Abstract: Soil moisture represented one of the most fundamental, critical, and indispensable environmental variables in modern agricultural crop production, directly influencing nearly every key stage of crop growth, development, and final maturation. Accurate, real-time monitoring of soil moisture content, precise implementation of scientific on-demand irrigation schedules, and the effective integration of comprehensive water and fertilizer management practices were essential, science-based strategies for significantly enhancing crop yield and improving product quality, conserving precious water resources, and proactively addressing increasingly severe climate-related challenges in agricultural systems. However, conventional soil moisture monitoring methods, such as manual sampling and single-point sensor measurements, often failed to effectively capture the continuous spatiotemporal variations in soil moisture across large agricultural fields. This study aimed to systematically address the significant, often overlooked impact of dynamic vegetation growth and development on soil moisture inversion processes within time-series analyses based on advanced change detection methods. The core principle of the selected change detection method lay in utilizing subtle variations in the radar backscattering coefficient to reliably infer corresponding changes in soil moisture content beneath the surface. This scientific approach relied on a reasonable assumption that the temporal dynamics of surface roughness and vegetation cover occurred over a much longer, more gradual scale compared to those of soil moisture, which fluctuates more frequently in response to irrigation, precipitation, and evaporation. Thus, over long-term continuous observations, the main fluctuations in radar backscattering intensity were primarily attributed to changes in soil moisture content rather than minor alterations in surface roughness or seasonal vegetation variations. In practical agricultural applications, to effectively account for the interfering effects of vegetation, targeted corrections were carefully applied to both the radar backscattering coefficient and the estimated soil moisture values under different surface cover conditions using a flexible piecewise function based on real-time vegetation coverage data. To overcome the inherent limitations associated with single-vegetation-index correction methods, this study innovatively proposed a composite vegetation index, NDEVI (Normalized Difference Enhanced Vegetation Index), derived from the precise fitting of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) data, to significantly enhance the accuracy and reliability of vegetation influence correction in soil moisture inversion processes. Subsequently, abnormal events associated with sudden variations in surface soil roughness were accurately identified using the DBSCAN clustering algorithm, and the long-term time series of soil moisture data was rationally divided into multiple distinct intervals characterized by relatively invariant time series patterns. The experimental results clearly showed that: 1) As vegetation density gradually increased, the maximum and minimum values of both the radar backscattering coefficient and soil moisture content exhibited a corresponding steady upward trend. Consequently, the proposed segmentation function based on fractional vegetation coverage effectively mitigated the adverse interference caused by varying vegetation conditions. 2) The single vegetation index exhibited obvious inherent limitations in capturing complex vegetation dynamics, whereas the composite vegetation index developed through precise fitting effectively mitigated these constraints. Correlation analysis between field-measured soil moisture data and inversion results demonstrated that the proposed NDEVI-based vegetation correction method significantly improved the overall model performance. Specifically, the coefficient of determination (R²) between retrieved and measured 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 applying the NDEVI correction. These results firmly confirmed that the improved change detection model outperformed the original traditional approach. The NDEVI-based correction method not only effectively minimized the negative influence of vegetation dynamics in change detection applications but also significantly surpassed the performance of traditional NDVI-based corrections, thereby yielding more reliable, robust, and accurate soil moisture estimations. 3) Significant variations in surface roughness over time severely compromised the accuracy of soil moisture content estimation using conventional methods. In contrast, the soil moisture inversion conducted over the invariant time segments identified in this study demonstrated markedly improved performance. Specifically, within the invariant time series T3, the coefficient of determination (R²) reached 0.892, and the root mean square error (RMSE) was 2.503%, indicating a substantial, noticeable enhancement in retrieval accuracy compared to conventional long time series approaches. The effective mitigation of vegetation and surface roughness effects was absolutely essential for successfully applying change detection methods in vegetated agricultural areas, as these two factors severely interfere with detection accuracy by masking crucial soil-related signals. This study significantly enhanced the applicability and practical value of change detection techniques by effectively addressing such interferences, meanwhile providing a solid, reliable theoretical foundation for time-series soil moisture inversion in complex agricultural environments. The proposed approach, fully validated to be reliable and effective through field experiments, can supply accurate, real-time soil moisture monitoring solutions for agricultural producers. These solutions are crucial for supporting sustainable agricultural production, enabling rational water resource allocation, implementing precision irrigation technologies, and ultimately promoting efficient, eco-friendly, and sustainable agricultural development worldwide.