Background:Monitoringforesthealthandbiomassforchangesovertimeintheglobalenvironmentrequirestheprovisionofcontinuoussatelliteimages.However,opticalimagesoflandsurfacesaregenerallycontaminatedwhencloudsarepresentorrainoccurs.Methods:Toestimatetheactualreflectanceoflandsurfacesmaskedbycloudsandpotentialrain,3DsimulationsbytheRAPIDradiativetransfermodelwereproposedandconductedonaforestfarmdominatedbybirchandlarchinGenheCity,DaXing’AnLingMountaininInnerMongolia,China.Thecanopyheightmodel(CHM)fromlidardatawereusedtoextractinpidualtreestructures(location,height,crownwidth).Fieldmeasurementsrelatedtreeheighttodiameterofbreastheight(DBH),lowestbranchheightandleafareaindex(LAI).SeriesofLandsatimageswereusedtoclassifytreespeciesandlandcover.MODISLAIproductswereusedtoestimatetheLAIofinpidualtrees.CombiningalltheseinputvariablestodriveRAPID,high-resolutionopticalremotesensingimagesweresimulatedandvalidatedwithavailablesatelliteimages.Results:Evaluationsonspatialtexture,spectralvaluesanddirectionalreflectancewereconductedtoshowcomparableresults.Conclusions:Thestudyprovidesaproof-of-conceptapproachtolinklidarandMODISdataintheparameterizationofRAPIDmodelsforhightemporalandspatialresolutionsofimagereconstructioninforestdominatedareas.
