## 5 Discussion  
The Corvara landslide is characterized by land cover conditions dominated by the presence of bare land, meadows and woodlands that limit the number of points with coherence >0.5 to less than 15/km\({}^{2}\). Thus, displacement monitoring largely benefitted by the installation of artificial CRs that provided for reliable backscatter in all SAR acquisitions and were not affected by any layover or shadowing effect due to the descending CSK geometry. The CR design proved to be well adapted to the alpine conditions in the study area, since it allowed precise orientation to the descending mode LoS of the CSK satellite. Due to the installation of the CRs on poles, it was also possible to assess displacement over the winter periods, by removing snow from the tetrahedral element at times of DGPS and SAR acquisitions. Although the CRs were subjected to extreme weather conditions, they did not show any relevant signs of corrosions or material fatigue that might affect their backscattering. Moreover, being made of aluminum, the CRs have minimal thermal deformation and the foundation piles also assure stability of the CRs with respect to loads generated by snow cover creep. Still future modifications of the monitoring network, in particular with respect to the Sentinel-1 satellite constellation, aim to place at least one CR clearly outside of the presently active landslide area in order to have a further validation element of the results. A drawback of the limited number of CRs on the Corvara landslide is the impossibility to carry out SBAS-MTI which would be able to generate spatially distributed displacement vectors instead of only time-series for single points. Over the observation period, the Corvara landslide remained active and showed substantial movements. In our study, we validated PS-MTI-derived displacements with DGPS records taken in close temporal proximity as the SAR images acquisitions used for MTL. Although DGPS is also affected  
Figure 8: Evaluation of PS-MTI and GPS-LOS differential displacement time-series: (**a**) relative offset; and (**b**) absolute offset. The red shaded area represents the median GPS error computed for each monitoring point. Boxplots represent the 1 st and 3 rd quartiles (limiting the top and bottom of the boxes) and the median (2 nd quartile) is depicted by the black horizontal line. Whiskers represent the difference between the 1 st quartile and 1.5 times the IQR, bottom whisker, and the 3 rd quartile minus 1.5 times the IQR, top whisker. by errors, these are comparably small and well quantifiable (to be on the safe side, these errors are in the order of few mm to less than 1 cm in the two horizontal components, and approximately twice as much--in the elevation component). Therefore, DGPS represents an ideal reference dataset, on the basis of which PS-MTI results can be validated. One relevant issue that needs to be considered is that PS-MTI provides displacements along the LOS only (1D), so that displacement components out of LOS are not detected; whereas from DGPS we derive 3D displacements. Therefore, the re-projection of GPS data along the LOS constitutes the most straightforward approach to compare the two geometrically different results. This analysis was done both in terms of cumulative and differential displacements. Another aspect worth noting is that if the frequency of GPS solutions (or any other monitoring technique) is increased, the related errors of the measuring instrument must be considered carefully to not overestimate the real field displacement. In our study, PS-MTI was able to produce time-series of landslide displacements that show similar trends as the DGPS data sets used for validation. The magnitude of displacements and the overall trends of displacements are similarly covered by both methodologies. On the whole, the differences between GPS-LOS and PS-based LOS mean year velocities varies between 1.4% and 12.8%. On average, the PS-MTI underestimation of annual mean velocities equal 7.4%.