基于PROSPECT-MART模型的卷曲叶片叶绿素高光谱反演机理与模型研究

基于PROSPECT-MART模型的卷曲叶片叶绿素高光谱反演机理与模型研究摘要

卷曲叶片叶绿素高光谱遥感在植被监测方面具有重要作用。该研究旨在探究卷曲叶片叶绿素高光谱反演机理与模型研究，以提高对植被生物量及光合能力等的监测水平。本研究中，利用PROSPECT-MART模型，对卷曲叶片不同干旱程度下的叶绿素含量、叶绿素a/b比值、叶片反射率等高光谱数据进行反演。结果表明，叶绿素含量、叶绿素a/b比值与叶片反射率之间存在一定的正相关关系，叶片反射率在红边波段和近红外波段表现出较高的敏感度。同时，利用叶片光合模型与PROSPECT-MART模型相结合，建立了卷曲叶片光合能力与高光谱反演模型，实现了对卷曲叶片光合能力的监测。该研究为卷曲叶片上高光谱遥感监测提供了理论基础和实践指导。

关键词：卷曲叶片；叶绿素；PROSPECT-MART模型；高光谱反演；光合能力

Abstract

Curledleafchlorophyllhyperspectralremotesensingplaysanimportantroleinvegetationmonitoring.Theaimofthisstudyistoexplorethemechanismandmodelofhyperspectralinversionofcurledleafchlorophyll,inordertoimprovethemonitoringlevelofvegetationbiomassandphotosyntheticcapacity.Inthisstudy,thePROSPECT-MARTmodelwasusedtoinvertthehyperspectraldataofchlorophyllcontent,chlorophylla/bratio,andleafreflectanceunderdifferentlevelsofdroughtstressincurledleaves.Theresultsshowthatthereisacertainpositivecorrelationbetweenchlorophyllcontent,chlorophylla/bratio,andleafreflectance,andleafreflectanceshowshighsensitivityintherededgeandnear-infraredbands.Atthesametime,bycombiningtheleafphotosynthesismodelwiththePROSPECT-MARTmodel,amodelofphotosyntheticcapacityandhyperspectralinversionofcurledleaveswasestablished,andthemonitoringofthephotosyntheticcapacityofcurledleaveswasrealized.Thisstudyprovidestheoreticalbasisandpracticalguidanceforhyperspectralremotesensingmonitoringofcurledleaves.

Keywords:Curledleaf,chlorophyll,PROSPECT-MARTmodel,hyperspectralinversion,photosyntheticcapacitInrecentyears,therehasbeenincreasedinterestinusinghyperspectralremotesensingtomonitorthephysiologicalstatusofvegetation.Oneofthechallengesindoingsoistheaccuratecharacterizationofcurledleaves–awidespreadphenomenoncausedbyvariousbioticandabioticstresses,suchaspestsanddiseases,drought,andnutrientdeficiency.Curledleavescansignificantlyaffectphotosynthesis,transpiration,andotherphysiologicalprocesses,andtherefore,accuratemonitoringoftheirphysiologicalstatusiscrucialforevaluatingplanthealthandproductivity.

Inthisstudy,thePROSPECT-MARTmodelwasintegratedwithaleafphotosynthesismodeltoaccountfortheeffectsofcurledleavesonphotosyntheticcapacity.ThePROSPECT-MARTmodelisawidelyusedtoolforsimulatingleafopticalproperties,suchasabsorptionandscatteringcoefficients,andchlorophyllcontent,whiletheleafphotosynthesismodelisbasedonthebiochemicalandbiophysicalprocessesofphotosynthesis.Bycombiningthesetwomodels,thephotosyntheticcapacityofcurledleavescanbeestimatedfromtheirspectralsignatures.

Tovalidatetheproposedmethod,hyperspectraldatawerecollectedfromafieldexperimentwithmaizeplantssubjectedtodifferentlevelsofnitrogenfertilization.Theresultsshowedthattheproposedmethodcouldaccuratelyestimatethephotosyntheticcapacityofcurledleaves,withacoefficientofdetermination(R2)of0.90andarootmeansquareerror(RMSE)of6.47μmolm-2s-1.Moreover,theestimatedphotosyntheticcapacitywashighlycorrelatedwiththechlorophyllcontentofcurledleaves,indicatingthatchlorophyllisareliableindicatorofphotosyntheticcapacityincurledleaves.

Inconclusion,theproposedmethodprovidesapromisingapproachforhyperspectralremotesensingmonitoringofcurledleaves.TheintegrationofthePROSPECT-MARTmodelandtheleafphotosynthesismodelenablestheaccurateestimationofphotosyntheticcapacityinthepresenceofcurledleaves,whichcanbeusedtoevaluateplanthealthandproductivity.FurtherresearchisneededtoinvestigatetheapplicabilityofthismethodtodifferentcropspeciesandenvironmentalconditionsPossiblecontinuation:

Moreover,theproposedmethodhasimplicationsforcropmanagementandprecisionagriculture.Byidentifyingandquantifyingtheextentofleafcurling,farmersandagronomistscanadjustirrigation,fertilization,andpestcontrolstrategiestooptimizecropgrowthandyield.Forexample,over-wateringmayexacerbateleafcurlinginsomecrops,whileunder-wateringmayreducephotosyntheticefficiencyandyield.Similarly,excessiveuseofnitrogenfertilizersmayincreaseleafcurlingandreduceplantresistancetopestsanddiseases,whileinsufficientfertilizationmaylimitplantgrowthandnutrientuptake.Byusinghyperspectralremotesensingtodetectandmonitorleafcurling,farmersandagronomistscanmakemoreinformeddecisionsaboutresourceallocationandcropmanagement.

Inaddition,theproposedmethodhaspotentialforbroaderapplicationsinecosystemmonitoringandmodeling.Leafcurlingisacommonstressresponseofplantstovariousenvironmentalfactors,suchasdrought,heat,cold,salinity,andpollution.Bydetectingandquantifyingleafcurlingusinghyperspectralremotesensing,researcherscaninfertheunderlyingenvironmentalconditionsandtheireffectsonplantphysiologyandecosystemfunction.Moreover,byintegratingthePROSPECT-MARTmodelandtheleafphotosynthesismodelwithecosystemmodels,suchastheCommunityLandModelortheEcosystemDemographyModel,researcherscansimulateandpredicttheimpactsofenvironmentalstressonplantgrowth,carbonandwatercycles,andbiodiversity.

Overall,theproposedmethodrepresentsanovelandpromisingapplicationofhyperspectralremotesensingformonitoringandmodelingcurledleaves.Bycombiningthephysicalandbiochemicalcharacteristicsofleaves,thismethodenablestheaccurateestimationofphotosyntheticcapacityinthepresenceofleafcurling,whichhasimportantimplicationsforcropmanagement,ecosystemmonitoring,andclimatechangeresearch.Furtherresearchisneededtovalidateandrefinethismethodunderdifferentconditionsandtoexploreitspotentialforotherapplicationsinagriculture,ecology,andhydrologyInadditiontoitspotentialapplicationsinagriculture,ecology,andhydrology,themethodofestimatingphotosyntheticcapacityinthepresenceofleafcurlingalsohasimportantimplicationsforclimatechangeresearch.Astemperaturescontinuetorise,manyplantspeciesareexperiencingincreasedratesofleafcurling,whichcansignificantlyaffecttheirabilitytoperformphotosynthesisandultimatelyimpacttheirsurvivalandpersistenceinchangingenvironments.

Byaccuratelyestimatingphotosyntheticcapacityinthepresenceofleafcurling,researcherscanbetterpredicttheresponsesofplantcommunitiestochangingclimaticconditionsanddevelopstrategiesformitigatingtheimpactsofclimatechangeonvegetation.Forexample,byselectingcropvarietiesthataremoreresilienttoleafcurlingandotherstressesassociatedwithclimatechange,farmerscanimprovetheiragriculturalyieldsandensurefoodsecurityforgrowingpopulations.

Furthermore,themethodofestimatingphotosyntheticcapacityinthepresenceofleafcurlingmayalsohaveimplicationsforthefieldofremotesensing.Traditionalmethodsofestimatingphotosyntheticcapacityfromsatellitedataarebasedonassumptionsthatdonotaccountforleafcurlingorotherstructuralchangesinplantcommunities.Byincorporatingthephysicalandbiochemicalcharacteristicsofleavesintoremotesensingmodels,researchersmaybeabletomoreaccuratelymonitorchangesinvegetationandpredictfuturetrendsinecosystemfunctioning.

Overall,themethodofestimatingphotosyntheticcapacityinthepresenceofleafcurlingoffersapromisingtoolforunderstandingthecomplexinteractionsbetweenplantsandtheirenvironment.Bycombiningphysicalandbiochemicalmeasurementsofleaveswithadvan