrame¶

toEEFeatureCollection(self[, latitude, …])

Converts a pd.DataFrame object into an ee.FeatureCollection object.

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 supports all image collections with the system:time_start property.

License¶

The project is licensed under the MIT license.