## 4 Results  
### Ground Deformation  
Ascending and descending data sets of Sentinel-1 SLCs were processed using the SBAS-InSAR technique. As the images have short spatial and temporal baselines, the SLCs exhibited good interferometric performance. In the determination of the subsets, images covering a period of 120 days with a spatial baseline less than 250 m were used to generate interferogram pairs for ascending orbit SLCs. The minimum temporal and spatial baselines for the SLCs in descending orbit were 150 days and 240 m, respectively. Meanwhile, to eliminate decorrelated pairs and ensure the absence of isolated clusters of images [54; 55; 56], a coherent threshold of 0.4 was set through a series of trial-and-error analyses. After the abovementioned selection, 457 and 487 pairs of SLCs were obtained for the ascending and descending orbit data sets, respectively, and the baseline connections are shown in Figure S2. After the SBAS-InSAR analysis, the ground deformation velocities were obtained with respect to a reference point, where ground deformation is almost zero. The spatial distributions of the annual ground deformation velocities in LOS from the ascending and descending data sets are shown in Figures 6 and 7, respectively, and the reference points are marked as the red star. The ground deformation velocities have positive and negative values, which correspond to the ground moving towards and away from the satellite, respectively. For the ascending data set, a total of 21,613 MPs were generated with an average density of 45.6 MPs/km\({}^{2}\). For the descending data set, 19,507 MPs were obtained with an average density of 42.1 MPs/km\({}^{2}\). The density of MPs in alpine mountains is lower than that in valley areas, which may be attributed to the complicated terrain conditions and the dense vegetation coverage in alpine regions. The deformation velocities fall in the ranges of [\(-\)84.4, 21.9] mm/year and [\(-\)41.0, 17.8] mm/year in the ascending and descending data sets, respectively; they were mainly concentrated at \(\pm\)5 mm/year for both data sets (yellow dots in Figures 6 and 7). The low deformation velocities indicate that most of the study area was relatively stable during the monitoring stage. The two data sets showing different extreme values might be attributed to two factors: (1) the two data sets have different geometric relationships with the study area and (2) the descending data set captures the minimum deformation velocity (\(-\)84.4 mm/year), whereas the ascending data set misses it due to the data coverage (area around TS1 in Figure 6). Focusing on areas with high deformation velocity (red and green clusters in Figures 6 and 7), they are characterized by strong spatial agglomeration and uneven distribution and mainly distributed in the 'V'-shape valley landforms along the rivers. Some of the high deformation areas are captured by both the ascending and descending data sets, such as the area around TS2, TS6, and TS7. However, some are detected by only one data set, such as TS3 in Figure 6 and TS4 in Figure 7. This difference might be explained by the different geometric relationships between the satellites' orbits and slope and indicates that the combination of both the ascending and descending data sets would improve the identification of more instances of ground deformation and, thus, provide an opportunity to better understand the development of regional landslides. The time series of the displacement at nine sample MPs (TS1-TS9, see Figures 6 and 7 for their locations) were extracted and are plotted in Figure 8. The MPs of TS1-TS7 are located in the area with high deformation velocity, while the MPs of TS8 and TS9 are in the river valley where the deformation rate is relatively low. The deformation in the river valley obtained from the ascending data set fluctuates around 0 mm, indicating the absence of prominent ground movement (Figure 8a).