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基于多源遥感数据的气溶胶类型识别模型研究 Title:ResearchonAerosolTypeIdentificationModelbasedonMulti-sourceRemoteSensingData Abstract: AerosolsplayasignificantroleinEarth'satmospherebyinteractingwithsunlight,impactingclimate,andinfluencingairquality.Accurateidentificationofaerosoltypesiscrucialforunderstandingtheirsources,distribution,andeffectsontheenvironment.Thispaperaimstoinvestigateanddevelopamodelforaerosoltypeidentificationusingmulti-sourceremotesensingdata.Theproposedmodelintegratesdatafromvarioussatellitesensorsandemploysmachinelearningalgorithmstodistinguishdifferentaerosoltypes. 1.Introduction: 1.1Background: Aerosolsaretinysolidorliquidparticlessuspendedintheatmosphere.Theycanoriginatefromnaturalsourcessuchasduststorms,volcaniceruptions,andseasalts,aswellasanthropogenicactivitieslikeindustrialemissionsandbiomassburning.Differentaerosoltypesexhibitdistinctopticalpropertiesandhavevaryingimpactsonclimateandairquality.Therefore,accurateidentificationofaerosoltypesiscrucialforenvironmentalmonitoringandassessment. 1.2Objective: Themainobjectiveofthisresearchistodevelopamodelforaerosoltypeidentificationusingmulti-sourceremotesensingdata.Themodelaimstoleveragethecapabilitiesofvarioussatellitesensorstodistinguishdifferentaerosoltypesandprovidevaluableinsightsintotheirspatialdistributionandtemporalvariation. 2.LiteratureReview: Severalstudieshavebeenconductedonaerosoltypeidentificationusingremotesensingdata.Thesestudieshaveutilizedvariousapproaches,includingspectralanalysis,multi-angleobservations,andmachinelearningalgorithms.However,mostofthesestudiesfocusedonusingasinglesatellitesensororlimitedgeographicalareas.Thepresentresearchaimstoovercometheselimitationsbyintegratingdatafrommultiplesensorsandexpandingtheanalysistocoverawidergeographicregion. 3.Methodology: 3.1DataCollection: Multi-sourceremotesensingdatawillbecollectedfromdifferentsatellitesensors,suchasMODIS,CALIOP,andOMI.Thesesensorsprovidemeasurementsofaerosolopticalproperties,suchasaerosolopticaldepth,aerosolsizedistributio