Optimization of Random Surface Scattering Models for RR Polarization in SoOp-R/GNSS-R Applications

Polarization in global navigation satellite system-reflectometry (GNSS-R) or signal of opportunity-reflectometry (SoOP-R) is commonly used for retrieving geophysical parameters. However, the attention toward other polarizations of reflected signals has increased with developments in this field. The widely used equation for RR polarization suggests that it decreases as soil moisture content increases, which contradicts the experimental data. The accurate forward calculation of RR polarization is essential for the subsequent retrieval algorithm in polarization GNSS-R/ SoOP-R. To address this issue, three new models have been developed: specular reflectivity model for polarization GNSS-R (Spec4PolR), small perturbation model for polarization GNSS-R (SPM4Pol), and Umich model for polarization GNSS-R (Umich4PolR). The Mueller matrix of these three models has been presented, and the wave synthesis technique has been employed to calculate the reflectivity at RR polarization. Spec4polR uses only three elements in the Mueller matrix for final reflectivity, while five elements are used in Umich4polR. In SPM4Pol, all elements construct the Mueller matrix, and only nine elements are employed for calculation. The effects of each element on soil moisture content are presented, and the final reflectivity at RR polarization is illustrated. However, due to the simple formulation of Spec4Pol, its reflectivity at RR polarization still decreases as soil moisture content increases. On the other hand, the results of SPM4Pol and Umich4Pol are consistent with the measured data, and the reflectivity at RR polarization increases as soil moisture content increases. The formula developed in this article for calculating RR polarization will contribute to subsequent polarization studies and geophysical parameter retrieval based on RR polarization.