## 6 Conclusions  
In this study, based on 64 Sentinel-1A SAR images, we conducted surface deformation monitoring of the Wanli mining area from 2018 to 2023 using the SBAS-InSAR technique. Key results include deformation velocity, relative subsidence, and time series of surface deformation. Significant findings are as follows:  
1. The total area of subsidence during the monitoring period is 109.73 km\({}^{2}\). Within these subsidence areas, the maximum cumulative subsidence is \(-283.41\) mm, with the maximum surface subsidence velocity reaching \(-46.45\) mm/y. Zone C7 is identified as the most severely affected area, with an average subsidence velocity of \(-11.08\) mm/yr.
2. Twenty-nine ground fractures were identified, and they were concentrated along the borders of the monitored subsidence areas, where the surface slope is relatively steeper. Statistical analysis further revealed that the orientation of most ground fractures aligns with the tangential trend of the subsidence basin margin where they occur. These findings support the reliability of the SBAS-InSAR monitoring results.  
Moreover, the successful outcomes of this application can serve as a valuable reference for similar mining areas with complex topography.
3. The SBAS-InSAR monitoring results, including the distribution of subsidence zones, spatiotemporal evolution patterns, and subsidence trends, provide robust data to support policymaking for subsequent geological hazard mitigation and ecological conservation efforts in mining areas. Meanwhile, this study serves as a valuable reference for investigating surface subsidence patterns induced by coal mining across diverse geological conditions. However, due to inherent systematic errors and decorrelation effects, accurately quantifying subsidence rates in the central subsidence zones remains challenging, with potential measurement uncertainties. To address this limitation, future studies should combine traditional subsidence monitoring methods to implement targeted, high-precision observations in critical areas. This combined approach will not only ensure the acquisition of high-accuracy data meeting project requirements but also optimize cost efficiency in monitoring operations.  
The original Draft was written by [PERSON], who also performed the methodology and formal analysis. The conceptualization of this study was conducted by [PERSON], who also reviewed and edited the manuscript. [PERSON] and [PERSON] performed the data curation. Validation and visualization were performed by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of Inner Mongolia Autonomous Region (Grant No. 2024 MS04025), the Fundamental Research Funds for the Central Universities in Inner Mongolia (Grant No. ZTY2024078), the National Natural Science Foundation of China (Grant No. 52264009), and the Doctoral Research Foundation of Inner Mongolia University of Technology (Grant No. BS2020024).  
The data that support the findings of this study are openly available and found in the ASF and GEE.  
The authors greatly appreciated the following data supports: The DEM data were supported by the EARTHDATA network station ([[http://search.asf.alaska.edu](http://search.asf.alaska.edu)]([http://search.asf.alaska.edu](http://search.asf.alaska.edu)), accessed on 15 August 2024). The Landsat data can be downloaded from the GEE ([[https://developers.google.com/earth-engine/datasets/catalog/landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat)]([https://developers.google.com/earth-engine/datasets/catalog/landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat)), accessed on 20 August 2024). The Sentinel-1A data and POD data were collected from the ASF ([[https://search.asf.alaska.edu](https://search.asf.alaska.edu)]([https://search.asf.alaska.edu](https://search.asf.alaska.edu)), accessed on 21 August 2024).  
The authors declare no conflicts of interest.