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1、LocalregressionkrigingapproachforanalysinghighdensitydataA,B,CBBWeiSun,BudimanMinasnyandAlexMcBratneyAChinaAgriculturalUniversity,EastCampus,ResearchCenterforPrecisionAgriculture.QingHuaDongLu17,Beijing,100083,P.R.China.BAustralianCentreforPrecisionAgriculture.Fac
2、ultyofAgriculture,FoodandNaturalResources,TheUniversityofSydney,NSW2006,Australia.CCorrespondingauthor.Emailsunwei1028@126.comAbstractThispaperdemonstratesthedevelopmentandanapplicationofalocalregression-kriging(RK)program.ItillustratestheperformanceofthelocalRKme
3、thodforpredictionofsoilpropertiesusinghighdensitycropyielddataintimeseries.WedevelopedasoftwarenamedRKGuidertocarryoutaserialstepsofRK,especiallylocalRKautomaticallyandusingtheprogram,weproduceahighresolutionsoilelectricalconductivitymapfromacoarsesurvey.Theresult
4、demonstratedthereisspatialcorrelationbetweenyielddataintimeseriesandsoilelectricalconductivity.HighdensityyielddatacanbetakenasauxiliaryvariablestopredictsoilECusingRK.KeyWordsLocalregressionkriging,variogrammodel,linearmodel,yield,electricalconductivity.Introduct
5、ionRegressionkrigingisaspatialpredictiontechniquewhichaddstheregressionvalueofexhaustivevariablesandthekrigingvalueofresidualstogether.ThelocalRKalgorithmwasdevelopedespeciallytotakeintoaccountthelocalcorrelationbetweenenvironmentalvariablesandtheunsatisfactorygoo
6、dnessoffitofthespatialvariancemodelfortheentiredataset.Besidethat,localRKisdevelopedtodealwithrichdata,usingthedatainfullcapacityortoimprovepredictionfromaglobalmodel.LocalRKresultisthesumofalocalregressionofauxiliaryvariablesandlocalkrigingoftheregressionresidual
7、s.ThisiscalledRKinamovingwindowandwasconsideredasthe“nextstep”forRKdevelopment(Hengletal.2007).Thisalgorithmwillplaymoreandmoreimportantroleingeostatisticsbecauselotsofcovariatesareavailabledramaticallynowbyadvancementtechnology(Pebesma2006).Howeverthereisaserious
8、constrainttowideruseofRK,especiallylocalRK,whichisthatausermustcarryoutvariousstepsinavarietyofsoftwarepackages(Hengletal.2007).Furthermore,thereisnosof