## 5 InSAR's Role in Landslide Susceptibility Mapping
### Utilization of InSAR-Derived Deformation Velocity in LSM
While employing the coupling method to optimize the landslide susceptibility map typically offers greater flexibility, allowing for more reasonable matrix weights or formula parameters based on the actual conditions of the study area, this determination process is highly reliant on empirical knowledge and may introduce greater uncertainty into the optimized landslide susceptibility map. On the other hand, when using InSAR-derived factors for joint model training, InSAR data may be assigned a lower weight than other typically more influential factors, such as slope and rainfall, resulting in an underestimation of the susceptibility level in some areas. The reported importance of the two methods will be further discussed in the following section. Other scholars have also attempted to explore different methods of incorporating InSAR to characterize a slow-moving sloped surface. [PERSON] et al. [136] demonstrated that updating the landslide inventory dataset using InSAR leads to a more accurate landslide susceptibility map reflecting current ground surface conditions. [PERSON] et al. [137] compared frequency ratio models built using both archive and SBAS-InSAR-derived landslide inventory datasets, revealing that models based on InSAR-derived landslide inventory datasets had higher AUC values and degree-of-fit indexes. [PERSON] et al. [138] augmented the archived landslide inventory dataset using PS- and DS-InSAR methods and proposed a new method for LSM, which significantly improved accuracy compared to models trained with the original dataset. These studies indicate that archived inventory datasets typically only record landslides that have already occurred without capturing slow-moving sloped surfaces, thus making the trained models insensitive to slow-moving events. In contrast, InSAR has the advantage of characterizing slow-moving slopes, which are precursors and may eventually lead to catastrophic landslides. Additionally, archived datasets often face a lag in data availability and may suffer from spatial biases, making it challenging to reflect recent surface features. Therefore, augmenting inventory datasets with InSAR enables the generation of more reliable and accurate landslide susceptibility maps that better reflect current surface conditions. It should be noted that most studies in the selected literature collection tend to utilize Sentinel-1, which carries a single C-band synthetic aperture radar instrument, as the InSAR data source. Despite its widespread application in measuring deformation information demonstrated in Section 3.2, it is less sensitive in certain types of areas, such as vegetation-covered regions, which are pretty common in mountainous areas and reservoirs. In contrast, L-band satellites have longer wavelengths and stronger penetration capabilities, resulting in higher coherence in forests or other vegetated areas [139]. Although L-band applications in LSM are currently limited, this limitation may be addressed with the future availability of more long-wavelength InSAR data sources, such as the ALOS-4 planned by JAXA.