ESTIMATING SPECTRAL ALBEDO AND NADIR REFLECTANCE THROUGH INVERSION OFSIMPLE BRDF MODELS WITH AVHRR MODIS-LIKE DATA/

In recent years, many computationally efficient bidirectional reflecta nce models have been developed to account for angular effects in land remote sensing data, particularly those from the NOAA advanced very hi gh resolution radiometer (AVHRR), polarization and directionality of t he Earth's reflectances (POLDER), and the planned EOS moderate-resolut ion imaging spectrometer (MODIS) and multi-angle imaging spectroradiom eter (MISR) sensors. In this study, we assessed the relative ability o f 10 such models to predict commonly used remote sensing products (nad ir reflectance and albedo). Specifically, we inverted each model with ground-based data from the portable apparatus for rapid acquisition of bidirectional observations of the land and atmosphere (PARABOLA) arra nged in subsets representative of satellite sampling geometries. We us ed data from nine land cover types, ranging from soil to grassland (Fi rst International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE)) to forest (Boreal Ecosystem-Atmosphere Study (BOREAS)). Retrieved parameters were used in forward model runs to est imate nadir reflectance and spectral albedo over a wide range of solar angles. We rank the models by the accuracy of the estimated products and find results to be strongly dependent on the view azimuth angle ra nge of the inversion data, and less dependent on the spectral band and land cover type. Overall, the nonlinear model of Rahman et al. [1993] and the linear kernel-driven RossThickLiSparse model [Wanner et al., 1995] were most accurate. The latter was at least 25 times faster to i nvert than the former. Interestingly, we found these two models were n ot able to match the various bidirectional reflectance distribution fu nction (BRDF) shapes as well as other models, suggesting their superio r performance lies in their ability to be more reliably inverted with sparse data sets. These results should be useful to those interested i n the computationally fast normalization of bidirectional reflectance data and the estimation of radiation flux parameters (albedo, absorbed radiation) over diverse land covers.