## 4 Results and Discussion
### Challenges While Processing  
This article and the related research [12; 13; 36; 39; 53] have proved that the time-series InSAR technique is an effective and efficient tool for landslide monitoring, but it suffer from certain limitations and shortcomings. Landslides are typically found in complex topographic regions, such as mountains, hills, and river valleys, where SAR side-looking imaging mode can result in SAR images having overlapping, shadowing, and foreshortening. When employing time-series InSAR for landslide monitoring, some landslides may not be detected. Additionally, the sliding direction of landslides can also cause a decrease in the monitoring accuracy of InSAR technology, particularly when the direction of landslide movement is perpendicular to the satellite's LOS, causing the displacement component of the landslide to be zero in the satellite's LOS, thereby rendering it impossible for time-series InSAR to detect the landslide movement. Therefore, when employing the time-series InSAR technique for landslide monitoring, it is necessary to take into account  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**Point**} & **Significant** & **Significant** & **Average Phase** & **Time Delay/d** \\  & & **Period/d** & **Time/year** & **Angle/rad** & **Time Delay/d** \\ \hline \multirow{3}{*}{Rainfall} & P1 & 314.8–396.7 & 2016–2019 & \(-\)1.183 \(\pm\) 0.210 & 68.7 \(\pm\) 12.2 \\  & P2 & 297.8–420.3 & 2016–2019 & \(-\)1.246 \(\pm\) 0.152 & 72.4 \(\pm\) 8.8 \\  & P3 & 405.6–428.3 & 2016–2019 & 1.433 \(\pm\) 1.887 & 83.3 \(\pm\) 109.6 \\ \hline \multirow{3}{*}{Reservoir water level} & P1 & 353.4–396.7 & 2016–2019 & 1.762 \(\pm\) 0.313 & 80.1 \(\pm\) 18.2 \\  & P2 & 280.5–420.3 & 2016–2019 & 2.810 \(\pm\) 0.142 & 19.3 \(\pm\) 8.3 \\ \cline{1-1}  & P3 & — & — & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average phase angle and time lag of rainfall, reservoir water level, and nonlinear displacement.  
various factors such as terrain, landslide motion direction, and SAR satellite line of sight direction. In exceptional circumstances, a combination of satellite data from both ascending and descending track or multiple satellite platforms can be utilized.  
Similarly, we encountered some issues when performing wavelet transforms on our time series data: (a) wavelet tools result in an edge effect at the beginning and end of the time-frequency space (the cone of influence), which cannot be eliminated and decreases the spectral accuracy of landslide time-series information [47]; (b) XWT and WTC require equal time intervals for the time series, which reduces the temporal resolution of certain time series; (c) XWT and WTC can only analyze two time series, reducing the computational efficiency when multiple time series are involved, and making it difficult to explain the correlation and time lag information between multiple series. In the future, we could explore improved methods, such as least-squares wavelet (LSWAVE) [23], multiple wavelet coherence (MWC) [54], etc., or other methods in time series analysis [55], to solve the above problems of InSAR time series with drivers analysis.