Monitoring Slow-Moving Landslides in Central Nepal using Multidimensional Small Baseline Subset (MSBAS) with the AMSTer Toolbox

This study presents a methodology for monitoring slow-moving landslides across Central Nepal (21,500 km²) using multi-temporal InSAR analysis. The region is characterized by complex terrain and dynamic environmental conditions, posing challenges related to the large volume of SAR data, diverse acquisition modes, steep Himalayan topography, dense vegetation, seasonal variations, and high ground displacement velocities.To address these challenges, a fully automated, computationally optimized, and self-evaluating processing chain was developed using the AMSTer Toolbox (Derauw et al., 2020; d'Oreye et al., 2021; Smittarello et al., 2022). The chain processes Sentinel-1 archives across five orbital tracks (Ascending 85 and 158; Descending 19, 92, and 121), comprising approximately 1,500 images and generating over 4,500 interferometric pairs. The system is also capable of handling ERS, ENVISAT, TSX, PAZ, and ALOS data for smaller portions of the region, and is prepared for the upcoming NISAR L-Band mission. Deformation maps are inverted using the MSBAS method (Samsonov and d'Oreye, 2012) to extract mean linear velocity maps and time series in Line of Sight and/or vertical and horizontal components.This study involves evaluating the impact of baseline selection criteria on displacement measurement accuracy. Using the Gayu Kharka landslide (Mustang region) as a calibration site, where optical imagery (Planet, Pléiades) indicates velocities of 12-15 cm/yr westward and 18-22 cm/yr southward, different temporal baseline strategies were systematically compared. Connecting each image to only the 1-3 shortest temporal neighbors provides best velocity estimates.Adding pairs with longer temporal baseline configurations (Bt 100- 400 days) fails to capture rapid movements by introducing phase aliasing.The effectiveness of Sentinel-1 ETAD (Extended Time Annotation Products) corrections for ionospheric, tropospheric, and geodetic effects was also assessed. Preliminary results indicate ETAD reduces vertical displacement standard deviations by factors of 2-3 under favorable conditions, though performance varies depending on atmospheric state.Additionally, 3D velocity decomposition using the Surface-Parallel Flow constraint was explored, enabling extraction of North-South displacement components. Initial results from the Bolde landslide, compared against continuous GNSS measurements from a newly installed network, demonstrate the method's capability to resolve three-dimensional displacement patterns.