温度反演经典文章(13)

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Z.-L.Lietal./RemoteSensingofEnvironment131(2013)14–37

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TheNEMmoduleis rstusedtoestimatetheinitialLSTandthenormalizedemissivitiesfromtheatmosphericallycorrectedradiancesatgroundlevel(Gillespieetal.,1996).Subsequently,theSRmoduleisemployedtocalculatetheratioofthenormalizedemissivitiestotheiraverage.AlthoughtheSRmodulecannotdirectlyobtaintheactualLSE,ithasbeendemonstratedtodescribetheshapeoftheemissivityspectrumwellevenifthesurfacetemperatureisroughlyestimatedbytheNEMmodule.Finally,onthebasisoftheresultsoftheSRmod-ule,theMMDmoduleisutilizedto ndthespectralcontrast(i.e.,theMMD)inNchannels,thentoestimatetheminimumLSEusingtheempiricalrelationshipbetweentheminimumLSE(LSEmin)inNchan-nelsandtheMMD.OnceLSEminisestimated,theLSEsintheotherchannelscanbestraightforwardlyderivedfromtheSR,andthentheLSTcanbere nedandestimated(Gillespieetal.,1998).

ThemainadvantageoftheTESisthatitcombinesattractivefea-turesofthreeprecursorsandusesanempiricalrelationshipbetweentherangeofemissivitiesandtheminimumemissivityintheNchan-nelstoretrievetheLSTandLSEs.Consequently,itcanbeappliedtoanykindofnaturalsurfacewithoutconsideringspectralvariationsintheemissivity,especiallyforsurfaceswithhighspectralcontrastemissivitiessuchasrocksandsoils(Gillespieetal.,1998;Sobrinoetal.,2008).Numericalsimulationandsome eldvalidationshavedem-onstratedthattheTEScanretrievetheLSTtowithinabout±1.5KandtheLSEstowithinabout±0.015whentheatmosphericeffectsareaccuratelycorrected(Gillespieetal.,1996,1998;Sawabeetal.,2003).Besides,HulleyandHook(2011)recentlyre nedtherelation-shipbetweenLSEminandMMDtomakeTESalgorithmavailableforMODIS'sthreeTIRchannels(29,31and32).

However,somereportshaveindicatedthattheTESmethodexhibitedsigni canterrorsintheLSTandLSEsofsurfaceswithlowspectralcontrastemissivity(e.g.,water,snow,vegetation)andunderhotandwetatmosphericconditions(Colletal.,2007;Gillespieetal.,1996,2011;Hulley&Hook,2009b,2011;Sawabeetal.,2003).Saboletal.(2009)pointedoutthatthelowemissivitycontrastandhighemis-sivitycontrasthavebeentreateddifferentlyinoriginalversionofTES.Consequently,theretrievedLSEsaretoolowandtheLSTistoohighintheoriginalversionforthematerials(suchassoils,vegetationandwater/snow)thatareplottedabovetheregressionlineinthescatterplotofLSEminandMMD.Thatiswhysomestudieshavereportedthatin-accurateatmosphericcorrectionsmayproduceLSTerrorsof2–4Kforbaresoil(Dashetal.,2002).Forwarmandwetatmosphericconditions,thecauseofsigni canterrorsisdifferent.Theuncertaintiesintheatmo-sphericcorrectionswillresultinalargeapparentemissivitycontrast.Thiseffectismoreseriousovergraybodysurfaces(Hulley&Hook,2011).Tominimizeatmosphericcorrectionerrors,Gillespieetal.(2011)improvedtheTESmethodbyusingawatervaporscaling(WVS)approachproposedbyTonooka(2005).

Asshownbynumericalsimulations,theuncertaintiesontheLSTandLSEretrievalsincreasewhenthenumberofchannelsisreduced,makingtheTESmethodinapplicabletomostoperationalsensors(Sobrinoetal.,2008).Moreover,sensorcalibrationerrorsandnoiseintheTIRchannelsalsocauseuncertaintiesintheretrievedLSTandLSEs(Gillespieetal.,2011;Jiménez-Muñozetal.,2006;Sobrinoetal.,2008).Inaddition,TESscaleslow-andhigh-contrastsurfacesdif-ferently,whichleadstostepdiscontinuitiesattheedgesofgraybodyunitssuchaswater,forests,andcrops(Sobrinoetal.,2007).Toover-cometheseproblems,Saboletal.(2009)recentlyreplacedthepowerrelationshipofLSEminandMMDintheoriginalTESmethodwithalinearexpression,andappliedthenewrelationshipavailableforallmaterialstoalleviatesuchdiscontinuities.Thisrevisionwasreportedtoreduceslightlytheaccuracyforbothrocksurfacesandgraybodiesbutcanimprovetheprecisionfornear-graybodysurfaces.

3.2.2.5.Iterativespectrallysmoothtemperatureemissivityseparationmethod(ISSTES).HyperspectralTIRdataprovidesmuchmoredetailedspectralinformationabouttheatmosphereandlandsurface.Borel

(1997,1998,2008)reportedthatatypicalemissivityspectrumisrathersmoothcomparedwiththespectralfeaturesintroducedbytheatmosphere.AccordingtotheRTEgiveninEq.(4),iftheLSTisnotaccuratelyestimated,thecorrespondingLSEspectrumwillexhib-ittheatmosphericspectralfeatures,i.e.,therewillbesawteethcausedbytheatmosphericabsorptionlinesontheestimatedLSEspectrum.ThebestestimatesoftheLSTandLSEshouldbeobtainedwhenthespectralsmoothnessoftheretrievedLSEismaximized.Basedonthisproperty,theiterativespectrallysmoothtemperatureemissivityseparationmethod(ISSTES)hasbeendevelopedtoiterativelyre-trievetheLSTandLSEsfromhyperspectralTIRdata.Varioussmooth-nesscriteriaincludingthe rstandsecondderivativehavebeenproposed(Borel,2008;Chengetal.,2010;Kananietal.,2007;OuYangetal.,2010),thoughtheyallleadtothesamestatisticalper-formanceregardlessofthedetailsofthesmoothnessfunction.

IngramandMuse(2001)analyzedthemethod'ssensitivitytosmoothnessassumptionsandmeasurementnoiseandfoundthattheretrievalaccuracycausedbytheassumptionsisnegligiblefortyp-icalmaterialsbutisdependentontheSNR,i.e.,highaccuracycanbeobtainedwithhighSNR.Similartomostmethodspresentedabove,theatmosphericcorrectionneedstobeaccuratelyperformed,anditsimpactontheretrievalresultsisthegreatestamongallin uences.Theretrievalaccuracyisalsosensitivetoshiftsinthecentralwave-lengthsandbandwidthsoftheTIRchannels(Borel,2008).Inaddition,Wangetal.(2011)reportedthattheoccurrenceofsingularpointsmayleadtodif cultiesin ndinganacceptablesolutionwhentheLSTisclosetotheeffectivetemperatureofthedownwardatmospher-icradiance.

3.2.2.6.Linearemissivityconstrainttemperatureemissivityseparationmethod(LECTES).InspiredbytheGBEinitiallyproposedbyBarducciandPippi(1996),Wangetal.(2011)proposedanewTESmethodtore-trievesimultaneouslybothLSTandLSEsfromatmosphericallycorrectedhyperspectralTIRdata.Thismethodassumesthattheemis-sivityspectrumcanbedividedintoMsegmentsandthattheemissivityineachsegmentvarieslinearlywiththewavelength.Thus,theemissiv-ityspectrumcanbereconstructedusingapiecewiselinearfunctionwithgainsaakandoffsetsbbk(k=1,…,M),andtheLSTandLSEscanbesimultaneouslyobtainedprovidedthatthenumbero …… 此处隐藏：5362字，全部文档内容请下载后查看。喜欢就下载吧 ……