Reducing a FeatureCollection

    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 
 );

    Python setup

    See the Python Environment page for information on the Python API and using geemap for interactive development.

     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 
 );

    Python setup

    See the Python Environment page for information on the Python API and using geemap for interactive development.

     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 .
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