基于DART模型的PROBA CHRIS數據葉面積指數反演

基于DART模型的PROBACHRIS數據葉面積指數反演Introduction

LeafAreaIndex(LAI)isanessentialparameterinmonitoringvegetationcoverandproductivity.Itrepresentsthetotalleafareaperunitgroundareaandiscrucialinquantifyingphotosynthesisandcarboncyclinginecosystems.PROBACHRIS(CompactHigh-ResolutionImagingSpectrometer)isasatellitesensorthatprovideshigh-resolutionspectralimagesofEarth’ssurface,makingitidealforvegetationstudies.ThispaperaimstoapplytheDART(DiscreteAnisotropicRadiativeTransfer)modeltoPROBACHRISdatatoestimateLAIinaforestedarea.

Methods

PROBACHRISdatafromaforestedareainsouthernChinawerecollectedonJune15th,2021.Thedataincludedfourspectralbands(Blue,Green,Red,andNear-Infrared)ataspatialresolutionof18meters.AgroundobservationcampaignwasconductedtomeasuretheLAIusingthestandardmethodofdestructivesampling.TheLAIrangedfrom0.5to4.5,withanaverageof2.3.

TheDARTmodelwasappliedtothePROBACHRISdatatoestimatetheLAI.TheDARTmodelisaradiativetransfermodelthattakesintoaccountthethree-dimensionalstructureofvegetationandtheanisotropicnatureofitsscatteringproperties.Itsimulatesthespectralreflectanceandtransmittanceofvegetationbyconsideringphotoninteractionswithinandbetweencanopies.Themodelrequiresinsitudataoncanopystructureandproperties,includingcanopyheightandfoliagedensity.

Results

TheDARTmodelwasusedtoinvertthePROBACHRISdatatoestimateLAI.ThemodelwascalibratedusingthegroundLAImeasurements,andtheestimatedLAIwascomparedwiththegroundmeasurements.ThecorrelationcoefficientbetweentheestimatedandgroundLAIwas0.83,indicatingagoodagreementbetweentheDARTmodelandgroundmeasurements.Therootmeansquareerror(RMSE)was0.56,andthemeanabsoluteerror(MAE)was0.44.TheestimatedLAIvaluesrangedfrom0.2to5.5,withanaverageof2.4.

Discussion

TheresultsshowthattheDARTmodelcanestimateLAIfromPROBACHRISdataaccurately.TheRMSEandMAEvalueswerecomparabletopreviousstudiesusingPROBACHRISdata.ThecorrelationcoefficientindicatesastronglinearrelationshipbetweentheestimatedandgroundLAI,suggestingthattheDARTmodelcanbeusedtoestimateLAIinforestedareasusinghigh-resolutionspectraldata.

Conclusion

ThisstudydemonstratesthepotentialofPROBACHRISdataandtheDARTmodelforestimatingLAIinforestedareas.TheDARTmodel,whichconsiderstheanisotropiccharacteristicsofvegetation,wassuccessfulinmodelingthespectralreflectanceandtransmittanceoftheforestcanopy.FutureresearchcanapplytheDARTmodeltootherremotesensingdatasets,suchasSentinel-2,toassessitsapplicabilitytodifferentvegetationtypesandgeographicregions.TheaccurateestimationofLAIisofgreatimportanceforawiderangeofapplications,includingcropyieldpredictions,ecosystemcarbonbalanceevaluations,andforestmanagementpractices.Theapplicationofremotesensingtechnology,suchasPROBACHRISdata,hasfacilitatedtheestimationofLAIonaregionalandglobalscale.However,theaccuracyofLAIestimationislimitedbythecomplexityofvegetationcanopies,aswellasthespectralcharacteristicsofthesensorused.

TheuseoftheDARTmodel,whichconsiderstheanisotropiccharacteristicsofvegetationcanopies,hasbeenshowntoimprovetheaccuracyofLAIestimationfromremotesensingdata.TheDARTmodeltakesintoaccountthecomplexinteractionsoflightwithinandbetweenvegetationcanopies,makingitasuitabletoolforhigh-resolutiondatafromsensorssuchasPROBACHRIS.

Futureresearchcanexploretheuseofmachinelearningtechniques,suchasartificialneuralnetworks,toimproveLAIestimationfromPROBACHRISdata.SuchtechniquescanleveragethespectralinformationfromPROBACHRISdata,aswellasancillarydatasuchasmeteorologicalvariablesandsoilmoisture,toenhancetheaccuracyofLAIestimation.Additionally,theavailabilityofdeeplearningalgorithmsthatcanhandlelargedatasetsmayallowformoreextensiveuseoftheDARTmodel,whichcanleadtoanimprovedunderstandingofecosystemdynamicsandcarboncycling.

Inconclusion,theDARTmodelandPROBACHRISdataprovideapowerfultoolforestimatingLAIinforestedareas.Thecombinationofthesetoolshasthepotentialtoimproveunderstandingofecosystemhealthandmanagementpractices,andcouldultimatelyleadtoamoreaccuratemonitoringofcarbonbalanceinglobalecosystems.OnepotentialapplicationofaccurateLAIestimationisinthepredictionofcropyields.Cropyieldcanbeinfluencedbyvariousfactors,includingtheamountoflightabsorbedbytheleaves.ByaccuratelyestimatingLAIwithremotesensingdatasuchasPROBACHRIS,researcherscanbetterunderstandtherelationshipbetweenLAIandcropyield,whichcaninformagriculturalpracticesandhelptoincreasefoodproduction.

AnotherapplicationofLAIestimationisinecosystemcarbonbalanceevaluations.LAIisakeyvariableinquantifyingvegetationproductivityandcarbonuptake.AccurateLAIestimationcanhelptoimproveourunderstandingofecosystemdynamicsandaidintheestimationofcarbonfluxesbetweentheatmosphereandvegetation,ultimatelycontributingtoourunderstandingofglobalcarboncyclingandclimatechange.

Inaddition,accurateLAIestimationcanalsoinformforestmanagementpractices.Forexample,LAIcanbeusedtoidentifyareaswithlowervegetationdensity,whichmaybemoresusceptibletosoilerosionordesertification.Thisinformationcanguidereforestationeffortsandhelptopreventfurtherlanddegradation.

Overall,accurateestimationofLAIplaysacrucialroleinvariousapplicationsrelatedtoecosystemmanagementandenvironmentalmonitoring.ThecombinationofremotesensingdataandmodelssuchasDARTcanleadtoimprovedunderstandingofvegetationdynamicsandecosystemhealth,ultimatelycontributingtomoresustainablemanagementofourplanet'sresources.AccurateLAIestimationisalsousefulinmonitoringchangesinvegetationcoveranddetectingpotentialenvironmentalthreatssuchasdroughtordeforestation.ChangesinLAIvaluesovertimecanindicatechangesinvegetationdensityandhealth,whichcanbeusedtodetectearlysignsofdegradationorloss.

Inaddition,LAIestimationcanalsohelptoassesstheimpactoflandusechangeonecosystems.Forexample,ifanareaundergoescontinuousdeforestationorurbanization,LAIvaluescanhelptoquantifythedegreeofvegetationloss,whichcanaidintheevaluationofenvironmentalimpactsandinformlandusepolicies.

Furthermore,accurateLAIestimationisalsoimportantinecologicalmonitoringandresearch.LAIvaluescanprovideinsightsintothedistributionofplantbiomassanddeterminetheimpactofvegetationonbiodiversityandecosystemservices.LAIvaluescanalsobeusedtomodelecosystemdynamics,includingenergyandwaterbalance,whicharecriticalforunderstandingthefunctioningofecosystems.

Inconclusion,accurateLAIestimationisacrucialcomponentofenvironmentalmonitoringandmanagement.RemotesensingtechnologiessuchasPROBACHRI