Efficiency of BRDF sampling and bias on the average photometric behavior

Second, we test the analysis scheme of a collection of photometric data acquired from various locations in order to answer the question: are these locations photometrically homogeneous or not? For instance, this question arises when combining data from an entire planetary body, where each spatial position is sampled at a single geometry. We tested the ability of the Bayesian method to decipher two situations, in the presence of noise: (i) a photometrically homogeneous surface (all observations with the same photometric behavior), or (ii) an heterogeneous surface with two distinct photometrical properties (half observations with photometric behavior 1, other half with photometric behavior 2). We show that the naive interpretation of the results provided by Bayesian method is not able to solve this problem. Therefore, we propose a separability test based on chi-square analysis, which is able to distinguish the two situations and thus, to access information about the photometric heterogeneity of a planetary body. When the noise level is high (10%), our simulations show that separability cannot be solved.