Impact of Time-Varying Soil and Vegetation Parameters on Passive Microwave Soil Moisture Retrieval

Soil moisture is a key variable in land surface water and energy cycles, and passive microwave remote sensing inversion is one of the primary approaches for large-scale soil moisture monitoring. Although physically based models for passive microwave soil moisture retrieval have been well established, the inversion process still faces challenges due to the large number of model parameters, some of which are difficult to obtain. In particular, soil surface roughness and vegetation single-scattering albedo, which characterize soil and vegetation effects, cannot be directly measured. As a result, most existing retrieval methods adopt empirically fixed parameter values, neglecting their temporal variability. In this study, a simulated brightness temperature dataset combined with a probability density approach is used to estimate monthly soil roughness and vegetation single-scattering albedo over the Shandian River Basin based on multi-temporal brightness temperature observations. These time-varying parameters are then incorporated into passive microwave soil moisture retrieval and evaluated against in situ soil moisture measurements and the MCCA soil moisture product. The results indicate that (1) soil roughness and vegetation single-scattering albedo exhibit pronounced intra-annual variability; (2) when the temporal variability of these parameters is considered, the overall accuracy of the retrieved soil moisture is comparable to that of the MCCA product, with good agreement in summer and improved stability in winter, and the temporal variations are more consistent with ground-based observations; and (3) introducing time-varying parameters reduces the intra-annual differences in monthly mean soil moisture, primarily because part of the brightness temperature variability is explained by parameter changes rather than being entirely attributed to soil moisture variations. Overall, incorporating the time-varying characteristics of soil and vegetation parameters enhances the temporal performance of passive microwave soil moisture retrieval, and furnishes new insights for the refinement of associated inversion methods.