We coupled dielectric mixing models with a full-wave ground-penetrating-radar (GPR) model to estimate the soil water content by inversion. Two mixing models were taken into account in this study, namely, a power law model and the Wang and Schmugge model. With this combination, we could account for the frequency dependence of the dielectric permittivity and apparent conductivity in the inverse algorithm and directly estimate the soil water content without using an empirical petrophysical formula or a priori knowledge on soil porosity. The approach was validated by a series of experiments with sandy soil in controlled laboratory conditions. The results showed that the performance of our approach is better than the common approach, which assumes a linear dependence of apparent conductivity on frequency and uses Topp’s equation to transform permittivity to water content. GPR data were perfectly reproduced in the time and frequency domains, leading to very accurate water-content estimates with an average absolute error of less than [Formula: see text]. However, the accuracy was reduced as the water content increased. Sensitivity analysis indicated that the Green’s function was most sensitive to the water content and sand-layer thickness but much less so with DC conductivity. The results also revealed that as the frequency increased, although the permittivity was nearly constant, the apparent electrical conductivity and the attenuation increased remarkably, especially for wet sands due to dielectric losses. The successful validation of the proposed approach opens a promising avenue of development to use dielectric mixing models for soil-moisture mapping from GPR measurements.
Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework
