Kunming city is located in the middle of Yunnan Province. Due to large-scale groundwater exploitation and urban development in recent years, this area has been affected by surface subsidence. In this paper, Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) data are used to monitor the surface subsidence in Kunming city area for better analysis and understanding. The study used data of Sentinel-1A from 2018 to 2020 with atmospheric correction based on GACOS to calculate the average annual subsidence rate in Kunming city area, and the results show that the maximum subsidence rate is 48 mm/year. The subsidence obtained by InSAR is compared with the vertical deformation information obtained by eight GNSS stations in continuous operation in the study area. The subsidence rate trend show by the two methods is consistent, which further verifies the validity of InSAR data to reflect the local deformation. Experimental results shown that the eastern and northeastern Dianchi lake areas were affected by underground resources mining, and the induced surface subsidence characteristics were obvious, with the surface subsidence rate reachde 48 mm/year and 37 mm/year respectively. The Kunyang Phosphate Mine also had different degrees of mining subsidence disaster, with the maximum subsidence rate reached 36 mm/year. The subsidence rate of InSAR and GNSS has the same trend on the whole. However, GNSS sites are generally located in stable areas, the settlement amount obtained in the same time period is somewhat different from that of InSAR.

Time-series interferometric synthetic aperture radar (TS-InSAR) is often affected by tropospheric artifacts caused by temporal and spatial variability in the atmospheric refractive index. Conventional temporal and spatial filtering cannot effectively distinguish topography-related stratified delays, leading to biased estimates of the deformation phases. Here, we propose a TS-InSAR atmospheric delay correction method based on ERA-5; the robustness and accuracy of ERA-5 data under the influence of different atmospheric delays were explored. Notably, (1) wet delay was the main factor affecting tropospheric delay within the interferogram; the higher spatial and temporal resolution of ERA-5 can capture the wet delay signal better than MERRA-2. (2) The proposed method can mitigate the atmospheric delay component in the interferogram; the average standard deviation (STD) reduction for the Radarsat-2 and Sentinel-1A interferograms were 19.68 and 14.75%, respectively. (3) Compared to the empirical linear model, the correlation between the stratified delays estimated by the two methods reached 0.73. We applied this method for the first time to a ground subsidence study in the Yuxi Basin and successfully detected three subsidence centers. We analyzed and discussed ground deformation causes based on rainfall and fault zones. Finally, we verified the accuracy of the proposed method by using leveling monitoring data.