## 4 Applications of InSAR in Landslide Monitoring
### Recent Advances in Landslide Monitoring with InSAR
#### 4.2.4 Improvements in Spatial Visibility and Temporal Resolution  
Landslides tend to occur more frequently in steep and rugged terrain, where the geometric distortions caused by the satellite viewing geometry and side-looking imaging mode, including foreshortening, layover, and shadow, are more pronounced. Due to the influence of shadow, areas visible in ascending orbit InSAR data may not be observable in descending orbit mode, and vice versa. The variability in spatial visibility may lead to incomplete data coverage for a tentative study area, hindering comprehensive monitoring and analysis of landslide dynamics. To overcome this challenge, scholars have found that overlaying images from different viewing geometries can enhance spatial visibility in regions affected by geometric distortions. By evaluating geometric distortions through the relationship between the geometric parameters of InSAR images and topographic data, the effective monitoring area for landslides can be identified. This is achieved by jointly processing measurement information from ascending and descending InSAR images using a masking process, thereby enhancing spatial visibility and enabling the precise extraction of deformation information. For example, [PERSON] et al. [97] mapped the geometric distortions of ascending ALOS images and descending ENVISAT images of the Zhouqu region in China with DEM, thereby determining the most suitable data assembly and significantly improving the coverage of InSAR measurements. [PERSON] et al. [98] conducted the visibility analysis to identify areas where the geometric distortions occurred in ALOS and Sentinel images in a reservoir region, masked the affected regions, and merged the effective observation areas from both SAR satellites, achieving a roughly 30% increase in the effective observation area. Furthermore, the visibility of differing wavelengths of SAR images varies depending on different land use and land cover (LULC) conditions. [PERSON] et al. [99] monitored the deformations in the Three Gorges area with X-band, C-band, and L-band datasets, revealing that L-band ALOS is more effective for deformation monitoring in vegetated areas due to its excellent penetration capability. [PERSON] et al. [100] evaluated the suitability of X-band COSMO-SkyMed and TerraSAR by conducting a visibility analysis with a matrix considering different land cover classifications and a proposed topographical relief index. This approach comprehensively considers the impact of both LULC and geometric distortions on spatial visibility in landslide monitoring. Building on this rationale, the integrated use of the differential penetration capabilities of various wavelengths and the observation geometry of ascending and descending orbits may further enhance the spatial visibility within the specific area. However, there is currently limited research that simultaneously considers these two aspects, and the integration may significantly increase the complexity of data processing, such as data registration, interferometric combination, phase unwrapping, etc. From the perspective of improving temporal resolution, [PERSON] et al. [19] proposed an innovative algorithm by employing the Kalman filter to dynamically integrate multiplatform InSAR observations into a unified time series, effectively enhancing the temporal resolution from 12 days with a single orbit to as short as 1 day and frequently to 2-5 days with multi-platform integration. In fact, the route of integrating InSAR observations from different SAR platforms into the same time series to enhance the temporal resolution has been validated in research within other domains such as permafrost and mining area monitoring [101; 102], but remains relatively unexplored in the context of landslide studies. The revisit periods of SAR satellites directly affect the continuity of landslide monitoring and its application in predicting landslide occurrences and providing early warnings, particularly in failure time predictions.