Soil moisture is a critical state variable in land–atmosphere interactions and the hydrological cycle. Owing to its all-weather and all-day observation capability, passive microwave remote sensing has become an important technique for regional soil moisture monitoring. However, most existing passive microwave soil moisture retrieval methods rely on fixed empirical parameters to characterize vegetation single-scattering albedo and soil surface roughness, which may not fully account for variations in surface conditions across different regions and seasons, thereby affecting retrieval accuracy. To address this issue, this study proposes a method for jointly constraining key parameters of the forward model for passive microwave soil moisture retrieval using multi-temporal brightness temperature observations. The vegetation single-scattering albedo ($\omega$) and soil surface roughness parameter ($rou$) are determined from multi-temporal brightness temperature information, and soil moisture is subsequently retrieved based on the optimized parameters. The Shandian River Basin was selected as the study area, and soil moisture retrievals were conducted using SMAP SPL3SMP brightness temperature data from 2019 to 2024. The retrieval results were evaluated using ground-based observations and compared with existing soil moisture products. The results show that: (1) The proposed parameterization method is theoretically capable of identifying the vegetation single-scattering albedo and soil surface roughness parameter across their respective parameter ranges. Based on the parameterization results in the study area, the vegetation single-scattering albedo exhibits a pronounced and relatively consistent annual pattern, with a temporal trend generally consistent with previous studies, whereas the intra-annual variation in the soil surface roughness parameter is not pronounced and cannot be reliably identified from the current results. (2) Validation against the ground-based soil moisture observation network shows that the proposed method achieves an overall RMSE, ubRMSE, MRE, and Bias of 0.0678, 0.0505, 0.3085, and −0.0453, respectively, and generally outperforms the DCA and SCA products. Compared with MCCA, the proposed method has a slightly higher ubRMSE (0.0505 vs. 0.0496) but a Bias closer to zero (−0.0453 vs. −0.0504). Consequently, its overall RMSE is lower than that of MCCA (0.0678 vs. 0.0707). These results suggest that the proposed method has the potential to reduce systematic errors while maintaining a level of random error comparable to that of existing products. (3) Spatial analysis demonstrates that the retrieved soil moisture patterns are consistent with the general spatial distribution characteristics of the study area. Compared with the MCCA, DCA, SCA-H, and SCA-V products, the proposed method exhibits stronger spatial gradients and provides clearer differentiation among regions with different moisture conditions. Overall, the proposed multi-temporal brightness temperature constraint method demonstrates good feasibility for passive microwave soil moisture retrieval and provides a new technical approach for determining key parameters in the forward model.

The accurate monitoring of water vapor is essential for understanding the hydrological cycle and improving weather forecasting. Although the Moderate-resolution Imaging Spectroradiometer (MODIS) provides spatially continuous precipitable water vapor (PWV), validation in Hunan Province reveals a systematic underestimation, with correlations to radiosonde (RS-PWV) around 0.40 and average RMSE and MAE reaching 23.80 and 18.04 mm. To address this issue, high-accuracy PWV derived from the BeiDou Navigation Satellite System (BDS-PWV), which show high consistency with RS-PWV, were incorporated. A random forest daily-scale water vapor fusion model was developed based on the differential characteristics of dry and wet season residuals. By employing day of year (DOY), latitude, longitude, and elevation as auxiliary factors, the model establishes a seasonal fusion framework that dynamically transitions between dry and wet seasons. Validation shows that the fusion PWV aligns closely with RS-PWV, reducing average RMSE and MAE to 4.71 and 3.81 mm, corresponding to improvements of 80.21% and 78.88% over MODIS, with accuracy increases exceeding 75% at all stations. The fusion model effectively mitigates MODIS’s underestimation and weather sensitivity, producing high-accuracy, spatially continuous daily PWV fields and offering strong potential for improving precipitation and weather forecasting in complex regions such as Hunan Province.