## 5 InSAR's Role in Landslide Susceptibility Mapping
### Utilization of InSAR-Derived Deformation Velocity in LSM  
The deformation velocity induced by InSAR remote sensing has seen a growing recognition (Figure 10) due to increased data availability, as discussed in Section 3.2. In fact, during the factor statistical analysis, we not only identified the use of InSAR-derived deformation velocity as an evaluation factor but also uncovered several other applications of InSAR in LSM. To provide a comprehensive overview of the role of InSAR in LSM, this review categorized its contribution into 'InSAR for optimization', 'InSAR derived factors', and 'other uses of InSAR'. On the whole, based on the selected literature collection, the application of InSAR in LSM originated in 2019 and has shown an overall increasing trend. In 2023, over half of the LSM studies (12 out of 19) utilized the InSAR technique, with instances of a study using two forms of InSAR applications simultaneously for comparative study or to enhance LSM reliability. Since 2022, there has been a growing tendency in research to optimize the LSM results using InSAR-derived deformation rates by some coupling methods, which generally refer to a contingency matrix [127; 128; 129], a correction matrix [130], a landslide susceptibility rating [131], etc. These methods typically involve generating a landslide susceptibility map using conventional factors such as slope, aspect, lithology, rainfall, etc., and then coupling this map with an InSAR-derived deformation map. For instance, [PERSON] et al. [132] employed a contribution matrix to revise the landslide susceptibility map with a ground deformation map, resulting in greater rationality and precision than the map without revision. [PERSON] et al. [133] effectively corrected the misclassified areas covering  
Figure 10: Utilization of InSAR in LSM research articles within the selected literature collection. The two review articles published in 2023 are not counted in this figure. ‘Papers using InSAR-derived factors’ indicates the direct utilization of InSAR-derived factors, typically deformation velocity, in the data-driven model construction process. ‘Papers using InSAR for optimization’ indicates the deformation map obtained from InSAR techniques coupled with the landslide susceptibility map using matrices or formulas. ‘Other uses of InSAR’ involves cases that do not fall into the abovementioned scenarios, such as using InSAR for validation or optimizing and augmenting landslide inventory datasets used for model training. (Software: Origin 2022). 59.48 km\({}^{2}\), utilizing a contingency matrix to optimize the landslide susceptibility map with the deformation map generated by SBAS-InSAR. The primary hypothesis underlying this method is that areas exhibiting notable deformation velocities are more susceptible to landslides and should be assigned more significant weight. Some scholars also explored the potential of coupling deformation maps with physically based models. [PERSON] et al. [134] presented a novel approach integrating the deformation map derived from SBAS-InSAR and stability coefficients derived from the Scoops 3D model, revealing the fact that relying solely on the InSAR technique or physical models may lead to an underestimation of landslide susceptibility. However, [PERSON] et al. [135] supposed that selecting an optimal coupling method for a landslide susceptibility map and deformation map retains significant uncertainties. They compared the results of incorporating deformation velocity as an evaluation factor for joint training in SVM and RF models by using a weighted overlay to couple the landslide susceptibility map and deformation map. The results indicated that both SVM and RF models achieved high under-the-curve (AUC) values when jointly trained with deformation velocity instead of coupling the two maps. Indeed, directly using InSAR-derived factors and coupling InSAR for optimization have their strengths and limitations.