Jl. Privette et al., ESTIMATING SPECTRAL ALBEDO AND NADIR REFLECTANCE THROUGH INVERSION OFSIMPLE BRDF MODELS WITH AVHRR MODIS-LIKE DATA/, J GEO RES-A, 102(D24), 1997, pp. 29529-29542
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.