## 2 Materials and Methods
### DEM Generation  
DEMs are an important component of landslide modelling, volume calculation, and landform classification. In this research, we performed an exploratory assessment of the usability of Sentinel-1 SLC TOPSAR datasets for the generation of post-event DEMs within an automated workflow. The three SLC TOPSAR image pairs were used to create post-event DEMs using InSAR. Each TOPSAR image was calibrated using precise orbit ephemerides (POD) products provided by the ESA. The POD products contain auxiliary information about the position of the satellite during the data acquisition.  
Figure 5: The classification framework used for the post-event geomorphological feature mapping for 2018 and 2019.  
For computational efficiency, the split TOPSAR function was applied to each TOPSAR image to select the swath sub-bursts covering the area of interest. The TOPSAR images require a precise coregistration to ensure each ground target contributes to the same (range and azimuth) pixel in both images [57]. The coregistration of SLC TOPSAR images can be completed on a pixel by pixel basis, with an accuracy in the order of one-tenth of the resolution. Therefore, the process of DEM-assisted coregistration was applied using the back-geocoding function. Due to the steep azimuth spectrum ramp in each burst [42] the correction of the shift in the azimuthal direction was performed by enhanced spectral diversity [68]. Then, an interferogram generation was carried out by cross multiplying the complex conjugate of two coregistered TOPSAR images. During this process, the amplitude of both images is multiplied while the phase represents the phase difference between the two TOPSAR images. The flat-Earth phase component was then subtracted to remove the phase present in the interferometric signal due to the curvature of the reference surface. The flat-Earth phase component was estimated using the orbital- and metadata information [69]. The resulting interferometric fringes represent a full 2 \(\pi\) cycle, with arbitrary colors representing half the sensor's wavelength. In the case of repeat-pass acquisitions, such as in the case of Sentinel-1, the interferograms are rather noisy, mainly due to temporal decorrelation. To improve the quality of the interferograms and reduce the phase noise of the adjacent pixels, the Goldstein [70] filtering with fast Fourier transformation (FFT) was used. We tried different settings and window sizes of the FFT filter and finally selected 64 for the FFT filter definition and \(3\times 3\) for the window size. The multi-look speckle filtering was applied to increase the radiometric accuracy by reducing the local variability of the signal [71]. The multi-look speckle filtering improves the signal-to-noise ratio at the expense of the spatial resolution [72]. Moreover, applying a multi-look speckle filtering on the interferogram increases the computational efficiency for phase unwrapping. The phase unwrapping is necessary to resolve the unknown multiple-of-wavelength ambiguity in the interferometric phase. We used the statistical-cost network-flow algorithm for phase unwrapping (SNAPHU), which is a two-dimensional phase unwrapping algorithm [73; 74; 75]. The SNAPHU algorithm is written in \(C\), can be used within the SNAP toolbox, or as a standalone program on a Linux platform. The unwrapped phase values were converted into elevation values with respect to a reference ellipsoid [76]. This process specifies each pixel in the unwrapped phase image with respect to a Cartesian reference system using radar coordinates (range, azimuth, phase variation). The last step in the DEM generation was data geocoding to allow for the comparison of the results with a reference DEM.  
The OBIA-delineated landslide deposition area for 2018 was used for the calculation of the landslide volume. The volume estimation was conducted by subtracting each of the three post-event DEMs from the pre-event ArcticDEM.