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1、RemoteSensingofEnvironment115(2011)3069–3079ContentslistsavailableatScienceDirectRemoteSensingofEnvironmentjournalhomepage:www.elsevier.com/locate/rseAstatisticalspatialdownscalingalgorithmofTRMMprecipitationbasedonNDVIandDEMintheQaidamBasinofChinaaa,ba,⁎,TingtingYanaShaof
2、engJia,WenbinZhu,AifengLűaInstituteofGeographicalSciencesandNaturalResourcesResearch,CAS,Beijing100101,ChinabGraduateUniversityofChineseAcademyofSciences,Beijing100039,ChinaarticleinfoabstractArticlehistory:Theavailabilityofprecipitationdatawithhighspatialresolutionisoffun
3、damentalimportanceinseveralReceived4January2011applicationssuchashydrology,meteorologyandecology.Atpresent,therearemainlytwosourcesofReceivedinrevisedform10June2011precipitationestimates:raingaugestationsandremotesensingtechnology.However,alargenumberofAccepted11June2011st
4、udiesdemonstratedthattraditionalpointmeasurementsbasedonraingaugestationscannotreflecttheAvailableonline23July2011spatialvariationofprecipitationeffectively,especiallyinungaugedbasins.Thetechnologyofremotesensinghasgreatlyimprovedthequalityofprecipitationobservationsandprod
5、ucedreasonablyhighresolutionKeywords:Downscalinggriddedprecipitationfields.Theseproducts,derivedfromsatellites,havebeenwidelyusedinvariouspartsofPrecipitationtheworld.However,whenappliedtolocalbasinsandregions,thespatialresolutionoftheseproductsistooTRMMcoarse.Inthispaper,w
6、epresentastatisticaldownscalingalgorithmbasedontherelationshipsbetweenNDVIprecipitationandotherenvironmentalfactorsintheQaidamBasinsuchastopographyandvegetation,whichDEMwasdevelopedfordownscalingthespatialprecipitationfieldsoftheseremotesensingproducts.ThisQaidamBasinalgori
7、thmisdemonstratedwiththeTropicalRainfallMeasuringMission(TRMM)3B43dataset,theDigitalElevationModel(DEM)fromtheShuttleRadarTopographyMission(SRTM)andSPOTVEGETATION.Thestatisticalrelationshipamongprecipitation,DEMandNormalizedDifferenceVegetationIndex(NDVI),whichisaproxyforv
8、egetation,isvariableatdifferentscales;therefore,amultiplelinearregressionmodelwasestablis
