Ground-Based Microwave Radiometry Experiment for the Calibration of Emission Models and Retrieval of Soil Moisture

Passive microwave remote sensing has made significant strides in the estimation of soil moisture; however, several challenges persist. A ground-based radiometry experiment conducted in an agricultural setting sought to enhance microwave emission models and retrieval algorithms for soil moisture and vegetation water content. The multi-frequency, dual-polarized brightness temperature (TB) data demonstrated a strong correlation with surface soil moisture. Importantly, surface roughness and vegetation water content were found to significantly influence the relationship between brightness temperature and soil moisture. Calibration of the τ-ω model led to improved performance. By utilizing the experimental data alongside the calibrated model, we evaluated the effectiveness of single-channel (SCA), dual-channel (DCA), and multi-channel (MCA) algorithms for estimating soil moisture and vegetation water content within single-objective estimation (SOE) and dual-objective estimation (DOE) frameworks. The findings revealed that SOE outperformed DOE, with MCA-SOE achieving the highest level of accuracy. Overall, this study lays the groundwork for the further development of passive microwave remote sensing methodologies aimed at estimating soil moisture and vegetation water content.