Ground deformation caused by excessive groundwater extraction has become a major environmental concern in agricultural regions worldwide. Interferometric Synthetic Aperture Radar (InSAR) enables large-scale monitoring of ground deformation. However, its performance often decreases in low-coherence areas affected by vegetation growth and irrigation. In this study, we conducted a comparative evaluation of three multi-temporal SBAS-InSAR processing frameworks, MintPy, LiCSBAS, and SARvey, to assess their consistency in monitoring ground deformation across Golestan Province, Iran, using Sentinel-1 data acquired between 2014 and 2024. The analysis included deformation velocity fields, cross-sectional profiles, and time-series displacements, which were compared with temperature and precipitation variations. All three frameworks identified a pronounced deformation zone in the Gorgan Plain, with maximum line-of-sight deformation rates up to 13 cm/year. Quantitative comparisons showed strong correlations among the frameworks (r = 0.80 to 0.89), confirming their mutual reliability even under low coherence conditions. The time-series analysis revealed clear seasonal deformation patterns, with summer subsidence and winter uplift closely related to hydroclimatic fluctuations. Overall, this study demonstrates that multi-temporal SBAS-InSAR approaches can provide consistent and physically meaningful deformation estimates in challenging agricultural environments, offering valuable insights for subsidence monitoring and water resource management.