AI-generated Key Takeaways
-
Use
featureCollection.reduceColumns()to aggregate data in the properties of aFeatureCollection. -
Use
featureCollection.reduceRegions()to overlay features on imagery. -
When using
reduceColumns(), the return value is a dictionary, and you may need to cast the result to a number usingee.Number().
To aggregate data in the properties of a FeatureCollection
, use featureCollection.reduceColumns()
. For example, to check the area properties
in the watersheds FeatureCollection
, this code computes the Root Mean Square
Error (RMSE) relative to the Earth Engine computed area:
Code Editor (JavaScript)
// Load watersheds from a data table and filter to the continental US. var sheds = ee . FeatureCollection ( 'USGS/WBD/2017/HUC06' ) . filterBounds ( ee . Geometry . Rectangle ( - 127.18 , 19.39 , - 62.75 , 51.29 )); // This function computes the squared difference between an area property // and area computed directly from the feature's geometry. var areaDiff = function ( feature ) { // Compute area in sq. km directly from the geometry. var area = feature . geometry (). area (). divide ( 1000 * 1000 ); // Compute the difference between computed area and the area property. var diff = area . subtract ( ee . Number . parse ( feature . get ( 'areasqkm' ))); // Return the feature with the squared difference set to the 'diff' property. return feature . set ( 'diff' , diff . pow ( 2 )); }; // Calculate RMSE for population of difference pairs. var rmse = ee . Number ( // Map the difference function over the collection. sheds . map ( areaDiff ) // Reduce to get the mean squared difference. . reduceColumns ( ee . Reducer . mean (), [ 'diff' ]) . get ( 'mean' ) ) // Compute the square root of the mean square to get RMSE. . sqrt (); // Print the result. print ( 'RMSE=' , rmse );
import ee import geemap.core as geemap
Colab (Python)
# Load watersheds from a data table and filter to the continental US. sheds = ee . FeatureCollection ( 'USGS/WBD/2017/HUC06' ) . filterBounds ( ee . Geometry . Rectangle ( - 127.18 , 19.39 , - 62.75 , 51.29 ) ) # This function computes the squared difference between an area property # and area computed directly from the feature's geometry. def area_diff ( feature ): # Compute area in sq. km directly from the geometry. area = feature . geometry () . area () . divide ( 1000 * 1000 ) # Compute the difference between computed area and the area property. diff = area . subtract ( ee . Number . parse ( feature . get ( 'areasqkm' ))) # Return the feature with the squared difference set to the 'diff' property. return feature . set ( 'diff' , diff . pow ( 2 )) # Calculate RMSE for population of difference pairs. rmse = ( ee . Number ( # Map the difference function over the collection. sheds . map ( area_diff ) # Reduce to get the mean squared difference. . reduceColumns ( ee . Reducer . mean (), [ 'diff' ]) . get ( 'mean' ) ) # Compute the square root of the mean square to get RMSE. . sqrt () ) # Print the result. display ( 'RMSE=' , rmse )
In this example, note that the return value of reduceColumns()
is a dictionary
with key ‘mean’
. To get the mean, cast the result of dictionary.get()
to a number with ee.Number()
before trying to
call sqrt()
on it. For more information about ancillary
data structures in Earth Engine, see this
tutorial
.
To overlay features on imagery, use featureCollection.reduceRegions()
. For
example, to compute the volume of precipitation in continental US watersheds, use reduceRegions()
followed by a map()
:
Code Editor (JavaScript)
// Load an image of daily precipitation in mm/day. var precip = ee . Image ( ee . ImageCollection ( 'NASA/ORNL/DAYMET_V3' ). first ()); // Load watersheds from a data table and filter to the continental US. var sheds = ee . FeatureCollection ( 'USGS/WBD/2017/HUC06' ) . filterBounds ( ee . Geometry . Rectangle ( - 127.18 , 19.39 , - 62.75 , 51.29 )); // Add the mean of each image as new properties of each feature. var withPrecip = precip . reduceRegions ( sheds , ee . Reducer . mean ()) . filter ( ee . Filter . notNull ([ 'prcp' ])); // This function computes total rainfall in cubic meters. var prcpVolume = function ( feature ) { // Precipitation in mm/day -> meters -> sq. meters. var volume = ee . Number ( feature . get ( 'prcp' )) . divide ( 1000 ). multiply ( feature . geometry (). area ()); return feature . set ( 'volume' , volume ); }; var highVolume = withPrecip // Map the function over the collection. . map ( prcpVolume ) // Sort descending. . sort ( 'volume' , false ) // Get only the 5 highest volume watersheds. . limit ( 5 ) // Extract the names to a list. . reduceColumns ( ee . Reducer . toList (), [ 'name' ]). get ( 'list' ); // Print the resulting FeatureCollection. print ( highVolume );
import ee import geemap.core as geemap
Colab (Python)
# Load an image of daily precipitation in mm/day. precip = ee . Image ( ee . ImageCollection ( 'NASA/ORNL/DAYMET_V3' ) . first ()) # Load watersheds from a data table and filter to the continental US. sheds = ee . FeatureCollection ( 'USGS/WBD/2017/HUC06' ) . filterBounds ( ee . Geometry . Rectangle ( - 127.18 , 19.39 , - 62.75 , 51.29 ) ) # Add the mean of each image as new properties of each feature. with_precip = precip . reduceRegions ( sheds , ee . Reducer . mean ()) . filter ( ee . Filter . notNull ([ 'prcp' ]) ) # This function computes total rainfall in cubic meters. def prcp_volume ( feature ): # Precipitation in mm/day -> meters -> sq. meters. volume = ( ee . Number ( feature . get ( 'prcp' )) . divide ( 1000 ) . multiply ( feature . geometry () . area ()) ) return feature . set ( 'volume' , volume ) high_volume = ( # Map the function over the collection. with_precip . map ( prcp_volume ) # Sort descending and get only the 5 highest volume watersheds. . sort ( 'volume' , False ) . limit ( 5 ) # Extract the names to a list. . reduceColumns ( ee . Reducer . toList (), [ 'name' ]) . get ( 'list' ) ) # Print the resulting FeatureCollection. display ( high_volume )
For more information about reducing feature collections, see Statistics of FeatureCollection Columns and Vector to Raster Conversion .
