As an important indicator of permafrost degradation, surface deformation is often used to monitor the thawing and freezing process in the permafrost active layer. However, due to the large area of the continuous permafrost of the Qinghai–Tibet Plateau (QTP) and the large amount of data processed by conventional time-series InSAR, previous studies have mostly focused on local area investigations, and regional characteristics of surface deformation of the continuous permafrost area on the QTP are still unclear. In this paper, we characterized surface deformation in space and time over the main continuous permafrost area on the QTP, by analyzing 11 ascending and 8 descending orbits of Sentinel-1 SAR data acquired between 2018 and 2021 with the time-series InSAR processing system LiCSAR. The reliability of the InSAR deformation results was verified by a combination of leveling measurement data, the intercomparison of overlapping area results, and field verification. The results show that the permafrost regions of the central QTP exhibited the most significant linear subsidence trend. The subsidence trend of permafrost on the QTP was mainly related to the thermal stability of permafrost, and the regions with larger subsidence rates were concentrated in sub-stable, transitional and unstable permafrost areas. We also found that, according to analysis of time-series displacement, the beginning and ending times of permafrost thawing were highly spatially heterogeneous, with the time of maximum thawing depth varying between mid-October and mid-November, which was probably attributed to the active layer thickness (ALT), water content in the active layer, and vegetation cover in these regions. This study is of great significance for understanding the changing trend of permafrost on the QTP under the background of climate change. In addition, this study also demonstrates that combination of Sentinel-1 SAR images with the LiCSAR system has significant potential for detecting permafrost deformation with high accuracy and high efficiency at regional and global scales.

Several studies have evaluated the accuracy of ice thickness model inversions, but the effects of surface velocity and topographic factors have not been fully studied. Because of the important potential of models for estimating regional and global ice thickness, the influence of the characteristics of the glaciers themselves on the models is unknown. Therefore, we estimated the thickness of 10 glaciers in HMA based on remote sensing data using the GlabTop2, GV22, and VOLTA models, and we analyzed the factors affecting accuracy. The first two models are representative of the widely used shallow ice approximation and laminar flow theory, and the latter is a new model based on shallow ice approximation. The results show that the RMSEs for GlabTop2, VOLTA, and GV22 are 36.6 m, 56.8 m, and 63.2 m, respectively. The accuracy of the three models differed obviously under the influence of different slopes, aspects, and surface velocities. In contrast to glaciers with higher surface velocities, GV22 is more suitable for glaciers with smaller surface velocities, and the accuracy of the model is significantly reduced when surface velocities are greater than 50 m/yr. Aspect has an effect on the accuracy of the GV22 ice thickness inversion but not on the other models. Ice thicknesses estimated using the three models is more reliable at surface slopes of 16–24°. These three models do not apply to gentle glaciers such as icecaps. Our study provides an important reference for choosing a suitable model to invert the glacier thickness in HMA.