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Yanshan city in Jiangxi Province was selected as the study area to address the challenges of large-scale cropland, complex terrain, and fragmented land parcels. Twenty scenes of Sentinel-1A imagery collected between 26 October 2022 and 24 May 2024 were utilized for ground subsidence analysis using SBAS-InSAR techniques. Sentinel-2 imagery was used for supervised classification, achieving an overall accuracy of 81.43% and a Kappa coefficient of 0.7833. Cropland exhibiting significant subsidence was identified through overlay analysis to inform field verification. To enable unified management and analysis of multi-source data, a conceptual E\u2013R model was implemented in PostgreSQL\/PostGIS and transformed into an object-relational spatial database, integrating unmanned aerial vehicle imagery, light detection and ranging point clouds, Sentinel-1 stacks, and obstacle factors. 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