ee.FeatureCollection.kriging

    • The kriging method on a FeatureCollection returns an Image representing the results of sampling a Kriging estimator at each pixel.

    • The method requires specifying the propertyName to be estimated, the semivariogram shape , range , sill , and nugget .

    • Optional arguments include maxDistance and reducer to control the inclusion of features and collapsing of overlapping points.

    • Examples demonstrate using kriging to generate an interpolated surface of air temperature from sampled points in both JavaScript and Python.

     Returns the results of sampling a Kriging estimator at each pixel.
    Usage Returns
    FeatureCollection. kriging (propertyName, shape, range, sill, nugget, maxDistance , reducer ) Image
    Argument Type Details
    this: collection
    		FeatureCollection Feature collection to use as source data for the estimation.
    propertyName
    		String Property to be estimated (must be numeric).
    shape
    		String Semivariogram shape (one of {exponential, gaussian, spherical}).
    range
    		Float Semivariogram range, in meters.
    sill
    		Float Semivariogram sill.
    nugget
    		Float Semivariogram nugget.
    maxDistance
    		Float, default: null Radius which determines which features are included in each pixel's computation, in meters. Defaults to the semivariogram's range.
    reducer
    		Reducer, default: null Reducer used to collapse the 'propertyName' value of overlapping points into a single value.

    Examples

    Code Editor (JavaScript) 

     /** 
 * This example generates an interpolated surface using kriging from a 
 * FeatureCollection of random points that simulates a table of air temperature 
 * at ocean weather buoys. 
 */ 
 // Average air temperature at 2m height for June, 2020. 
 var 
  
 img 
  
 = 
  
 ee 
 . 
 Image 
 ( 
 'ECMWF/ERA5/MONTHLY/202006' 
 ) 
  
 . 
 select 
 ([ 
 'mean_2m_air_temperature' 
 ], 
  
 [ 
 'tmean' 
 ]); 
 // Region of interest: South Pacific Ocean. 
 var 
  
 roi 
  
 = 
  
 ee 
 . 
 Geometry 
 . 
 Polygon 
 ( 
  
 [[[ 
 - 
 156.053 
 , 
  
 - 
 16.240 
 ], 
  
 [ 
 - 
 156.053 
 , 
  
 - 
 44.968 
 ], 
  
 [ 
 - 
 118.633 
 , 
  
 - 
 44.968 
 ], 
  
 [ 
 - 
 118.633 
 , 
  
 - 
 16.240 
 ]]], 
  
 null 
 , 
  
 false 
 ); 
 // Sample the mean June 2020 temperature surface at random points in the ROI. 
 var 
  
 tmeanFc 
  
 = 
  
 img 
 . 
 sample 
 ( 
  
 { 
 region 
 : 
  
 roi 
 , 
  
 scale 
 : 
  
 25000 
 , 
  
 numPixels 
 : 
  
 50 
 , 
  
 geometries 
 : 
  
 true 
 }); 
  
 // 250 
 // Generate an interpolated surface from the points using kriging; parameters 
 // are set according to interpretation of an unshown semivariogram. See section 
 // 2.1 of https://doi.org/10.14214/sf.369 for information on semivariograms. 
 var 
  
 tmeanImg 
  
 = 
  
 tmeanFc 
 . 
 kriging 
 ({ 
  
 propertyName 
 : 
  
 'tmean' 
 , 
  
 shape 
 : 
  
 'gaussian' 
 , 
  
 range 
 : 
  
 2.8e6 
 , 
  
 sill 
 : 
  
 164 
 , 
  
 nugget 
 : 
  
 0.05 
 , 
  
 maxDistance 
 : 
  
 1.8e6 
 , 
  
 reducer 
 : 
  
 ee 
 . 
 Reducer 
 . 
 mean 
 () 
 }); 
 // Display the results on the map. 
 Map 
 . 
 setCenter 
 ( 
 - 
 137.47 
 , 
  
 - 
 30.47 
 , 
  
 3 
 ); 
 Map 
 . 
 addLayer 
 ( 
 tmeanImg 
 , 
  
 { 
 min 
 : 
  
 279 
 , 
  
 max 
 : 
  
 300 
 }, 
  
 'Temperature (K)' 
 );

    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) 

     # This example generates an interpolated surface using kriging from a 
 # FeatureCollection of random points that simulates a table of air temperature 
 # at ocean weather buoys. 
 # Average air temperature at 2m height for June, 2020. 
 img 
 = 
 ee 
 . 
 Image 
 ( 
 'ECMWF/ERA5/MONTHLY/202006' 
 ) 
 . 
 select 
 ( 
 [ 
 'mean_2m_air_temperature' 
 ], 
 [ 
 'tmean' 
 ] 
 ) 
 # Region of interest: South Pacific Ocean. 
 roi 
 = 
 ee 
 . 
 Geometry 
 . 
 Polygon 
 ( 
 [[ 
 [ 
 - 
 156.053 
 , 
 - 
 16.240 
 ], 
 [ 
 - 
 156.053 
 , 
 - 
 44.968 
 ], 
 [ 
 - 
 118.633 
 , 
 - 
 44.968 
 ], 
 [ 
 - 
 118.633 
 , 
 - 
 16.240 
 ], 
 ]], 
 None 
 , 
 False 
 , 
 ) 
 # Sample the mean June 2020 temperature surface at random points in the ROI. 
 tmean_fc 
 = 
 img 
 . 
 sample 
 ( 
 region 
 = 
 roi 
 , 
 scale 
 = 
 25000 
 , 
 numPixels 
 = 
 50 
 , 
 geometries 
 = 
 True 
 ) 
 # Generate an interpolated surface from the points using kriging parameters 
 # are set according to interpretation of an unshown semivariogram. See section 
 # 2.1 of https://doi.org/10.14214/sf.369 for information on semivariograms. 
 tmean_img 
 = 
 tmean_fc 
 . 
 kriging 
 ( 
 propertyName 
 = 
 'tmean' 
 , 
 shape 
 = 
 'gaussian' 
 , 
 range 
 = 
 2.8e6 
 , 
 sill 
 = 
 164 
 , 
 nugget 
 = 
 0.05 
 , 
 maxDistance 
 = 
 1.8e6 
 , 
 reducer 
 = 
 ee 
 . 
 Reducer 
 . 
 mean 
 (), 
 ) 
 # Display the results on the map. 
 m 
 = 
 geemap 
 . 
 Map 
 () 
 m 
 . 
 set_center 
 ( 
 - 
 137.47 
 , 
 - 
 30.47 
 , 
 3 
 ) 
 m 
 . 
 add_layer 
 ( 
 tmean_img 
 , 
 { 
 'min' 
 : 
 279 
 , 
 'max' 
 : 
 300 
 , 
 'min' 
 : 
 279 
 , 
 'max' 
 : 
 300 
 }, 
 'Temperature (K)' 
 , 
 ) 
 m
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