## 2 Materials and Methods
### Data and Data Preparation
#### 2.2.2 Radar Data  
We used the Sentinel-1 Interferometric Wide Swath (IWS) Level-1 Single Look Complex (SLC) product. The SLC is the main product of the IWS mode. It is mainly used for interferometric applications [38; 52]. The Sentinel-1 IWS SLC product includes three sub-swaths and each of them includes one intensity image per polarization channel, and several bursts over each of the sub-swaths [40; 52]. However, for mapping purposes, the SLC data should be post-processed and converted to the Ground Range Detected Geo-referenced product (GRD). For mapping geomorphological features and related changes, backscatter coefficients (also known as sigma naught, or sigma 0) were created by converting the Sentinel-1 SLC images with slant-range geometry to Sentinel-1 GRD products. The GRD data consist of focused SAR data that have been corrected using SLC data by applying a calibration and speckle noise filtering using the Lee refined filter [53; 54], and a ground geometry correction using an Earth ellipsoid model. The thermal noise of the GRD products was removed using a noise look-up table provided for each image to derive the calibrated noise profiles matching the calibrated GRD data and to improve the quality of the TOPSAR images [55; 56]. A visual inspection of several Sentinel-1 scenes from different orbit tracks and the two available flight directions (ascending with the satellite moving north, and descending with the satellite moving south) showed that ascending images were more suitable than the descending images for mapping the geomorphological features and related changes in Hitardalur valley due to their geometry. The whole landslide, including both the landslide source and the deposition areas, is usually well visible on the ascending image, whereas only the deposition area is visible on the descending image. Therefore, one pre-event (5 July 2018) and two post-event (17 July 2018 and 5 August 2019) ascending Sentinel-1 TOPSAR scenes (SLC type) in the interferometric wide (IW) mode, with vertical-vertical (VV) and vertical-horizontal (VH) dual polarizations from track 16 were selected for the geomorphological mapping. Figure 3 shows the combination of Sentinel-1 sigma naught VV polarization and the Sentinel-2 datasets for each year, which were used for geomorphological feature mapping. Moreover, we used six additional Sentinel-1 IWS SLC datasets for generating post-event DEMs using InSAR. The quality of DEMs generated from InSAR analysis essentially depends on the perpendicular component of the spatial baseline (known as the perpendicular baseline or \(\text{B}_{\text{perp}}\)) between the radar antenna locations, divided by the distance to the ground. The distance to the ground is similar for the orbiting satellites, so the spatial baseline is the important variable. If the perpendicular baseline is too small, the topographic effects on the differential phase are not pronounced enough. On the other hand, if the baselines are too big, the coherent phase is increasingly different, leading to decorrelation. For example, for the ERS satellite, a suitable \(\text{B}_{\text{perp}}\) for DEM generation is recommended to be between 150 and 300 m at the time of the image acquisition [57]. This grants an angle between both acquisitions, which allows the retrieval of topographic variations from parallel-like effects. This \(\text{B}_{\text{perp}}\) was originally recommended for the ERS data, while for Sentinel-1 a lower range is usually used according to the literature. For example, [PERSON] et al. [58] described that due to the orbit maintenance strategy, the baselines for the Sentinel-1 A and B are mostly in the range of \(\pm\)150 m. [PERSON] et al. [59] used a \(\text{B}_{\text{perp}}\) between 96 and 170 m for landslide mapping with Sentinel-1. For this study, we downloaded the available Sentinel-1 images with a \(\text{B}_{\text{perp}}\) range according to these recommendations for 2018 and 2019 (only from June to September to avoid potential snow cover) and processed them to generate post-event DEMs (Section 2.4).