Effect of surface soil moisture gradients on modelling radar backscattering from bare fields

In agriculture, soil moisture gradients near the surface are important. The objective of this study was to determine their impact on the estimation of the radar backscatter when using surface diffusion models. Two surface radar backscatter models were evaluated in a bare soil with soil moisture stratification induced by irrigation and evaporation. The models were the semi-empirical model (OM) and the Integral Equation Model (IEM). These models were coupled with a penetration depth model. Three approaches were used to calculate the reflectivity at the air-soil interface required by the models. The first one was based on the incoherent reflectivity contribution of each stratum. The second approach calculated the Fresnel reflectivity based on the mean complex dielectric constant over a fixed depth, and the third one computed the Fresnel reflectivity using the mean complex dielectric over the penetration depth of the incident signal. The estimations from the OM model were highly correlated with the observations for all combinations of incidence angles, frequencies and polarizations. However, the backscatter was systematically underestimated and bias increased with wavelength. The IEM provided the most accurate estimate but was sensitive to radar configuration and roughness. The two Fresnel reflectivity based approach tended to overestimate the backscatter in the drying sequence after intensive irrigation following held work. The incoherent reflectivity on the other end underestimated the backscatter in freshly dry disturbed soil. The sampling depth had to be chosen carefully when estimating the backscatter from the second approach, especially in K-u and L-bands.