温度反演经典文章(19)

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

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complicatestheseparateretrievalofsurfaceparameters(LSTandLSEs)andatmosphericpro les.Thedeterminationofsurfaceparam-etersfromspacerequiresknowledgeoftheatmosphericpro lesandviceversa.ItisthereforenaturalthoughchallengingtodevelopamethodthatsimultaneouslyretrievestheLST,LSEs,andatmosphericpro les(oratmosphericquantitiesusedintheatmosphericcorrec-tions)withoutanyaprioriknowledgeaboutthesurfaceoratmo-sphere.Maetal.(2000,2002)madea rstattemptatretrievingthoseparametersfrommultispectralTIRmeasurements.WiththeappearanceofhyperspectralTIRsensors,thethousandsofnarrowbandwidthchannelsinTIRcansupplyenoughverticalresolutiontoallowextractionofatmosphericinformationandcanalsoprovidemorephysicalconstraintstoaccuratelyseparatetheLSTandtheLSEs.Althoughafewstudieshavebeenconductedinrecentyears(Lietal.,2007;Wangetal.,2013),therearestillatleasttwoaspectsthatrequireincreasedattentioninthefuture.First,rapidandaccurateRTEmodelsmustbedevelopedtomeettherequirementsofaccuracyandspeedintheretrievalprocess.Second,ANNsandphysicalretriev-almethodsshouldalsobemodi edordevelopedtoimprovethere-trievalaccuracies.Forexample,moredetailsshouldbeconsideredintheANNs,includingthearchitecturesandlearningschemes,selec-tionofrepresentativetrainingdata,andthechannelsemployed.Atthesametime,additionalconstraints,suchasthelinearemissivityconstraintproposedbyWangetal.(2011),biningANNsandphysics-basedmethodsalsorepresentsanoptioninthenearfuture,becausetheadvantagesofthesetwotechniquescancomplementeachother:ANNscanprovideini-tialguessesfortheLST,LSEs,andatmosphericpro les(oratmosphericquantities),andthenphysicalretrievalmethodscanfurtherimprovetheseinitialguesses.

5.2.MethodologytosimultaneouslyderiveLSTandLSEfromthenewgenerationofgeostationarysatelliteswithmultispectralandmulti-temporaldata

Thenewgeostationarysatellitesareprevailingoverthepolar-orbitsatellitesininvestigatingthetemporalevolutionoflandsurfaceandatmosphericinformationbecausetheyprovidehigh-frequencyobservationsat xedviewinganglesoverthesamesurfacedespitetheircoarserspatialresolutions.EffortshavefocusedonretrievingtheLSTfrommultispectraldatabutwithoutconsideringmulti-temporalinformation.ItisthereforeveryattractivetodevelopanewmethodtosimultaneouslyretrievetheLSTandLSEbytakingad-vantageofthemultispectralandmulti-temporalinformationprovid-edbythegeostationarysatellites.Withthegeostationarysatellitedata,time-andangle-consistentLSTscanbedirectlyproducedusingthesenewLSTretrievalmethodswithoutneedingtotemporallyorangularlynormalizetheLST.

5.3.Re nementofLSTretrievalalgorithmswiththeconsiderationofaerosolandcirruseffects

AtmosphericcorrectionisoneofthemostimportantissuesintheLSTretrievalalgorithms,anderrorsinatmosphericcorrectiondirectlydecreasetheaccuracyofthe nalderivedLST.BecauseofthehightransmittanceofaerosolintheTIRchannel(approximately0.95–0.98inMODISTIRchannels)(Wan,1999)undernormalclear-skyconditionsandthelackofreal-timeaerosolestimates(aerosolload-ing,sizedistributions,types,andscatteringphasefunctions),anaver-ageaerosoldistributionandaconstantaerosolloadinghavebeenusedinthedevelopmentofalloftheLSTretrievalalgorithmsreviewedinSection3.TheeffectofaerosolonLSTretrievalisrelative-lysmallcomparedwiththeeffectofwatervapor,butitcannotbeig-noredwhenaimingforhighlyaccurateLSTsforuseincertainspecial

applications,especiallyinthepresenceofheavyaerosolloadings(Jiménez-Muñoz&Sobrino,2006).ToimprovetheaccuracyofLSTre-trieval,existingLSTretrievalalgorithmsmustbere ned,ornewalgorithmsmustbedevelopedtocorrectfortheaerosoleffect,partic-ularlyinthecaseofheavyaerosolloading.

Inaddition,theeffectofcirruscloudsonLSTretrievalshouldalsobeconsidered.Cirruscloudsarealwaysconsideredtobecloudcon-taminationinmanyLSTretrievalalgorithmsandthepixelscoveredbycirruscloudsarescreenedoutindatapreprocessing.Becausether-malinfraredwavelengthscanpenetratecirruslayers,itispossibletoobtaintheLSTundercirruscoverfromTIRdata.Tothisend,newLSTretrievalalgorithmsshouldbedevelopedtocompensatefortheeffectofthecirrusclouds.

5.4.RetrievalofcomponenttemperaturesinheterogeneouspixelsInaheterogeneousandnon-isothermalpixel,theobservedradi-anceistheensembleradianceofseveralcomponents(e.g.,soilandvegetation).Thepixel-averagetemperaturedoesnotre ecttherealtemperatureofeachcomponent.Ifeachcomponentisassumedtobeisothermal,thecomponenttemperatureencapsulatesmorephys-icalmeaningthanthepixel-averagevalueandprovidesbetterparam-eterizationsoftheheat uxesattheland-atmosphereinterface.Therefore,thecomponenttemperaturesofamixedpixelaremoreimportantthantheaveragevalues.However,theretrievalofcompo-nenttemperaturesisdif cultbecausemorevariables,includingthecomponentemissivitiesandatmosphericeffects,mustbeknowninadvance.Severalauthorshaveattemptedtoretrievecomponenttem-peraturesfrommulti-angulardata(Jiaetal.,2003;Lietal.,2001;Menentietal.,2001;Shi,2011).Themethodsthattheyhavedevel-opedarefarfromsatisfyingandshouldbeimprovedinthefuture.Inaddition,furtherinvestigationsshouldfocusonminingtheauxilia-ryinformationprovidedbyspatial,temporal,andspectraldata.Be-causedifferentVZAsmaycorrespondtodifferentpixelsizes,newalgorithmsareexpectedtousehyperspectralTIRdataatagivenVZA,astheinformationregardingthecomponenttemperatureswith-inamixedpixelisincludedinthehyperspectralTIRdata.

5.5.MethodologyforretrievingLSTfrompassivemicrowavedataandforcombiningLSTsretrievedfromTIRandpassivemicrowavedataTheTIRdataprovidestheLSTwitha nespatialresolution(e.g.,severalkilometers),butitlosesef ciencywhenthelandsurfaceisfullyorpartlycoveredbyclouds.Incontrast,microwavescanpene-trateclouds,allowingforLSTretrievalinallweatherconditionsbutwithacoarserspatialresolution(uptotensofkilometers)(Airesetal.,2004).TIRandmicrowavedatacanthuscomplementeachother,andthecombinationofthetwoisapromisinglineofresearchforproducinglong-termLSTproductsinallweatherconditionswithaspatialresolutionas neasthatofTIRdata.Futurestudiesare …… 此处隐藏：4698字，全部文档内容请下载后查看。喜欢就下载吧 ……