The retrieval of information related to physical surface parameters is a major objective of many studies in remote sensing investigations. In that context, modelling radar backscattering through natural surfaces has become an important team of research and active remote sensing has shown its utility for many applications in hydrology, geology and other specialities. Most traditional electromagnetic models consider natural surfaces as zero mean bi-dimensional stationary Gaussian random processes. Consequently, surfaces are characterized by an unique spatial scale whose statistical properties could be represented by the height std (s) and the correlation length (1).Although measurements conducted on artificial random surfaces built according to a specific roughness description have shown a good agreement with theoretical results within the range of validity of electromagnetic models, in natural conditions the agreement between experimental measurements and theoretical values could be considered as poor. This fact is essentially due to the inadequate description of natural surfaces who exhibits large spatial variation and in consequence a large variability of the correlation function. In that context, many mathematical work dealing with natural surface description have shown that they are better described as self affined random processes than stationary processes. The fundamental difference between 1 /f processes and other random processes is the very long memory of l/f processes, characterized by very long correlation properties similarly with evolutionary processes which slowly modify the present state according to the past experience.
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