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 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!
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')
Methods¶
The above-mentioned features extend the following Earth Engine classes:
ee.Feature¶
|
Constructs an ee.Feature describing a bounding box from a query submitted to a geodocer using the geopy package. |
|
Constructs an ee.Feature describing a point from a query submitted to a geodocer using the geopy package. |
ee.FeatureCollection¶
|
Constructs an ee.Feature describing a point from a query submitted to a geodocer using the geopy package. |
ee.Geometry¶
|
Constructs an ee.Geometry describing a bounding box from a query submitted to a geodocer using the geopy package. |
|
Constructs an ee.Geometry describing a point from a query submitted to a geodocer using the geopy package. |
|
Constructs an ee.Geometry describing a multi-point from a query submitted to a geodocer using the geopy package. |
ee.Image¶
|
Computes one or more spectral indices (indices are added as bands) for an image. |
|
Masks clouds and shadows in an image (valid just for Surface Reflectance products). |
|
Scales bands on an image. |
ee.ImageCollection¶
|
Gets the closest image (or set of images if the collection intersects a region that requires multiple scenes) to the specified date. |
|
Gets the time series by region for the given image collection and geometry (feature or feature collection are also supported) according to the specified reducer (or reducers). |
|
Gets the time series by regions for the given image collection and feature collection according to the specified reducer (or reducers). |
|
Computes one or more spectral indices (indices are added as bands) for an image collection. |
|
Masks clouds and shadows in an image collection (valid just for Surface Reflectance products). |
|
Scales bands on an image collection. |
Non-Earth Engine classes such as pd.DataFrame are also extended:
pd.DataFrame¶
|
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 and time series supports all image collections with the
system:time_startproperty.
License¶
The project is licensed under the MIT license.