## 7 Conclusions  
In this review, we primarily examined the current trends, research landscape, and future research directions in landslide monitoring and susceptibility mapping from the perspective of the utilization of spaceborne InSAR. By analyzing the selected literature collection, we identified the recent surge of landslide research using spaceborne InSAR and the regional imbalances in such studies. We emphasized the emergence of various SAR platforms, particularly the significant role and application of Sentinel-1 in landslide research. These findings will provide reliable references for selecting research areas and SAR data sources in future InSAR applications for landslide studies. In terms of landslide monitoring, we reviewed the principal InSAR methods and summarized recent advances in InSAR-based landslide monitoring across five aspects. Specifically, we observed the emergence of new approaches in atmospheric delay correction that account for spatial heterogeneity and temporal variation. In the monitoring dimension, we delineated the main strategies for achieving 3D monitoring with spaceborne InSAR and highlighted the importance of exploring single-orbit InSAR for 3D monitoring in areas where multi-orbit measurements are impeded by geometric distortion or limited data availability. We also summarize the role of InSAR-derived displacement data as a valuable tool in landslide failure time prediction, with recent advancements in probabilistic analysis and data-driven methods further enhancing its predictive capabilities. Regarding the spatiotemporal visibility of landslide monitoring, the research advance encompassed the integration of multi-platform, multi-frequency SAR imagery to enhance spatial visibility and the alignment of SAR images from different platforms into a unified time series to improve temporal resolution. Lastly, we reviewed the integration of spaceborne InSAR with other technologies in landslide monitoring, particularly the combination with physical models, which can offer valuable insights for validating and explaining landslide kinematics and failure patterns. For LSM, we first cataloged all the evaluation factors and their frequencies in the selected literature collection, discovering that InSAR-derived factors, specifically deformation velocity, appeared infrequently (only nine cases). Among these nine cases, the importance of this factor varied significantly. We suggested that future research could delve into more comprehensive analyses by exploring a broader literature dataset. Additionally, we found that some studies employed coupling methods to use InSAR-derived deformation maps for optimizing susceptibility maps (12 cases), and most of these studies reported significant performance improvements quantitatively or qualitatively. We pointed out that the current LSM primarily focuses on static mapping, with InSAR-derived factors or maps often treated as static attributes (e.g., annual averages). However, InSAR should not be evaluated solely as a static factor; instead, its potential to capture temporal variations should be leveraged to enhance LSM. Therefore, we suggested greater attention should be paid to dynamic LSM, which integrates time-series analyses and multiple dynamic factors--such as InSAR-derived displacement, rainfall, vegetation conditions, and soil moisture--to assess temporal variations in susceptibility. Incorporating these elements enables a more refined understanding of landslide risk evolution and strengthens early warning. Additionally, we highlight the potential of integrating data-driven approaches with failure time prediction models, particularly for slopes influenced by multiple triggering factors. Notably, this developmental trend highlights the need for effective management and processing of SAR big data, necessitating a focus on efficient computation strategies for InSAR and the optimization of data compression and storage solutions. Y.C.