智慧农业（中英文）SmartAgricultureVol.3,No.2
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EstimatingGrainProteinContentofWinterWheatin
ProducingAreasBasedonRemoteSensingand
MeteorologicalData
1,2341,251,2
WANGLin,LIANGJian,MENGFanyu,MENGYang,ZHANGYongtao,LIZhenhai
（1.KeyLaboratoryofQuantitativeRemoteSensinginMinistryofAgricultureandRuralAffairs/BeijingResearch
CenterforInformationTechnologyinAgriculture,Beijing100097,China;2.NationalEngineeringResearchCenter
forInformationTechnologyinAgriculture,Beijing100097,China;3.NationalAgro-techExtensionandServiceCen‐
ter,Beijing100125,China;4.BeijingAgricultureTechnologyExtensionStation,Beijing100029,China;5.Jiangsu
NonidtAgriculturalScienceandTechnologyCo.Ltd,Nanjing210001,China）
Abstract:Withtherapiddevelopmentofeconomyandpeople''slivingstandards,people''sdemandsforcropshavechangedfrom
quantitytoquality.Theriseandrapiddevelopmentofremotesensingtechnologyprovidesaneffectivemethodforcropmonitor‐
ing.Accuratelypredictingwheatqualitybeforeharvestishighlydesirabletooptimizemanagementforfarmers,gradingharvest
andcategorizedstoragefortheenterprise,futuretradingprice,andpolicyplanning.Inthisresearch,themainproducingareasof
winterwheat(Henan,Shandong,Hebei,AnhuiandJiangsuprovinces)werechosedastheresearchareas,withcollected898
samplesofwinterwheatovergrowingseasonsof2008,2009and2019.AHierarchicalLinearmodel(HLM)forestimating
grainproteincontent(GPC)ofwinterwheatatheading-floweringstagewasconstructedtoestimatetheGPCofwinterwheatin
2019byusingmeteorologicalfactors,remotesensingimageryandglutentypeofwinterwheat,whereremotesensingdataand
glutentypewereinputvariablesatthefirstlevelofHLMandthemeteorologicaldatawasusedasthesecondlevelofHLM.To
solvetheproblemofdeviationininterannualandspatialexpansionofGPCestimationmodel,maximumvaluesofEnhanced
VegetationIndex(EVI)fromApriltoMaycalculatedbymoderate-resolution-imagingspectroradiometerwerecomputedtorep‐
resentthecropgrowthstatusandusedintheGPCestimationmodel.Criticalmeteorologicalfactors(temperature,precipitation,
radiation)andtheircombinationsforGPSestimationwerecomparedandthebestestimationmodelwasusedinthisstudy.The
resultsshowedthattheaccuracyofGPCconsideringthreemeteorologicalfactorsperformedhigheraccuracy(Calibratedset:
22
R=0.39,RMSE=1.04%;Verificationset:R=0.43,RMSE=0.94%)thantheothersGPCmodelwithtwometeorologicalfac‐
torsorsinglemeteorologicalfactor.Therefore,threemeteorologicalfactorswereusedasinputvariablestobuildawinterwheat
GPCforecastmodelfortheregionalwinterwheatGPCforecastinthisresearch.TheGPCestimationmodelwasappliedtothe
GPCremotesensingestimationofthemainwinterwheat-producingareas,andtheGPCpredictionmapofthemainwinter
wheatproducingareasin2019wasobtained,whichcouldobtainthedistributionofwinterwheatqualityintheHuang-Huai-Hai
region.Theresultsofthisstudycouldprovidedatasupportforsubsequentwheatplantingregionalizationtoachievegreen,high-
yield,high-qualityandefficientgrainproduction.
Keywords:winterwheat;grainproteincontent(GPC);remotesensing;hierarchicallinearmodel(HLM);meteorologicaldata
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