Remote sensing inversion algorithms exist the problems of land surface parameters retrieval by means of remote sensing physical models

which is also a key problem in remote sensing image interpretation. We developed a priori knowledge based remote sensing inversion strategy. All the available information on parameters gained from the former inversion stages are taken as the prior knowledge in the next stage’s inversion. To do the inversion objectively

we need to describe the information content that the parameters get in each inversion stage

and the reliability of the accumulated knowledge as well. The concepts and theory of unascertained number and blind number are developed from the requirements of describing the uncertain information in application. They have been used in some techniques and producing fields to solve the practical problems

such as that used in the engineering theory. Our new approach is to describe the reliability of the inverted parameter and accumulated knowledge by the definition of the unascertained number. For the inverted parameter

during the inversion stage

the changing of its reliability then can be calculated by using math method of the unascertained mathematics

which is used to judge the inversion result and to modify the prior knowledge about the parameter. This is expected to be a quantitative expression of the accumulation of a priori knowledge. We take the land surface temperatures inversion as an example to show how the new method works.