Empirical Validation of Soil Temperature Sensing Depth Derived From the Tau-z Model Utilizing Data From the Soil Moisture Experiment in the Luan River (SMELR)

Soil moisture is a critical variable in climate forecasting, hydrology, and others. Satellite-based remote sensing techniques have been used to map soil moisture globally, including visual bands, infrared, Synthetic Aperture Radar (SAR), passive microwave remote sensing, etc. Passive microwave remote sensing techniques, especially at the L-band, such as the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), have higher accuracy than the others due to their higher sensitivity to the dielectric constant of the soil profile. However, the unclear sensing depth at L-band for SMOS and SMAP leads to a mismatch in the calibration/validation and application of their soil moisture products. In this study, we apply soil temperature sensing depth model, i.e., the tau-z model, to the soil temperature, soil moisture, and brightness temperature (TB) data collected during the Soil Moisture Experiment in the Luan River (SMELR). The effectiveness of the tau-z model in interpreting the L-band microwave observations is validated through forward simulations with the Community Microwave Emission Modelling (CMEM). Results showed that: 1) the bias in TB simulation can be reduced from 26.22 K/12.00 K to -11.66 K/-8.839 K for H/V-polarization; 2) the RMSE is reduced from 30.2 K/20.48 K to 12.92 K/11.66 K for H/V-polarization by considering the microwave sensing depth. Daily TB signal variation partly attributed to the soil temperature sensing depth due to different bands' penetration capacity. The result is expected to improve the understanding of microwave data collected by muli-frequency synthetic platforms such as Copernicus Microwave Imaging Radiometer (CIMR).