## 5 InSAR's Role in Landslide Susceptibility Mapping
### Reported Importance of InSAR in Landslide Susceptibility Mapping  
To further illustrate the significance of InSAR-derived deformation velocity in LSM, we compiled articles employing InSAR for optimization (Table 2, as indicated by the line 'Papers using InSAR for optimization' in Figure 10) and those utilizing deformation velocity as an evaluation factor (Table 3, as indicated by the line 'Papers using InSAR-Derived factors' in Figure 10). For studies employing InSAR-derived deformation maps for optimization, among the 12 articles listed, all but 1 reported positive optimization outcomes, indicating this approach's significant role in enhancing LSM accuracy. Although not all articles provided pre- and post-optimization accuracy (e.g., AUC value), as they may focus on changes in the number and percentage of each susceptibility class cell, their case studies or field surveys quantitatively or qualitatively confirmed the improvements. Notably, the degree of accuracy improvement of this optimization method may be influenced by regional characteristics and specific coupling methodologies, suggesting the need for broader analysis to identify potential objective patterns. When deformation velocity is used as an evaluation factor, it is evident that it does not consistently rank as a significant factor across various studies and region types. This inconsistency in ranking highlights the variability in the perceived importance of deformation velocity across different research contexts. The diverse region types--riverbank, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Article** & **Before Optimization** & **After Optimization** \\ \hline
[127] & AUC value: 0.8561 (1.R), 0.7545 (FR) & not given \\
[128] & AUC value: 0.916 (FR-RF), 0.897 (RF), 0.866 (FR) & not given \\
[129] & AUC value: 0.981 & not given \\
[130] & Accuracy: 0.972 (CGboost), 0.961 (RF), 0.890 (NB), 0.884 (ANN), 0.861 (ANN) & not given \\
[131] & R-index: 91.03\% & R-index: 97.09\% \\
[132] & AUC value: 0.821 & AUC value: 0.869 \\
[133] & AUC value: 0.84 (L), 0.91 (SVM) & 66,094 classification error cells (99.48 km\({}^{2}\)) were corrected. \\
[134] & not given & not given \\
[135] & AUC value: 0.980 & AUC value: 0.973 \\
[140] & AUC value: 0.808 & AUC value: 0.90 \\
[141] & not given & R-index: 79.2323\% \\
[142] & AUC value: 0.7288 (CGboost), 0.6928 (RF) & not given \\ \hline \hline \end{tabular} Notes: AUC; area under the curve of the receiver operating characteristic (ROC), XGBoost: Extreme gradient boosting. RF: random forest. NB: naive Bayes. ANN: artificial neural network. KNN: K nearest neighbor. LR: logistic regression. SVM: support vector machine. FR: frequency ratio. R-index: Relative landslide density.  
\end{table} 
Table 2: The twelve articles from the selected literature collection utilized an InSAR-derived deformation map for the optimization of LSM results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Article** & **Number of Evaluation** & **Ranking of Deformation** & **Percentle Rank of** & \\
**Factors** & **Velocity Importance** & **Deformation Velocity** & **Type of Study Area** \\ \hline
[27] & 13 & not given & - & hybrid area \\
[29] & 17 & 8 & 47.1\% & riverbank \\
[47] & 11 & 4 & 36.4\% & hybrid area \\
[135] & 12 & 3 & 25.0\% & riverbank \\
[138] & 14 & 3 & 21.4\% & hybrid area \\
[143] & 36 & not given & - & mountainous area \\
[144] & 15 & not given & - & hybrid area \\
[145] & 14 & 13 (in study area 1) & 92.9\% & riverbank, \\
[146] & 7 & not given & - & riverbank \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The nine articles from the selected literature collection utilized deformation velocity as an evaluation factor. hybrid area, and mountainous area--covered by these studies indicate that the relevance of deformation velocity can vary significantly based on geographic and environmental conditions. This variability may affect its perceived importance in landslide monitoring and susceptibility modeling.