## 4 Results  
The study site is characterized by a relatively gentle morphology. Consequently, only a few areas, mostly located outside the landslide area, are affected by topographic distortions such as layover and shadow in relation to presence of steep scarps and the acquisition geometry of the CSK descending mode (see Figure 3A). The topographic distortion map has been obtained using SARscape software; the red and blue areas represent layover and shadowing regions derived after the computation of the local incidence angle (LIA) within the study area. The LIA is the angle between the normal to the backscattering element and the incoming radar beam, which depends on SAR satellite orbits, off-nadir angle and the DEM of the area of interest. This angle is negative on \"active layover\" areas, while it is higher than \(90^{\circ}\) in \"active shadow\" areas. It must be noted that the entire layover zone masked--as well as the entire shadow zone--extends beyond the respective \"active\" areas because it also considers the so-called near and far passive Layover and Shadowing [63]. Figure 3: Maps showing: the topographic distortions over the study site (**A**); the estimated interferometric coherence across the landslide area (**B**); and the annual velocities observed both within and outside the landslide boundaries (**C**). Despite the small extension of areas characterized by Shadowing and Layover, due to the widespread presence of bare lands, meadows and woodlands, in and outside the landslide area, the estimated interferometric coherence (CC) across the landslide area and the surrounding slopes is in most cases well below 0.5. Specifically, points characterized by coherence higher than 0.5 are about 30 points/km\({}^{2}\) (i.e., only 380 over an area of 13 km\({}^{2}\)) (see Figure 3B). Generally, they correspond to building in the nearby village of Corvara, ski-lift facilities, a few isolated bare ground points and most of the CRs installed inside the landslide. Thus, such density is even lower in the landslide zone, where less than 15 points/km\({}^{2}\) can be assessed at CC > 0.5. It should be pinpointed that even the CRs that during the MTI survey period moved at higher rates (namely CRs, n 51, 54, 55, 56), failed to be identified as points of CC > 0.5, thus no assessment of displacement rate was possible for these CRs. In general, the displacement rates retrieved by MTI over points showing CC > 0.5 (and \(\upmu/\sigma\) threshold > 4.2) reach up to more than \(-\)800 mm/year inside the landslide, including points corresponding to CRs (see Figure 3B), while they remain confined to less than \(\pm\)10 mm/year in areas outside the landslide. The comparison between PSI-MTI and GPS displacements in correspondence to the CRs, is presented for the period between April 2014 and July 2015. In fact, although SAR scenes were available from October 2013, it was only possible to derive displacement time-series from April 2014 onwards. The reason for this is the presence of snow that resulted in a very low coherence, as well as a very low number of acquired images preceding the snow-covered period. Moreover, no PS results could be obtained for the CRs that experienced the largest displacements, i.e., CR54, CR55, CR51 and CR56; according to the GPS monitoring results these had annual displacements of 2.56 m, 24.12 m, 18.75 m and 2.43 m, respectively (see Figure 1). Furthermore, no time-series of displacement could be obtained for CR56 that recorded large GPS displacement (annually 2.43 m; see Figure 1) in the southwards direction, i.e., perpendicular to the LOS of the satellite. According to Equation (3), \(U_{\mathrm{G}}\) vectors are visualized in 3D [64] in order to understand their orientation according to the LOS (Figure 4). The projection along the LOS results in GPS-LOS (\(U_{\mathrm{G}}\)) values that can be substantially lower in magnitude than real depending on the offset angle between the 3D GPS displacement vector and the specific LOS vector unit.