On utilization of a priori knowledge in inversion of remote sensing models

Satellite remote sensing deals with a complex system coupling atmosphere an d surface. Any physical model with reasonable precision needs several to te ns of parameters. Without a priori knowledge of these parameters, Propositi on 3 of Verstraete et al. requires the number of independent observations t o be greater than the number of unknown parameters. This requirement can ha rdly be satisfied even in the coming EOS era. As Tarantola pointed out, the inversion problems in geoscience are always underdetermined in some sense. In order to make good use of every kind of a priori knowledge for effectiv ely extracting information from remote sensing observations, the right ques tion to set is as follows: Given an imperfect model and a certain amount of a priori information on model parameters, in which sense should one modify the a priori information, given the actual observation with noise? A prior i knowledge of physical parameters can be presented in different ways such as physical limits, global statistical means and variance for a certain lan dcover type, or previous statistics and temporal variation of a specific ta rget. When such a priori knowledge can be expressed as joint probability de nsity, Bayessian theorem can be used in the inversion to obtain posterior p robability densities of parameters using newly acquired observations. There is no prerequirement on how many independent observations must be made, an d the knowledge gained merely depends on the information content of the new observations. Some specific problems about knowledge accumulation and rene wal are also discussed.