基于最優(yōu)插值算法的紅外和微波遙感海表溫度數(shù)據(jù)融合

基于最優(yōu)插值算法的紅外和微波遙感海表溫度數(shù)據(jù)融合基于最優(yōu)插值算法的紅外和微波遙感海表溫度數(shù)據(jù)融合

摘要：

海表溫度是反映海洋環(huán)境變化重要指標之一，在氣候變化、海洋資源開發(fā)利用、海洋災(zāi)害預(yù)警等領(lǐng)域具有重要作用。傳統(tǒng)的海表溫度測量方式受到時間和空間限制，而遙感技術(shù)可以實現(xiàn)全球海表溫度的長時間序列監(jiān)測。然而，不同遙感數(shù)據(jù)的誤差和缺失問題限制了數(shù)據(jù)融合的準確性和精度。本文提出一種基于最優(yōu)插值算法的紅外和微波遙感海表溫度數(shù)據(jù)融合方法，以提高融合數(shù)據(jù)的準確性和精度。首先，對紅外和微波遙感數(shù)據(jù)進行質(zhì)量控制和空間匹配，去除異常值和缺失點。然后，基于最優(yōu)插值算法對兩種遙感數(shù)據(jù)進行融合，以得到更為完整和準確的海表溫度數(shù)據(jù)。最后，通過與實際海表溫度觀測數(shù)據(jù)進行比較，驗證本文融合方法的準確性和精度。研究結(jié)果表明，本文提出的基于最優(yōu)插值算法的紅外和微波遙感海表溫度數(shù)據(jù)融合方法可以有效提高遙感技術(shù)測量的海表溫度數(shù)據(jù)準確性和精度。

關(guān)鍵詞：紅外遙感；微波遙感；最優(yōu)插值算法；海表溫度數(shù)據(jù)融合

Abstract:

Seasurfacetemperature(SST)isanimportantindicatortoreflectthechangesinmarineenvironment.Itplaysanimportantroleinmanyfields,suchasclimatechange,marineresourcesdevelopmentandutilization,andmarinedisasterwarning.ThetraditionalmethodsofSSTmeasurementarelimitedbytimeandspace,whileremotesensingtechnologycanrealizelong-termsequencemonitoringofglobalSST.However,theerrorsandmissingproblemsofdifferentremotesensingdatalimittheaccuracyandprecisionofdatafusion.Inthispaper,amethodoffusinginfraredandmicrowaveremotesensingSSTdatabasedonoptimalinterpolationalgorithmisproposedtoimprovetheaccuracyandprecisionofthefusiondata.Firstly,thequalitycontrolandspatialmatchingofinfraredandmicrowaveremotesensingdataarecarriedouttoremoveoutliersandmissingpoints.Then,thetwokindsofremotesensingdataarefusedbasedonoptimalinterpolationalgorithmtoobtainmorecompleteandaccurateSSTdata.Finally,theaccuracyandprecisionofthefusionmethodproposedinthispaperareverifiedbycomparingwithactualSSTobservationdata.TheresearchresultsshowthatthemethodproposedinthispapercaneffectivelyimprovetheaccuracyandprecisionofremotesensingSSTdatameasurementbasedonoptimalinterpolationalgorithm.

Keywords：Infraredremotesensing;microwaveremotesensing;optimalinterpolationalgorithm;SSTdatafusion。Remotesensingtechnologyhasbeenwidelyusedtomeasureseasurfacetemperature(SST)inrecentyears.InfraredremotesensingandmicrowaveremotesensingaretwocommonlyusedmethodsformeasuringSST.However,eachmethodhasitsownadvantagesandlimitations.Infraredremotesensingcanprovidehigh-resolutionSSTdata,butitisaffectedbycloudsandatmosphericinterference.MicrowaveremotesensingcanmeasureSSTthroughcloudsandhasgoodtemporalcoverage,butthespatialresolutionisrelativelylow.

Toovercomethelimitationsofindividualmethods,datafusioncanbeusedtointegratetheadvantagesofbothmethodsandprovidemoreaccurateandcompleteSSTdata.Inthispaper,anoptimalinterpolationalgorithmisproposedtofuseinfraredandmicrowaveremotesensingSSTdata.ThealgorithmselectsthebestSSTdatafromeachsourcebasedontheiruncertaintiesandcombinesthemusingweightedaveraging.

Totesttheeffectivenessoftheproposedmethod,simulationswereconductedunderdifferentscenarios.TheresultsshowedthatthefusionmethodcansignificantlyimprovetheaccuracyandcompletenessofSSTdatacomparedtoindividualmethods,especiallyinareaswithhighcloudcoverorlowmicrowaveretrievalaccuracy.

Inaddition,thealgorithmwasappliedtorealSSTdataobtainedfromtheAdvancedVeryHighResolutionRadiometer(AVHRR)andtheAdvancedMicrowaveScanningRadiometerforEOS(AMSR-E)fortheperiodof2003-2011.TheresultsshowedthatthefusionmethodwasabletocapturethetemporalandspatialvariabilityofSSTaccurately,eveninareaswithfrequentcloudcover.

Overall,theproposedmethodprovidesapromisingapproachforobtainingmoreaccurateandcompleteSSTdata,whichiscrucialforunderstandingtheglobalclimatesystem,oceancirculation,andmarineecosystemdynamics。Inadditiontoitsimportanceforunderstandingglobalclimateandoceandynamics,accurateandcompleteSSTdataisalsocrucialforavarietyofpracticalapplications,includingweatherforecasting,oceanmodeling,andfisheriesmanagement.Forexample,SSTdatacanbeusedtoaccuratelypredicttheonsetandintensityofhurricanesandothertropicalstorms,aswellastoforecasttheabundanceanddistributionofcommerciallyimportantfishstocks.

TheavailabilityofaccurateandcompleteSSTdataisthereforevitalformanysectors,includingmarinescience,fisheries,andmeteorology.However,traditionalSSTmeasurementmethodssuchasinsitumeasurementsandsatelliteremotesensinghavelimitationsthatcanimpedeourabilitytoobtainreliableandcomprehensivedata.

Insitumeasurements,whichinvolvedeployingtemperaturesensorsthroughouttheocean,providehighlyaccurateSSTdatabutarelimitedintheirspatialcoverageandtemporalresolution.Satelliteremotesensing,whichusesinstrumentssuchasAVHRRandAMSR-Etomeasuretheradiationemittedbytheocean'ssurface,canprovidemorecomprehensivespatialcoveragebutisoftenhamperedbythepresenceofclouds,whichcanobscuretheviewoftheocean'ssurface.

ThefusionmethodproposedinthestudyaddressestheselimitationsbycombiningdatafrommultiplesourcestocreateamorecompleteandaccuratepictureofSST.Byintegratingdatafromvarioussatellitesensorsandblendingthemwithinsitumeasurements,theproposedmethodisabletocapturethespatialandtemporalvariabilityofSSTwithahighdegreeofaccuracyandprecision.

Inconclusion,accurateandcomprehensiveSSTdataisessentialforunderstandingthecomplexdynamicsoftheworld'soceansandclimatesystems.Thefusionmethodproposedinthestudyoffersapromisingapproachforobtainingsuchdata,bycombiningmultiplesourcesofdatatocreateamorecompleteandreliablepictureofSST.Assuch,themethodhasimportantimplicationsforawiderangeoffields,frommarinescienceandfisheriestometeorologyandclimatemodeling。InadditiontotheimportanceofaccurateSSTdataforunderstandingtheoceansandclimatesystems,therearealsopracticalimplicationsforindustriesandsocietyasawhole.Forexample,reliableSSTdatacanhelpimproveweatherforecastingandstormtracking,whichcanprovideearlywarningandreducetheimpactsofnaturaldisastersoncoastalcommunities.

Moreover,SSTdataplaysacrucialroleinthemanagementoffisheriesandothermarineresources.ChangesinSSTcanaffectthedistributionandabundanceoffishpopulations,whichcanhavesignificanteconomicandsocialimpactsoncoastalcommunitiesthatrelyonfishingfortheirlivelihoods.AccurateSSTdatacanhelpfisheriesmanagersmakeinformeddecisionsaboutwhenandwheretofish,whichcanhelpsustainfishpopulationsoverthelongterm.

Beyondtheseimmediateapplications,accurateSSTdataisalsoessentialforunderstandingthelarger-scaledynamicsoftheglobalclimatesystem.TheoceansplayacriticalroleinregulatingtheEarth'sclimate,absorbingandstoringheatandcarbondioxide.ChangesinSSTcanaffectthetransferofheatandenergybetweentheoceansandatmosphere,whichinturncaninfluenceweatherpatternsandglobalclimate.

Forexample,theElNi?o-SouthernOscillation(ENSO)isanaturalphenomenonthataffectsSSTintheequatorialPacificOceanandhasfar-reachingimpactsonweatherpatternsaroundtheworld.DuringanElNi?oevent,warm,nutrient-poorwaterdisplacescool,nutrient-richwater,whichcanleadtodroughtsinsomeregionsandfloodsinothers.AccurateSSTdataisessentialforunderstandingandpredictingthesecomplexdynamics,whichcanhelpgovernmentsandcommunitiesprepareforandmitigatetheimpactsofextremeweatherevents.

Overall,thefusionmethodproposedinthestudyrepresentsavaluabletoolforimprovingtheaccuracyandreliabilityofSSTdata,withimportantimplicationsforawiderangeoffieldsandapplications.Inarapidlychangingworld,wherehumanactivitiesarealteringtheoceansandclimateinunprecedentedways,accurateSSTdataismoreimportantthaneverforunderstandingandmanagingthecomplexsystemsthatsustainlifeonEarth。AccurateSSTdataiscriticalforavarietyofdifferentfieldsandapplications.Thefusionmethodproposedbythestudycanhelpimprovetheaccuracyandreliabilityofthesedata,whichcanhaveimportantimplicationsforunderstandingandmitigatingtheimpactsofextremeweatherevents.

Forexample,understandingSSTscanhelpuspredictandprepareforhurricanes,whicharepoweredbythewarmwatersoftheocean.TheaccuracyofSSTdatacanalsoimpactthefishingindustry,ascertainfishspeciespreferspecifictemperatureranges.Inaddition,scientistsstudySSTstounderstandhowtheoceansareabsorbingheatandcarbondioxidefromtheatmosphere,whichcanhelpusbetterpredictandpreparefortheeffectsofclimatechange.

AccurateSSTdatacanalsobeusedbypolicymakerstomakeinformeddecisionsaboutthemanagementofmarineresourcesandtheoceanenvironment.Forexample,understandingSSTscanhelpusidentifyareasthataresensitivetoclimatechange,anddevelopstrategiesforprotectingthem.Itcanalsohelpustounderstandhowdifferentareasoftheoceanareinterconnected,whichcaninformdecisionsaboutmarineconservationandmanagement.

Inconclusion,thefusionmethodproposedbythestudyhasimportantimplicationsforawiderangeoffieldsandapplications.Asourplanetcontinuestochangeatanunprecedentedrate,accurateSSTdataismoreimportantthaneverforunderstandingandmanagingthecomplexsystemsthatsustainlifeonEarth.ImprovedSSTdatacanhelpuspredictandprepareforextremeweatherevents,protectmarineresources,anddevelopstrategiesformitigatingtheeffectsofclimatechange。Furthermore,accurateSSTdataisvitalforavarietyofindustriessuchasmarinetransportation,fishing,andoffshoreenergyproduction.ThetimelyandreliablepredictionofSSTcanhelpshippingcompaniesplantheirroutesandavoidareasofextremeweatherconditions,thussavingtimeandcost.AccurateSSTdatacanalsohelpfishersoptimizetheirfishingstrategies,avoidingareaswithlowproductivityandpotentialfishstockdepletion.Additionally,offshoreenergycompaniesrelyonaccurateSSTdataforsafetypurposes,asextremeweatherconditionsandtemperatureshiftscanaffectthestabilityandsafetyoftheirplatforms.

Thefusionmethodproposedinthestudycanalsohaveimplicationsforthetourismindustry.AccurateSSTdatacanhelptouristsplantheirtripsandchoosedestinationsbasedonweatherconditions,reducingthelikelihoodofunexpectedweathereventsthatcanruinvacations.Inturn,thiscanhelpthetourismindustrytoattractmorevisitorsandgeneratemorerevenue.

Overall,thedevelopmentandapplicationofaccurateSSTdatahasfar-reachingimplicationsforawiderangeoffieldsandindustries.ThefusionmethodproposedbythestudycanhelptoimprovetheaccuracyofSSTdataandpromotethesustainablemanagementofourplanet'sresources.Itiscrucialthatscientists,policymakers,andindustriesworktogethertocontinuedevelopingandapplyingtoolsandtechnologiestoimproveourunderstandingoftheEarth'ssystemsandmanagetheimpactofenvironmentalchange。TheaccuracyofSSTdataisparticularlyimportantinthefieldofclimatescience.Globalwarmingiscausingchangesinoceantemperatures,whichcanhavesignificantimpactsonmarineecosystemsandweatherpatterns.UnderstandingchangesinSSTcanhelpresearcherspredictandmanagetheimpactsofglobalwarming,suchassealevelrise,oceanacidification,andincreasedstormintensity.

AccurateSSTdataisalsoimportantforthefishingindustry,whichreliesheavilyonoceantemperaturestolocateandcatchfish.Fishspecieshavespecifictemperaturepreferencesandmigrationpatterns,andchangesinSSTcanaltertheirdistributionandbehavior.AccurateSSTdatacanhelpfishermenmakeinformeddecisionsaboutwhereandwhentofish,whichcanhelptosustainfishpopulationsandsupporttheirlivelihoods.

Inaddition,theshippingindustryreliesonSSTdatatoinformrouteplanningandoptimizefuelconsumption.Changesinoceantemperaturescanaffectwaterdensityandcurrents,whichcanimpactshipspeedandfuelefficiency.AccurateSSTdatacanhelpshipcaptainsmakedecisionsthatimprovesafety,savefuel,andreduceshippingemissions.

TheagricultureindustryalsoreliesonSSTdataforcropmanagement.ChangesinSSTcaninfluenceweatherpatterns,whichcanaffectprecipitationandtemperatureconditions,leadingtocropyieldvariations.AccurateSSTdatacanhelpfarmersmakeinformeddecisionsaboutplanting