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The analysis of multi-temporal Synthetic Aperture Radar (SAR) datasets using specific algorithms, such as Persistent Scatterer Interferometry (PSI) or Small-Baseline Interferometry (SBAS), enables the generation of ground velocity maps and displacement time series, achieving sub-centimetric accuracies in ideal cases. These applications have significantly transformed the approach to investigating landslide processes and surface deformation measurements can now be obtained at relatively high spatial and temporal resolutions without the need for costly instrumentation. The current availability of regional, country-scale, and even continental-scale datasets has not only impacted research activities but has also influenced the daily practices of practitioners and civil protection strategies. In mountainous areas, intrinsic limitations of satellite SAR imagery can hinder the nominal performance of PSI and SBAS results. In this contribution, we present a comprehensive analysis of C-Band SAR datasets from the European Space Agency (ESA) satellites ERS-1/2, Envisat ASAR, and Sentinel-1 spanning the period 1992-2020. Our goal is to reconstruct the multi-decadal spatial and temporal evolution of surface displacements at the Brienz/Brinzauls landslide complex, located in canton Graubnden, Switzerland. To achieve this, we analyzed approximately 1,000 SAR images using standard differential interferometry (DInSAR), multitemporal stacking, PSI, and SBAS approaches. The extensive network of Global Navigation Satellite System (GNSS) stations on the Brienz landslide complex allowed us to validate the deformation results. Our analysis sheds light on the limitations that arise when relying on satellite radar measurements for the analysis and interpretation of complex landslide scenarios, particularly in cases of significant spatial and temporal heterogeneities in the deformation field. Satellite radar interferometry measurements are now routinely employed in local investigations, as well as in regional, national, and continental monitoring programs. Therefore, our results hold significant relevance for users seeking a comprehensive understanding of such datasets in complex scenarios.
Manconi et al. (Fri,) studied this question.