To address the risks of surface subsidence induced by coal mining activities, this study develops an enhanced PS/DS-InSAR deformation monitoring approach that incorporates land cover type information to improve the adaptability of measurement point selection in complex surface environments and to ensure spatial completeness of deformation mapping. Taking the Dashuitou mining area as a case study, the method uses Sentinel-1 SAR imagery in combination with optical remote sensing–derived land cover classification to assign adaptive coherence thresholds for different surface types, optimizing the identification of distributed scatterers and integrating them with persistent scatterers to construct an interferometric network. This enables multi-temporal deformation time series inversion and subsidence rate mapping. Results demonstrate that the proposed approach effectively increases measurement point density and improves the spatial continuity of the deformation field in subsidence funnels and high-gradient deformation zones, avoiding the data gaps typically found in low-coherence areas with conventional methods. By analyzing the spatial distribution of surface deformation over the monitoring period, the study identifies subsidence centers, boundary transition features, and deformation evolution patterns, providing technical support for detailed monitoring and mitigation of surface subsidence processes in coal mining areas.