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ee.FeatureCollection.kriging

  • Thekriging method on aFeatureCollection returns anImage representing the results of sampling a Kriging estimator at each pixel.

  • The method requires specifying thepropertyName to be estimated, the semivariogramshape,range,sill, andnugget.

  • Optional arguments includemaxDistance andreducer to control the inclusion of features and collapsing of overlapping points.

  • Examples demonstrate usingkriging 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.

UsageReturns
FeatureCollection.kriging(propertyName, shape, range, sill, nugget,maxDistance,reducer)Image
ArgumentTypeDetails
this:collectionFeatureCollectionFeature collection to use as source data for the estimation.
propertyNameStringProperty to be estimated (must be numeric).
shapeStringSemivariogram shape (one of {exponential, gaussian, spherical}).
rangeFloatSemivariogram range, in meters.
sillFloatSemivariogram sill.
nuggetFloatSemivariogram nugget.
maxDistanceFloat, default: nullRadius which determines which features are included in each pixel's computation, in meters. Defaults to the semivariogram's range.
reducerReducer, default: nullReducer 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.varimg=ee.Image('ECMWF/ERA5/MONTHLY/202006').select(['mean_2m_air_temperature'],['tmean']);// Region of interest: South Pacific Ocean.varroi=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.vartmeanFc=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.vartmeanImg=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 usinggeemap for interactive development.

importeeimportgeemap.coreasgeemap

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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Last updated 2023-10-06 UTC.