Impact of Tropospheric Delay Correction on the Quality of Landslide Mapping in the Southern Central Andes, Northwestern Argentina

Slow-moving landslides in high-mountain regions pose a significant natural hazard and are capable of delivering large sediment volumes to the fluvial system. Time series analysis of Interferometric Synthetic Aperture Radar (InSAR) allows us to identify unstable and potentially dangerous areas prone to landsliding, but this technique also helps quantify seasonal dynamics for predicting landslide behavior.

Our study in the Eastern Cordillera of the Argentine Andes focuses on enhancing InSAR's reliability for landslide mapping. This region is characterized by moisture changes along the topographic gradient across the orogen and seasonal variability associated with the South American Summer Monsoon. We extract InSAR time series data from Sentinel-1A/B's C-band (2014-2022) and ALOS1 PALSAR's L-band (2006-2011). Tropospheric delay is caused by atmospheric turbulence and vertical stratification changes. These delays can introduce significant errors in deformation measurements, thus impacting the quality of maps portraying landslide deformation rates. To address this problem, we apply various correction techniques, ranging from spatial and temporal filtering to water-vapor estimation from an atmospheric model. Fading signal noise, another challenge caused by multi-looking and short temporal baselines in the Small Baseline Subset (SBAS) technique, additionally compromises InSAR time series accuracy. We investigate the pattern and magnitude of fading signals in landslide areas using Small Baseline Subset (SBAS) with different neighboring connections and non-linear phase inversion methods, such as the Eigenvalue Decomposition-based Maximum Likelihood (EMI), Eigenvalue Decomposition (EVD), and the Phase Triangulation Algorithm (PTA).

Our research evaluates both statistical methods and Global Atmospheric Models for correcting tropospheric delays and fading signal noise. We explore statistical methods, such as double-difference filtering and corrections based on phase elevation, for different spatial windows, including individual catchments, moving windows, and adaptive window sizes. The efficiency of these methods varies with the environmental and topographic conditions in the orogen. Both stratified and turbulent components of the troposphere, along with fading signal noise, can significantly influence tropospheric delay and time series quality. In the context of the factors that influence deformation signals and the combined array of methods to obtain robust measurements, we can identify the spatial and temporal characteristics of slow-moving landslides and assess the different impacts on rate changes.