Utility of an Image-Based Canopy Reflectance Modeling Tool for Remote Estimation of LAI and Leaf Chlorophyll Content in Crop Systems

Remotely sensed data in the reflective optical domain function as a unique cost-effective source for providing spatially and temporally distributed information on key biophysical and biochemical parameters of land surface vegetation. The challenging task of estimating leaf chlorophyll content (C_ab) and leaf area index (LAI) is here undertaken for crop systems in Maryland using a regularized canopy reflectance (REGFLEC) modeling tool, which couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) models. Using 10-m resolution SPOT-5 imagery, REGFLEC effectuated robust retrievals of C_ab and LAI for a diversity of agricultural fields characterized by a wide range in leaf chlorophyll and LAI levels with relative root-mean-square deviations on the order of 11% and 15%, respectively. REGFLEC is made entirely image-based by incorporating radiometric information from pixels belonging to the same land cover class during a LUT-based model inversion approach.