ABSTRACT
The objective of this study is to improve the performance of semi-empirical radar
backscatter models, which are mainly used in microwave remote sensing (Oh 1992,
Oh 2004 and Dubois). The study is based on satellite and ground data collected on
bare soil surfaces during the Multispectral Crop Monitoring experimental campaign
of the CESBIO laboratory in 2010 over an agricultural region in southwestern France.
The dataset covers a wide range of soil (viewing top soil moisture, surface roughness
and texture) and satellite (at different frequencies: X-, C- and L-bands, and different
incidence angles: 24.3° to 53.3°) configurations. The proposed methodology consists
in identifying and correcting the residues of the models, depending on the surface
properties (roughness, moisture, texture) and/or sensor characteristics (frequency,
incidence angle). Finally, one model has been retained for each frequency domain.
Results show that the enhancements of the models significantly increase the simulation
performances. The coefficient of correlation increases of 23% in mean and the
simulation errors (RMSE) are reduced to below 2 dB (at the X and C-bands) and to 1
dB at the L-band, compared to the initial models. At the X- and C-bands, the best
performances of the modified models are provided by Dubois, whereas Oh 2004 is
more suitable for the L-band (r is equal to 0.69, 0.65 and 0.85). Moreover, the modified
models of Oh 1992 and 2004 and Dubois, developed in this study, offer a wider
domain of validity than the initial formalism and increase the capabilities of retrieving
the backscattering signal in view of applications of such approaches to stronglycontrasted
agricultural surface states.