Abstract
With the continuous reduction of cultivated land in China, this study aims to develop a comprehensive monitoring system to support sustainable cropland management in complex terrains. 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-R 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. The system provides user management, point cloud-to-3D-Tiles conversion, and high-precision Global Navigation Satellite System support. Preliminary tests confirmed the framework's reliability and operational feasibility. The novelty lies in integrating multi-source remote sensing, geospatial analysis, and spatial database technology into a scalable, parcel-centered framework for complex agricultural landscapes, supporting cultivated land protection and land-use-change monitoring.