[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\u003cp\u003e\u003ccode\u003ekriging()\u003c/code\u003e interpolates values across a FeatureCollection using specified Kriging parameters to generate an Image.\u003c/p\u003e\n"],["\u003cp\u003eIt estimates values for each pixel based on the spatial correlation of a numeric property within the input FeatureCollection.\u003c/p\u003e\n"],["\u003cp\u003eThe interpolation process is guided by a semivariogram model defined by \u003ccode\u003eshape\u003c/code\u003e, \u003ccode\u003erange\u003c/code\u003e, \u003ccode\u003esill\u003c/code\u003e, and \u003ccode\u003enugget\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eUsers can specify a search radius (\u003ccode\u003emaxDistance\u003c/code\u003e) and a reducer to handle overlapping points (\u003ccode\u003ereducer\u003c/code\u003e).\u003c/p\u003e\n"]]],["The `kriging` method interpolates a surface from a `FeatureCollection` by sampling a Kriging estimator at each pixel, returning an `Image`. Key parameters include: `propertyName` (numeric property to estimate), `shape` (semivariogram shape), `range`, `sill`, and `nugget` (semivariogram values). `maxDistance` limits feature inclusion in pixel calculations. An optional `reducer` handles overlapping points. Example demonstrates creating a temperature surface from sampled points, setting Kriging parameters, and visualizing the result.\n"],null,["# ee.FeatureCollection.kriging\n\nReturns the results of sampling a Kriging estimator at each pixel.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|------------------------------------------------------------------------------------------------------|---------|\n| FeatureCollection.kriging`(propertyName, shape, range, sill, nugget, `*maxDistance* `, `*reducer*`)` | Image |\n\n| Argument | Type | Details |\n|--------------------|------------------------|------------------------------------------------------------------------------------------------------------------------------------|\n| this: `collection` | FeatureCollection | Feature collection to use as source data for the estimation. |\n| `propertyName` | String | Property to be estimated (must be numeric). |\n| `shape` | String | Semivariogram shape (one of {exponential, gaussian, spherical}). |\n| `range` | Float | Semivariogram range, in meters. |\n| `sill` | Float | Semivariogram sill. |\n| `nugget` | Float | Semivariogram nugget. |\n| `maxDistance` | Float, default: null | Radius which determines which features are included in each pixel's computation, in meters. Defaults to the semivariogram's range. |\n| `reducer` | Reducer, default: null | Reducer used to collapse the 'propertyName' value of overlapping points into a single value. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n/**\n * This example generates an interpolated surface using kriging from a\n * FeatureCollection of random points that simulates a table of air temperature\n * at ocean weather buoys.\n */\n\n// Average air temperature at 2m height for June, 2020.\nvar img = ee.Image('ECMWF/ERA5/MONTHLY/202006')\n .select(['mean_2m_air_temperature'], ['tmean']);\n\n// Region of interest: South Pacific Ocean.\nvar roi = ee.Geometry.Polygon(\n [[[-156.053, -16.240],\n [-156.053, -44.968],\n [-118.633, -44.968],\n [-118.633, -16.240]]], null, false);\n\n// Sample the mean June 2020 temperature surface at random points in the ROI.\nvar tmeanFc = img.sample(\n {region: roi, scale: 25000, numPixels: 50, geometries: true}); //250\n\n// Generate an interpolated surface from the points using kriging; parameters\n// are set according to interpretation of an unshown semivariogram. See section\n// 2.1 of https://doi.org/10.14214/sf.369 for information on semivariograms.\nvar tmeanImg = tmeanFc.kriging({\n propertyName: 'tmean',\n shape: 'gaussian',\n range: 2.8e6,\n sill: 164,\n nugget: 0.05,\n maxDistance: 1.8e6,\n reducer: ee.Reducer.mean()\n});\n\n// Display the results on the map.\nMap.setCenter(-137.47, -30.47, 3);\nMap.addLayer(tmeanImg, {min: 279, max: 300}, 'Temperature (K)');\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# This example generates an interpolated surface using kriging from a\n# FeatureCollection of random points that simulates a table of air temperature\n# at ocean weather buoys.\n\n# Average air temperature at 2m height for June, 2020.\nimg = ee.Image('ECMWF/ERA5/MONTHLY/202006').select(\n ['mean_2m_air_temperature'], ['tmean']\n)\n\n# Region of interest: South Pacific Ocean.\nroi = ee.Geometry.Polygon(\n [[\n [-156.053, -16.240],\n [-156.053, -44.968],\n [-118.633, -44.968],\n [-118.633, -16.240],\n ]],\n None,\n False,\n)\n\n# Sample the mean June 2020 temperature surface at random points in the ROI.\ntmean_fc = img.sample(region=roi, scale=25000, numPixels=50, geometries=True)\n\n# Generate an interpolated surface from the points using kriging parameters\n# are set according to interpretation of an unshown semivariogram. See section\n# 2.1 of https://doi.org/10.14214/sf.369 for information on semivariograms.\ntmean_img = tmean_fc.kriging(\n propertyName='tmean',\n shape='gaussian',\n range=2.8e6,\n sill=164,\n nugget=0.05,\n maxDistance=1.8e6,\n reducer=ee.Reducer.mean(),\n)\n\n# Display the results on the map.\nm = geemap.Map()\nm.set_center(-137.47, -30.47, 3)\nm.add_layer(\n tmean_img,\n {'min': 279, 'max': 300, 'min': 279, 'max': 300},\n 'Temperature (K)',\n)\nm\n```"]]
