An empirical calibration of the integral equation model based on SAR data and soil parameters measurements

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 paper 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, 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 (Fung, 1994) with a coupling of the empirical function of correlation length, shows a very good agreement with the backscattering measurements provided by spaceborne SAR systems.