Scaling-up and Model Inversion methods with narrow-band Optical Indices for Chlorophyll Content Estimation in closed Forest Canopies with Hyperspectral Data

Abstract

Radiative transfer theory and modeling assumptions were applied at laboratory and field scales in order to study the link between leaf reflectance and transmittance and canopy hyperspectral data for chlorophyll content estimation. This study was focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma Region, Canada, where field measurements, laboratory-simulation experiments, and hyperspectral compact airborne spectrographic imager (CASI) imagery of 72 channels in the visible and near-infrared region and up to 1-m spatial resolution data were acquired in the 1997, 1998, and 1999 campaigns. A different set of 14 sites of the same species were used in 2000 for validation of methodologies. Infinite reflectance and canopy reflectance models were used to link leaf to canopy levels through radiative transfer simulation. The closed and dense ( 4) forest canopies of Acer saccharum M. used for this study, and the high spatial resolution reflectance data targeting crowns, allowed the use of optically thick simulation formulae and turbid-medium SAILH andMCRM canopy reflectance models for chlorophyll content estimation by scaling-up and by numerical model inversion approaches through coupling to the PROSPECT leaf radiative transfer model. Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as 750 710 perform better than when all single spectral reflectance channels from hyperspectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized. Estimates of leaf pigment by hyperspectral remote sensing of closed forest canopies were shown to be feasible with root mean square errors (RMSE’s) ranging from 3 to 5 5 g cm2. Pigment estimation by model inversion as described in this paper using these red edge indices can in principle be readily transferred to the MERIS sensor using the 750 705 optical index.