Abstract: Soil moisture can represent one of the most indispensable environmental variables during crop production in modern agriculture. Accurate and real-time monitoring of soil moisture content can directly dominate every stage of crop growth, development, and final maturation. Water and fertilizer practices can be effectively integrated to significantly enhance crop yield and product quality. Precious water resources can be conserved to alleviate the increasingly severe climate challenges in agricultural systems. It is often required to precisely implement on-demand irrigation schedules. However, conventional manual sampling and single-point sensor measurements cannot effectively capture the continuous spatiotemporal variations in soil moisture in the large-scale fields. This study aimed to systematically explore the significant, often overlooked impact of dynamic vegetation growth and development on soil moisture inversion within time-series analysis using improved change detection methods. Subtle variations in the radar backscattering coefficient were also selected to reliably infer soil moisture content beneath the surface. The surface roughness and vegetation cover were assumed over a much longer period at a gradual scale, compared with soil moisture, which fluctuated more frequently in response to irrigation, precipitation, and evaporation. Thus, the main fluctuations in the radar backscattering intensity were primarily attributed to soil moisture content rather than minor alterations in surface roughness or seasonal vegetation over long-term continuous observations. In practice, targeted corrections were applied to the radar backscattering coefficient and the soil moisture values under different surface cover conditions using a flexible piecewise function, according to real-time vegetation coverage data. Temporal dynamics were also estimated to effectively account for the interfering effects of vegetation. A composite vegetation index, NDEVI (Normalized Difference Enhanced Vegetation Index) was derived from the precise fitting of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) data. The accuracy and reliability of vegetation influence correction were significantly enhanced in soil moisture inversion, compared with single-vegetation-index correction. Abnormal events with sudden variations in the surface soil roughness were accurately identified using the DBSCAN clustering. The long-term time series of soil moisture data were divided into multiple intervals by invariant time series patterns. The experimental results showed that: 1) The radar backscattering coefficient and soil moisture content exhibited a steady upward trend as vegetation density increased gradually. Consequently, the segmentation function with fractional vegetation coverage effectively mitigated the adverse interference caused by varying vegetation. 2) The composite vegetation index after precise fitting effectively captured complex vegetation dynamics, compared with the single vegetation index. Correlation analysis between field-measured and inversion soil moisture demonstrated that the NDEVI vegetation correction significantly improved the overall performance of the model. Specifically, the coefficient of determination (R²) between measured and retrieved soil moisture values increased from 0.624 to 0.725, while the root mean square error (RMSE) decreased from 4.751% to 4.062% after the NDEVI correction. The improved change detection method outperformed the original conventional approach. The NDEVI correction effectively minimized the negative influence of vegetation dynamics in the change detection applications. Thereby, dependable, robust and accurate soil moisture was estimated after NDVI corrections. 3) The better performance was achieved in the soil moisture inversion over the invariant time segments. In contrast, serious variations in surface roughness over time severely compromised the accuracy of soil moisture content estimation using conventional approaches. Vegetation and surface roughness effects were effectively mitigated with change detection methods in vegetated agricultural areas, as these two factors severely interfered with detection accuracy by masking crucial soil-related signals. The practical value of change detection techniques was significantly enhanced to reduce the interference. Specifically, the retrieval accuracy was substantially enhanced, where the R² reached 0.892 within the invariant time series T3, and the RMSE was 2.503%, compared with conventional long time series approaches. The field experiments were fully validated to accurately monitor the soil moisture in real time. Meanwhile, the findings can provide a solid and reliable theoretical reference for time-series soil moisture inversion in complex agricultural environments, particularly for the water resource allocation and precision irrigation in efficient, eco-friendly and sustainable agriculture worldwide.