Evaluation of Various Radar Backscattering Models Using C-Band and L-band SAR Data for Soil Moisture Estimation
Khabazan, Saeed1; Motagh, Mahdi2

Soil moisture and surface roughness play an important role in several applications such as hydrology, agriculture, risk prediction, climate studies. Soil moisture exhibits very large spatial and temporal variability. Therefore, the ability of measuring soil moisture content on a large scale from space is very important. The fundamental basis of microwave remote sensing for soil moisture estimation is the contrast in dielectric properties of water and dry soil, and the relationship between the Fresnel reflection coefficient and dielectric constant.
The objective of this research is to evaluate capability of various radar backscattering models (Oh, Dubois, and Water Cloud Model (WCM)) for estimation of soil moisture in three different vegetation canopy covers and various soil depths. We test the models using different amount of NDVIs (i.e., NDVI<0.2, 0.20.4) and three different amounts of soil depths (i.e., 0 We used seven Airborne Synthetic Aperture Radar (AIRSAR) images and one Landsat image acquired in 10 June 2003 from the Little Washita Watershed simultaneously. The data acquired are in both C and L frequencies with HH, HV, and VV polarizations. The ground data used in this paper is the SMEX03 data set from The Little Washita Watershed that was gathered at the time images acquisitions. The results show that the models frequently tend to over-estimate the radar response. The errors indicate that the models tested are dependent on the root mean square (RMS) surface height, the soil moisture, vegetation covers, soil depths and the radar incident. Detailed results and diagrams will be expressed in the full paper.