Highlights What are the main findings? The nonlinear influence of vegetation on backscatter and soil moisture across varying coverage levels is further complicated by the saturation effect of conventional NDVI in densely vegetated areas. This saturation limits its utility for effective vegetation correction in soil moisture retrieval. With extended soil moisture retrieval time series, surface roughness can no longer be treated as a constant in change detection methods. What are the implications of the main findings? Extreme values of backscatter and soil moisture were calibrated across varying vegetation coverage densities. This study proposes a new vegetation index, the NDEVI, which is optimized to correct for vegetation impacts in densely vegetated regions. The DBSCAN algorithm divided long-time series into invariant periods to ensure temporal consistency of surface parameters while filtering out abnormal events.Highlights What are the main findings? The nonlinear influence of vegetation on backscatter and soil moisture across varying coverage levels is further complicated by the saturation effect of conventional NDVI in densely vegetated areas. This saturation limits its utility for effective vegetation correction in soil moisture retrieval. With extended soil moisture retrieval time series, surface roughness can no longer be treated as a constant in change detection methods. What are the implications of the main findings? Extreme values of backscatter and soil moisture were calibrated across varying vegetation coverage densities. This study proposes a new vegetation index, the NDEVI, which is optimized to correct for vegetation impacts in densely vegetated regions. The DBSCAN algorithm divided long-time series into invariant periods to ensure temporal consistency of surface parameters while filtering out abnormal events.Abstract Although surface soil moisture (SSM) is particularly important in crop yield prediction, irrigation scheduling optimization, and runoff generation mechanisms, accurate monitoring of time-series SSM is still challenging for agricultural and hydrological research. This study presented an improved approach integrating Sentinel-1 C-band SAR and MODIS optical data (2019-2020) to estimate surface soil moisture. To address vegetation effects, we developed a piecewise function using fractional vegetation coverage (FVC) to correct soil moisture and backscatter extrema and established the normalized difference enhanced vegetation index (NDEVI) to characterize backscatter-vegetation relationships across various land covers. Furthermore, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm identified anomalous surface changes, enabling segmentation of long-term series into invariant periods that satisfy the change detection method assumptions. Validation in the Shandian River Basin demonstrated significant improvement over traditional methods, achieving determination coefficients (R2) of 0.844 and root mean square errors (RMSE) of 0.030 m3/m3. The method effectively captured soil moisture dynamics from precipitation and irrigation events, providing reliable monitoring in heterogeneous landscapes. This integrated approach offers a robust technical framework for multi-source remote sensing of soil moisture in semi-arid areas, enhancing capability for agricultural water resource management.