## 5 Discussion  
### Accuracy of the Yearly DInSAR Displacement Rate and Its Decomposition  
In general, the two independent datasets (DInSAR and FT) show good agreement despite the fact that different time spans were covered (DInSAR: 2017-2021; FT: 2015-2019/2019-2021). The comparison of the spatial distribution shows several effects:  
1. The FT product is unable to detect displacement in case of sun shadow (Grischa, Muragl; Figure 5d,g) or snow cover (Davains; Figure 5k). These areas have to be masked out in the FT approach due to a failure in detecting features because of the strong difference between the images used, whereas DInSAR is independent from lighting conditions. Further, at Gianda Grischa we could visually identify a systematic error in the displacement detection of the northern rock glacier, which is caused by different sun illumination angles between the two acquisitions resulting in shifted shadows of the blocks on the surface (Figure 5d). Hence, inaccuracies of the DInSAR product are rather overestimated, as we do not correct for these artefacts induced by the FT product. 2. DInSAR shows systematic underestimation of the movement in areas with strong and abrupt changes in displacement values such as at fast moving rock glacier parts  
Figure 8: Pearson matrix assessing correlation between timing of melt (approach by [PERSON] et al., 2022 [85]), timing of seasonality (_season_start_) and elevation. Significant values are highlighted with their respective \(p\)-values as follows: **\(p<0.001\). The number of observations used for the respective analysis is shown in the lower part of the figure. Figure 9: Kernel density estimate plots of end-of-snowcover date (approach by [PERSON] et al., 2022 [85]) against start of seasonality date (_season_start_). near the front (Kaiserbergtal, Davains; Figures 4b and 5l) or adjacent to the lateral boundaries (Muragl; Figure 5h). In these cases, we still observe reduced velocities; it is only at Gianda Grischa that the movement of the middle part of the southern rock glacier is not detected at all (Figure 5e). Such effects are caused by errors in the phase unwrapping process. We observe that the upper detectable boundary of DInSAR displacement is lower than the previously described [34; 43] single full phase cycle \(2\pi\), which represents about a 1.7 m/a displacement. At our study sites, phase unwrapping errors start to occur at already half a phase cycle (0.9 m/a), but can occur at down to even less than 0.4 m/a (Figure 5) in unfavorable conditions (e.g., Gianda Grischa). More unwrapping errors in interferograms with longer temporal baselines, which were used to bridge gaps with low coherence due to snowfall events during summer, could partially explain the reduced sensitivity. This highlights the importance of dense C-Band time series for rock glacier monitoring. Phase unwrapping tends to fail preferably at the margins of rock glaciers, where abrupt phase changes occur along with a decorrelation of the signal due to the instability of the surface. Unwrapping errors occur mostly along East-West boundaries due to the reduced number of adjacent pixels in this direction, which is caused by the uneven pixel size (2.3 m in range, 13.9 m in azimuth). Thereby, distinct boundaries in the north-south direction develop for unwrapping affected areas (e.g., Kaiserbergtal, Gianda Grischa, Muragl; Figure 6). Projection to squared pixels in Cartesian coordinates before unwrapping might reduce this tendency and could improve results, but the interpolation and oversampling in an azimuth direction of the wrapped interferograms might cause new artefacts. Therefore, we did not carry out an adapted workflow. Besides such changes in preprocessing, advanced methods for phase unwrapping such as deep-learning-based approaches (e.g., [86; 87]) could improve the successful unwrapping of the interferograms. * The spatial patterns of the two independent datasets match very well.