To get image statistics in multiple regions stored in a FeatureCollection,
you can use image.reduceRegions() to reduce multiple regions at once.
The input to reduceRegions() is an Image and a
FeatureCollection. The output is another FeatureCollection
with the reduceRegions() output set as properties on each Feature.
In this example, means of the Landsat 7 annual composite bands in each feature geometry
will be added as properties to the input features:
Code Editor (JavaScript)
// Load input imagery: Landsat 7 5-year composite. var image = ee.Image('LANDSAT/LE7_TOA_5YEAR/2008_2012'); // Load a FeatureCollection of counties in Maine. var maineCounties = ee.FeatureCollection('TIGER/2016/Counties') .filter(ee.Filter.eq('STATEFP', '23')); // Add reducer output to the Features in the collection. var maineMeansFeatures = image.reduceRegions({ collection: maineCounties, reducer: ee.Reducer.mean(), scale: 30, }); // Print the first feature, to illustrate the result. print(ee.Feature(maineMeansFeatures.first()).select(image.bandNames()));
import ee import geemap.core as geemap
Colab (Python)
# Load input imagery: Landsat 7 5-year composite. image = ee.Image('LANDSAT/LE7_TOA_5YEAR/2008_2012') # Load a FeatureCollection of counties in Maine. maine_counties = ee.FeatureCollection('TIGER/2016/Counties').filter( ee.Filter.eq('STATEFP', '23') ) # Add reducer output to the Features in the collection. maine_means_features = image.reduceRegions( collection=maine_counties, reducer=ee.Reducer.mean(), scale=30 ) # Print the first feature, to illustrate the result. display(ee.Feature(maine_means_features.first()).select(image.bandNames()))
Observe that new properties, keyed by band name, have been added to the
FeatureCollection to store the mean of the composite in each
Feature geometry. As a result, the output of the print statement should
look something like:
Feature (Polygon, 7 properties)
type: Feature
geometry: Polygon, 7864 vertices
properties: Object (7 properties)
B1: 24.034822192925134
B2: 19.40202233717122
B3: 13.568454303016292
B4: 63.00423784301736
B5: 29.142707062821305
B6_VCID_2: 186.18172376827042
B7: 12.064469664746415