## 6 Conclusions  
In this study, a cloud-based InSAR processing system was utilized to analyze surface deformation around South Lhonak Lake in Sikkim, India, before and after the GLOF event. Our findings reveal that a moraine landslide occurred at the junction of the South Lhonak glacier's terminus and the lake, with observable deformation characteristics preceding the landslide. There exists a certain correlation between InSAR deformation results and variables such as rainfall, lake area, and slope. Notably, the surface deformation rate in the landslide area accelerated at the onset of the rainy season, with both the accumulated deformation and the duration of accelerated deformation showing correlation with the duration of rainfall. Soon after the area of South Lhonak Lake began to increase rapidly each year, the deformation rate of the points within the landslide area subsequently increased significantly. Additionally, areas with steeper slopes exhibited heightened susceptibility to accelerated deformation induced by heavy rainfall, with water flow erosion potentially contributing to moraine landslides. Through comprehensive analysis, we inferred that heavy rainfall-induced landslides in the moraine could be one of the primary factors leading to a breach in the terminal moraine and triggering the GLOF. Analysis of post-disaster InSAR deformation results and optical images suggests that significant deformation in the landslide area was primarily caused by the movement of loose soil and debris on its surface, with a relatively stable overall condition of the landslide region. Although deformation is evident in the eastern vicinity of the landslide area, it remains relatively stable at present. However, the presence of cracks in the top area suggests a potential risk of secondary landslides. Future research may leverage higher-resolution optical images, SAR data from various bands (e.g., Lutan-1 SAR satellite data and NISAR satellite data), and meteorological data to further investigate GLOF occurrences. Such efforts can mitigate the impact of GLOF events and inform infrastructure development planning.  
Conceptualization, Y.L. and Y.Y.; methodology, Y.L. and Y.Y.; software, Y.L. and B.L.; validation, Y.L., B.L. and Y.Y.; formal analysis, Y.Y.; investigation, Y.Y.; resources, Y.L. and W.J.; data curation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and citing, Y.L. and B.L.; visualization, Y.Y.; supervision, Y.L. and W.J.; project administration, Y.L. and W.J.; funding acquisition, Y.L. and W.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (grant no. 2021 YFC3001903), the National Natural Science Foundation of China under Grant (grant number 42374039), Tibet Autonomous Region Science and Technology Program (grant number XZ202301 YD0002C-01), and the Alibaba Innovation Research Program (24024696-CRAQ717 HZ11230008).  
The authors would like to thank the European Space Agency (ESA) for providing the Sentinel-1A SAR data and the U.S. Geological Survey (USGS) for providing the Landsat data for free. These data can be obtained through the AI Earth cloud platform.  
The authors would like to thank the anonymous reviewers for their time and constructive feedback in helping to improve the quality of this paper.  
The authors declare no conflicts of interest.