Using InSAR time series to characterize landslide deformation dynamics in the south-central Andes

Slow-moving landslides are an important erosional geomorphic process that shape hillslopes and transport large amounts of sediment material to river channels. They may have potentially catastrophic consequences for infrastructure and human life. Identifying the spatiotemporal pattern of hillslope deformation is essential for understanding the kinematic evolution of hillslope failure and mitigating associated hazards. InSAR (Interferometric Synthetic Aperture Radar) is an effective geodetic method for mapping landslide deformation with high spatiotemporal resolution and precision, especially where direct access to the hillslope areas is difficult.
The study area in the south-central Andes is characterized by steep climatic and topographic gradients. The low-elevation eastern foreland areas with dense vegetation cover change to semi-arid and arid, near-vegetation-free high-elevation areas. InSAR phase estimation and landslide mapping in such a complex region can be affected by spatial and temporal variations of soil moisture, vegetation cover, and atmospheric regime.  
In this study, we extract InSAR time series from the C-band ascending and descending track of Sentinel-1A/B data acquired between 2014 and 2022 and the L-band ascending track of ALOS1 PALSAR data acquired between 2006 and 2011 in the south-central Andes of northwest Argentina. We compare Sentinel deformation time series and maps derived from the linear small baseline subset technique with different numbers of connections in sequential interferogram formation with non-linear phase inversion techniques. We assess the phase bias contribution of short-temporal baseline interferograms for the time series analysis and propose several correction techniques tailored to this study area. Statistical and weather based models are used to reduce the impact of tropospheric delay on the deformation signal, especially during convective events controlled by the South American Monsoon and the large fluctuation of topographic relief effects on the tropospheric phase delay. We investigate the difference between tropospheric correction methods. We further implement a double-difference filter with different local and regional spatial filters to reduce the tropospheric delay on the InSAR time series. After additional filtering steps to remove further ionospheric noise in the time series, we identify the landslide spatial extent and their dynamic through spatial analyses. 
 Our results reveal multiple landslides, including three transitional bodies with downslope velocities of 5-10 cm/yr that demonstrate the importance of carefully filtering InSAR time series for slow-moving landslide detection.