eemont 0.1.8

How does it work?

Earth Engine classes, such as ee.Image and ee.ImageCollection, are extended with eemont. New methods and constructors are added to these classes in order to make the code more fluid by being friendly with the Python method chaining.

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.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 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

indices = eemont.indices()
indices.BAIS2.formula # check info about spectral indices
indices.BAIS2.reference

eemont.listIndices() # Check all available indices

• 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

• Time series by region (or regions):

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)

• New Geometry, Feature and Feature Collection constructors:

seattle_bbox = ee.Geometry.BBoxFromQuery('Seattle',user_agent = 'my-eemont-query-example')
cali_coords = ee.Feature.PointFromQuery('Cali, Colombia',user_agent = 'my-eemont-query-example')
amazonas_river = ee.FeatureCollection.MultiPointFromQuery('Río Amazonas',user_agent = 'my-eemont-query-example')

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.

License

The project is licensed under the MIT license.