A priori knowledge accumulation and its application to linear BRDF model inversion

A priori knowledge can significantly improve the retrieval of surface bidir ectional reflectance and spectral albedo from satellite observations. Here a priori knowledge takes the form of field measurements of bidirectional re flectance factors for various surface cover types in red and near-infrared bands. Bidirectional reflectance and albedo retrieval refers to inversion o f a kernel-driven bidirectional reflectance distribution function (BRDF) mo del using surface reflectance observations derived from orbiting spacecraft . A priori knowledge is applied when noise and poor angular sampling reduce the accuracy of model inversion given a limited number of observations. In such cases, a priori knowledge can indicate when retrieved kernel weights or albedos are outside expected bounds, leading to a closer examination of data. If data are noisy, a priori knowledge can be used to smooth the data. If the data exhibit poor angular sampling, a priori knowledge can be used according to Bayesian inference theory to yield a posteriori estimates of u nknown kernel weights. In the latter application, Bayes theory is applied i n data space rather than in parameter space. Extensive study and simulation using 73 sets of field observations and 395 spaceborne observation sets fr om the POLDER instrument validates the importance of a priori information i n improving inversions and BRDF retrievals.