To monitor and improve the geolocation accuracy of domestic Synthetic Aperture Radar (SAR) payloads, this paper identifies wind turbine locations across three terrain types using high-resolution remote sensing imagery and deep learning models. Leveraging the strong scattering characteristics of wind turbines in SAR imagery, it constructs a large-scale ground control point (GCP) database for long-term and wide-area geometric processing of SAR images. The results show that Gaofen-3 Fine Strip II (FSII) mode imagery exhibits periodic fluctuations in geolocation accuracy from 2017 to 2020, with a cycle of approximately 747.99 days, during which the accuracy gradually degrades from its peak. It also analyzes the influence of terrain on geometric positioning, revealing that mountainous areas experience the lowest positioning accuracy. Using the constructed GCP database, the average geolocation error of Gaofen-3 FSII imagery is reduced from 44.64 meters to 7.94 meters across various terrains, achieving consistent accuracy across all three terrain types.