Abstract: The ecological environment problems in coal mining areas have been paid more and more attention by the society. How to realize the accurate, efficient and economical early identification and dynamic monitoring of coal mining subsidence areas is particularly urgent. Based on the data collection and analysis, this paper uses the DEM data of 5 periods from 2000 to 2022 for differential decomposition calculation, and uses 164 long-term series Sentinel-1 data from June 15,2015 to July 15,2023 to dynamically monitor the land subsidence in the coal mining area. The current distribution and land subsidence characteristics of the coal mining subsidence area in Daliuta town, Shenmu city are identified, and a set of early identification methods of coal mining subsidence area based on multi-data sources is formed. The results show that:①The distribution area of coal mining subsidence area in Daliuta town is 252.70km², including two types of ground collapse and goaf suspended roof. The coal mining subsidence problems in Shigetai, Halagou, Daliuta and Huojitu mining areas of Shenhua company are serious.②The DEM data with a resolution of 2m is resampled to 5m and then the difference operation is performed. The error is 0.01m, and the accuracy is high and the calculation is efficient. ③DEM difference decomposition and SBAS-InSAR technology can accurately identify the range of ground collapse with high matching degree, and each method complements and confirms each other.

Key words: DEM difference solution, SBAS-InSAR technology, coal mining subsidence area, Daliuta town