Performance Evaluation of Phase and Weather-Based Models in Atmospheric Correction with Sentinel-1Data: Corvara Landslide in the Alps

Phase delay caused by atmospheric effects due to spatial and temporal variations of pressure, temperature, and water vapor content is one of the major error sources in estimation of ground deformation by interferometric synthetic aperture radar (InSAR>). Therefore, accuracy of ground deformation measurement is highly contingent on the robustness of the atmospheric correction techniques. These techniques rely either on auxiliary data such as numerical weather models (NWMs) or on the analysis of the interferometric phase itself. The accuracy in phase delays estimation of mixing effects of turbulent delay in atmosphere and stratified delay in lower troposphere is a key factor in determination of performance of each technique. Hence, the performance evaluation of the techniques is required in order to assess their potentials, robustness, and limitations. This article analyzes and evaluates the performance of four NWMs (i.e., ERA-Interim, ERA5, MERRA2, and WRF) and two phase-based techniques (i.e., linear and power law) to estimate phase delay using Sentinel-1A/B data over the Corvara landslide located in the Alps. The GPS data and GACOS product were used to validate the results. We generally found that ERA5 outperformed among other weather models with a phase standard deviation reduction of 77.7% (with respect to the InSAR phase), a correlation coefficient of 0.86 (between InSAR phase and estimated tropospheric delay) and a less significant error in the velocity estimation of the landslide.