Integration of Strong Fluctuation Theory With Rough Soil Models for Improving Snow Backscattering Simulations

In this work, we proposed a reappraisal of strong fluctuation theory (SFT) aimed at improving its capability in simulating snow backscattering. As conceived in the original form, SFT considers the air-snow and snow-soil interfaces as flat surfaces. This study aims at accounting for the roughness of the snow-soil interface by coupling SFT with suitable rough soil surface models, namely, Oh model and integral equation model (IEM), with the aim of increasing accuracy and reliability of SFT simulations. The accuracy of the reappraised SFT was evaluated in comparison with SAR data collected by Sentinel-1 (S-1) and COSMO-SkyMed (CSK) over two test areas located in the western part of Italian Alps, for which in situ measurements of the main soil and snow parameters were available. The reappraised SFT was able to significantly improve the simulation accuracy at both frequencies with respect to the original implementation. In the better case, we obtained <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2} =0.57$ </tex-math></inline-formula>, RMSE =2.9 dB, and bias <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= -0.17$ </tex-math></inline-formula> dB. A comparison with dense media radiative transfer (DMRT) theory in single-layer configuration demonstrated that the reappraised SFT can obtain comparable results with a significantly smaller computational cost.