## 4 Applications of InSAR in Landslide Monitoring  
### Overview of InSAR Methodologies  
The InSAR technique is widely utilized in landslide monitoring due to its capability of penetrating through clouds, enabling all-weather observation, and achieving millimeter-level deformation monitoring of landslides. Initially, the D-InSAR technique, which measures phase differences in single pair images, was widely used for landslide monitoring, as exemplified by [PERSON] et al. [43], who employed D-InSAR to assess the stability of a landslide zone on the Bureya River and revealed small-scale surface dynamics within the affected area. However, D-InSAR is constrained by spatial and temporal decorrelation and atmospheric delay errors, and demonstrates limitations in long-term monitoring. To compensate for these limitations, multi-temporal InSAR (MT-InSAR), which acquires multiple SAR images over a time series for analysis based on high-correlation points known  
Figure 6: Comparison of the utilization of InSAR and other spaceborne remote sensing in landslide monitoring and susceptibility mapping. (Software: Origin 2022). as measurement points (MPs), has been widely used. According to the feature of MPs, MT-InSAR methods can be broadly categorized as persistent scatterer InSAR (PS-InSAR) methods, distributed scatterer InSAR (DS-InSAR) methods, and the methods combined persistent scatterers (PSs) and distributed scatterers (DSs). PSs typically refer to targets with stable backscattering characteristics over long time series and are almost unaffected by spatial and temporal decorrelation, such as rocks, buildings, bridges, or artificial corner reflectors (CRs). Many PS-InSAR methods, such as StaMPS [44], PSI [45], and IPTA [46], have been used for landslide monitoring. However, landslides often occur in mountainous and densely vegetated areas with low coherence, where there may not be enough PSs for modeling and monitoring. One solution is to optimize the phase of statistically homogeneous pixels (SHPs) within a particular spatial area and use high-quality optimized pixels as DSs to increase the density of measurement points (MPs). Various DS-InSAR methods, such as SBAS-InSAR [47], MSBAS-InSAR [48], and ISBAS-InSAR [49], have been proven to be applicable and efficient in landslide monitoring. In addition, [PERSON] et al. [50] proposed the adaptive distributed scatterer InSAR combined with land cover (ADSI-CLC) method for landslide monitoring in complex terrains, which incorporates land cover maps as a constraint for DS selection and employs a coherence weighted spatial adaptive filtering method to estimate the optimal phase, thereby enhancing DSs density and the efficiency of SHPs identification. Additionally, some studies have attempted to utilize PSs and DSs simultaneously. For example, [PERSON] et al. [51] presented an improved adaptive network construction algorithm integrating both PSs and DSs to enhance the density of effective MPs and refine the accuracy of phase unwrapping, resulting in 7.8 times more points compared to the IPTA point network. Based on the analysis of 61 landslide monitoring research articles in the selected literature collection (Figure 7), it is evident that the application of D-InSAR has been relatively uncommon over the past decade. Before 2018, PS-InSAR methods were slightly more prevalent than DS-InSAR methods. However, after 2018, methods based on DSs represent an overwhelming proportion. Although various MT-InSAR methods combining PSs and DSs have emerged in recent years, relevant research remains relatively scarce. Figure 7: The utilization of the InSAR technique between 2013 and 2023 within the selected articles regarding landslide monitoring. The ‘total frequency’ refers to the frequency of the four InSAR techniques (methods integrating PSs and DSs, DS-InSAR, PS-InSAR, and D-InSAR) that have been used, and the ‘publication number’ indicates the number of selected landslide monitoring articles.