Physically Based Estimation Soil Moisture from L-band Radiometer

Soil moisture, as the fundamental parameters for land surface water resource formation, it plays an important role in climate change. The goal of the Soil Moisture and Ocean Salinity mission (SMOS) over land is to infer surface soil moisture from L-band, dual-polarization radiometric measurements under a range of viewing angles. Previous research has shown that L-band passive microwave remote sensing sensors can be better used to monitor soil moisture over land surface. However, the effects of soil surface roughness play a significant role in the microwave emission from the surface. Therefore, a good parameterization of the effects is a prerequisite for retrieving surface soil moisture information. There are two types of approaches-the physical modeling and semi-empirical approaches that are commonly used in modeling the surface emission. The model parameters used in semi-empirical approaches are often derived from limited field observations and always need to be evaluated when applying to other datasets or application purposes. In recent theoretical model developments, advanced integral equation model (AIEM) has demonstrated a much wider application range for surface roughness conditions than that from conventional models. In this study, we generate a simulated database with a wide range of the surface roughness and soil moisture conditions under SMOS sensor configurations using AIEM model. A simple and accurate surface emission model is developed based on the simulated database that can be easily used as forward model in the passive microwave remote sensing applications. An inversion procedure is set up in terms of dual-polarization microwave brightness temperatures available from the forward model to retrieve soil moisture with a minimum of auxiliary information about the ground. The inversion technique is validated with microwave radiometer experimental data at Beltsville, MD. The results reveal that the use of dual-polarization and multi-angular inversion technique to estimate soil moisture from radiometric measurements decrease the perturbing effects of surface roughness on the soil moisture estimation.