英文论文研究An RM-NN algorithm for retrieving land surface temperature.pdf

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JOURNALOFGEOPHYSICALRESEARCH,VOL.112,D21102,doi:10.1029/2007JD008428,2007

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AnRM-NNalgorithmforretrievinglandsurfacetemperatureand

emissivityfromEOS/MODISdata

KebiaoMao,1,2,3JianchengShi,4,5Zhao-LiangLi,6,7andHuajunTang8

Received18January2007;revised27April2007;accepted28June2007;published1November2007.

[1]ThreeradiativetransferequationsarebuiltforMODISbands29,31,and32,which

involvesixunknownparameters(averageatmospherictemperature,landsurface

temperature(LST),threebandemissivities,andwatervaporcontent).Therelationships

betweengeophysicalparametershavebeenanalyzedindetail,whichindicatesthatneural

networkisoneofthebestmethodstoresolvetheseill-posedproblems(LSTand

emissivity).Retrievalanalysisindicatesthatthecombinedradiativetransfermodel(RM)

withneuralnetwork(NN)algorithmcanbeusedtosimultaneouslyretrieveland

surfacetemperatureandemissivityfromModerate-ResolutionImagingSpectroradiometer

(MODIS)data.SimulationdataanalysisindicatesthattheaverageerrorofLSTis

under0.4Kandtheaverageerrorofemissivityisunder0.008,0.006,and0.006for

bands29,31,and32,respectively.Thecomparisonanalysisbetweenretrievalresultby

RM-NNandMODISproductalgorithmindicatesthatthegeneralizedsplitwindow

LSToverestimatestheemissivityandunderestimateslandsurfacetemperature.The

retrievalresultsbyRM-NNliebetweenthetwoproductsprovidedbyNASAandcloserto

day/nightLSTalgorithmafterstatisticsanalysis.Theaverageerroris0.36Krelative

toMODISLSTproduct(MOD11_L2)retrievedbygeneralizedsplitwindowalgorithmif

wemakearegressionrevision.Thecomparisonofretrievalresultswithground

measurementdatainXiaotangshanalsoindicatesthattheRM-NNcanbeusedtoretrieve

accuratelylandsurfacetemperatureandemissivi

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