## 4 Result  
### Landslide Detection Based on Multi-Direction Phase Gradient Stacking  
Utilizing the differential interferograms from the ascending and descending tracks, our method derived phase gradient stacking results for both ascending and descending data in the study area, illustrated in Figure 5. The null regions within these results denote areas affected by geometric distortion, varying between the ascending and descending tracks due to their distinct monitoring angles. While most regions exhibit low values, indicating no deformation, conspicuous red aggregation areas highlight anomalies starkly contrasting with their surroundings. A total of 26 landslides, labeled A1-A26, were identified in the results of the ascending data (Figure 5a), and a total of 32 landslides, labeled D1-D32, were \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Senor**} & \multirow{2}{*}{**Band**} & \multirow{2}{*}{**Track**} & \multirow{2}{*}{**Pass Direction**} & **Pixel Spacing (m)** & **Number** & \multirow{2}{*}{**Temporal Coverage**} \\  & & & & **in Rg \(\times\) Az** & **of Images** & \\ \hline Sentinel-1A & C & 55 & Ascending & 2.33 \(\times\) 13.95 & 71 & 22 March 2018–19 September 2020 \\ Sentinel-1A & C & 62 & Descending & 2.33 \(\times\) 13.95 & 74 & 22 March 2018–19 September 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SAR data parameters. Figure 4: Combination of temporal and spatial baseline of Ascending and Descending: (**a**) Ascending; (**b**) Descending. The triangles represent each SAR capture, and the black connecting lines represent the interference pair. identified in the results of the descending data (Figure 5b). Notably, A1-A19 and D1-D19 represent the same landslides detectable via both ascending and descending data. These landslides span significant areas and exhibit minimal geometric distortion in both sets of results, facilitating clear detection. Conversely, A20-A26 are landslides detectable solely by ascending data, while D20-D32 are detectable only by descending data. These differences may be due to smaller landslide areas or different geometric distortions that result in some landslides only being detectable through a single track. Of course, it is also possible that the data processing process is responsible for this discrepancy. Most of the landslides detected on the ascending and descending tracks were located on both sides of the Bailong River. Within the orbit ascent and descent results, the most prominent regions displaying robust landslide detection signals include the Suoertou landslide, the Nanyu landslide group, and the Zhongpai landslide. Locations A1 and D1 in Figure 5 represent the Suoertou landslide, exhibiting consistent deformation regions in both ascending and descending track results. The Nanyu landslide group comprises numerous smaller landslides, such as A7 and D7 along the Xieliupo landslide, A9 and D9 at the Mentou Ping landslide, and D27 at the Jiangdingya landslide, all positioned along the Bailong River. These landslides exhibit evident signals in our results due to slope instability induced by persistent erosion at their bases from river water. Notably, significant disparities exist between the monitoring outcomes of the ascending and descending data in this region. While landslides 7-9 exhibit detectable signals in both the ascending and descending tracks, additional landslides, like D25-D29, are visible only in the descending  
Figure 5: The outcomes derived from the multi-direction phase gradient stacking of the ascending and descending data are depicted as follows: (**a**) The ascending result and (**b**) The descending result. In these representations, the Bailong River is demarcated by the blue line, while the black vector box delineates the boundaries of identified landslides. The dashed box indicates the two key areas of analysis. track results. Geometric distortion primarily accounts for the absence of D28 and D29 in the lifting track data.