Optimizing the Radiative Transfer Model Using Deep Neural Networks for NISAR Soil Moisture Retrieval - Xiaodong Huang; Lorenzo Giuliano Papale; Marco Lavalle; Fabio Del Frate; Heresh Fattahi; Steven K. Chan; Rowena B. Lohman; Xiaolan Xu; Yunjin Kim (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Soil moisture retrievals based on rigorous physical backscattering models require a comprehensive description of the vegetation structure and biophysical parameters, including the density of the scatterers, height, and vegetation water content. Semiphysical models, such as the water cloud model, are also extensively used and rely on estimates of vegetation water content or biomass derived from optical vegetation indices, such as LAI and NDVI. However, such indices only contain parts of the true variability of vegetation structure and how it changes across various land-cover types. In this study, we introduce radiative transfer neural network (RTNet), which combines a parameterized first-order radiative transfer model with four scattering components (surface, volume, double-bounce, and triple-bounce scattering components) and deep residual neural networks for the soil moisture retrieval. The input features consist of the HV backscattering coefficient, vegetation water content, and several other information categories, such as soil texture and weather data. The RTNet is optimized to minimize the difference between the estimated and measured HH total backscattering. After imposing a physical constraint on the RTNet outputs, they are then applied to the ensemble random forest machine learning regressor to retrieve the volumetric soil moisture. The proposed framework is validated using the SMAPVEX12 L-band UAVSAR data, aggregated to a resolution of 100 m, which is finer than the NISAR level 3 soil moisture product (200-m resolution). The estimated HH total backscattering coefficients show a high agreement with the UAVSAR-measured HH backscattering with a root-mean-square error (RMSE) of approximately 3 dB across the entire image in nonforested regions. The retrieved volumetric soil moisture also shows a very high agreement with the in situ soil moisture, achieving the RMSE of 5.65% and R2 of 0.7.