## 3 Data and Methods
### Reference Data Generation Using Feature Tracking and DEM Differencing  
Apart from DInSAR approaches used to monitor surface deformation, the manual or automated tracking of surface features in optical imagery has been widely applied in recent years to investigate rock glacier kinematics with remotely sensed imagery [14; 80; 81]. Here, we use the Environmental Motion Tracking software (EMT Version 0.9.3, [[http://www.tu-dresden.de/geo/emt](http://www.tu-dresden.de/geo/emt)]([http://www.tu-dresden.de/geo/emt](http://www.tu-dresden.de/geo/emt)); accessed on 15 January 2022; [82]) that matches image patches in consecutive images based on a combination of cross correlation and least squares matching algorithms [82; 83]. While cross correlation is used for first-order matching, the iterative least squares matching approach can achieve subpixel accuracies.  
For the study areas located in Switzerland, we use orthoimages from 27 August 2015 and 14 September 2019 provided by Swisstopo [84] at a ground resolution of 0.25 \(\times\) 0.25 m. Given the strong differences in shadowing and snow coverage between the 2015 and 2019 orthoimages, we excluded these areas from our analysis based on simple thresholds for masking shadow and snow. For computational efficiency, all images were resampled to 0.5 \(\times\) 0.5 m ground resolution, providing sufficient detail for optical FT. For the Kaiserberg rock glacier, aerial images captured by an unpiloted aerial vehicle were used to derive digital topographic data and orthoimages. Using the structure from motion multi view  
Figure 3: Schematic description of East-West (\(d_{E\_W}\)) and Up-Down (\(d_{U\_D}\)) displacement decomposition from ascending and descending line-of-sight (LOS) displacement (\(d_{LOS\_ASC}\); \(d_{LOS\_DESC}\)). Besides, elevation change (\(d_{z}\)) and direction of movement (\(\tau_{dir}\)) are also depicted. Modified from [47].  
stereo algorithms, as implemented in Agisoft Metashape Professional (Version 1.7.2), we constructed dense point clouds from 876 and 622 images for 21 July 2019 and 28 August 2021, respectively. Using a differential global navigation satellite system (Trimble R8s/R2), we measured 50 ground control points, resulting in positional accuracies (root mean squared error--RMSE) of our UAV derived point clouds of 5.2 and 5.8 cm for 2019 and 2021, respectively. Point clouds were exported as raster-based digital elevation models with a ground resolution of 0.5 \(\times\) 0.5 m, providing sufficient detail for comparison with DInSAR results. Because UAV-derived orthoimages might show inconsistencies in appearance due to different lighting conditions and snow cover, we favor FT in hillsades derived from digital elevation models [80]. In both the Swiss and Austrian study areas, we applied a 50 m buffer around mapped rock glacier features to create a regular grid of object points (i.e., patch centers) for FT in EMT, spaced at 2 m in x and y direction (i.e., every fourth pixel). Tracking in EMT was performed using patch sizes of 30 \(\times\) 30 pixels (i.e., 15 \(\times\) 15 m) and a search area of the same dimension, allowing for a maximum displacement of 15 m in all directions between image pairs. To quantify the residual mismatch between orthoimage pairs, we randomly distributed 2500 object points on stable surfaces (i.e., outside potentially moving periglacial landforms) and tracked displacement using the same settings as described above. Here, we use the 90 th percentile of tracked stable areas as a cutoff value to distinguish significant surface movement from non-significant movement that cannot be resolved by our FT in optical imagery. This limit of detection (LoD) is 0.12 m/a for Davains, 0.05 m/a for Muragl, 0.07 m/a for Nair, 0.09 m/a for Gianda Grischa, and 0.05 m/a for the Kaiserberg rock glacier. For the comparison with the DInSAR derived results, all products were then resampled to the 5 m spatial sampling of the DInSAR product.