1. An implemented method for predicting a performance of a remote sensor material identification process under one or more given environmental condition and at least one uncertainty, the remote material identification process being used for automatically identifying a preselected target material within imagery acquired from one or more remote sensors, the method comprising: setting at least one environmental parameter representative of the one or more environmental conditions and at least one uncertainty associated with at least one of the environmental parameters, or calibration of the one or more remote sensors; simulating, by a specially programmed computer processor, a “true” target spectral radiance at least one acquisition angle under the one or more environmental conditions, the target being made of a preselected material, the simulating being based on the at least one environmental parameter, a type of sensor, and a previously measured reflectance function for the preselected target material; performing, by the specially programmed computer processor, the remote material identification process using: a) the “true” target spectral radiance at the at least one acquisition angle, b) at least one environmental parameter, c) the material reflectance database, and d) at least one uncertainty to determine an estimated target signature and uncertainty associated with the estimated target signature, wherein the material identification process further identifies the target material by comparing: the estimated target signature with, a predicted spectral signature for each of at least one candidate material at each of at least one acquisition angle, the predicted spectral signature being determined by using 1) BRDF for the candidate material, 2) the environmental parameter, and 3) an uncertainty associated with the BRDF for the candidate material; and determining, by the specially programmed computer processor, a probability of a false identification, using results of said remote material identification process.