Title :
Evaluation of Radar Backscattering Models IEM, Oh, and Dubois for SAR Data in X-Band Over Bare Soils
Author :
Baghdadi, Nicolas ; Saba, Elie ; Aubert, Maelle ; Zribi, Mehrez ; Baup, Frederic
Author_Institution :
Unite Mixte de Rech. Territoires, Environ., Teledetection et Inf. Spatiale, Agric. & Environ. Eng. Res. (CEMAGREF), Montpellier, France
Abstract :
The objective of this letter is to evaluate the surface radar backscattering models, namely, integral equation model (IEM), Oh, and Dubois, for synthetic aperture radar data in X-band over bare soils. This analysis uses a large database of TerraSAR-X images and in situ measurements (soil moisture “mv” and surface roughness “ h_rms”). Oh´s model correctly simulates the radar signal for HH and VV polarizations, whereas the simulations performed with the Dubois model show a poor correlation between TerraSAR-X data and model. The backscattering IEM simulates correctly the backscattering coefficient only for h_rms <; 1.5 cm in using an exponential correlation function and for h_rms >; 1.5 cm in using Gaussian function. However, the results are not satisfactory for the use of IEM in the inversion of TerraSAR-X data. A semiempirical calibration of IEM was done in X-band. Good agreement was found between the TerraSAR-X data and the simulations using the calibrated version of the IEM.
Keywords :
backscatter; electromagnetic wave polarisation; electromagnetic wave scattering; moisture; remote sensing by radar; soil; synthetic aperture radar; Dubois radar backscattering model; Gaussian function; HH radar polarization; IEM radar backscattering model; Oh radar backscattering model; TerraSAR-X images; VV radar polarization; X-band SAR data; bare soil; integral equation model; radar backscattering model evaluation; semiempirical calibration; soil moisture; surface radar backscattering models; surface roughness; synthetic aperture radar data; Backscatter; Correlation; Data models; Radar; Rough surfaces; Soil; Surface roughness; Dubois model; Oh model; TerraSAR-X images; integral equation model (IEM);
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2011.2158982