An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils
The retrieval of surface parameters demands the use of well calibrated models and unfortunately, none of the existing models provide consistently good agreement with the measured data. The overall objective of this article is to propose a semi-empirical calibration of the Integral Equation Model (IEM) so as to better reproduce the backscattering coefficient measured from SAR images over bare soils. As correlation length is not only the least accurate parameter but also the most difficult to measure, we propose its empirical estimation from an experimental data set of SAR images and soil parameter measurements. Based on a first data set, correlation length behaviour was studied in terms of αrms (where α is the wave number and rms the standard deviation of surface height) and radar configuration (polarization and incidence angle). Exponential relationships between optimal correlation length and rms height were found for each radar configuration. The IEM was then tested on another set of measured data in order to validate the calibration procedure. The new calibrated version of the IEM, corresponding to the original IEM with a coupling of the empirical function of correlation length, shows a very good agreement with the backscattering measurements provided by space-borne SAR systems. This adapted version of the IEM can be used in inversion techniques for retrieving rms height and/or soil moisture from radar observations.