## 5 Discussion
### Effect of Underlying Topography  
Overall, snow depth variability was successfully captured over a range of simple, steep, and complex underlying topographies, and a combination of SfM approaches (land- and UAS-based). For small-scale areas, high-resolution estimates are needed when monitoring complex and highly variable underlying topographies, as shown in Figure 6. This is relevant in the Arctic as snow drift accumulation and sastrugi features are common and require improved resolutions for capturing their variability. In the produced snow depth maps, a high level of resolution can be seen in the capture of sastrugi features, footsteps, and leeside snow drift accumulation areas within SDEMs transects (Figure 6).  
Some errors arose in the land-based surveys due to the poor camera positions compared to an UAS survey (Figure 6). Shadowed areas caused by a low sun angle and terrain relief also downgraded the reconstruction performance in terms of point cloud noise (Figure 6c). In this context, it was also noticed how any unnatural features such as footsteps conversely assisted the reconstruction of these feature-less areas (not shown). Although the number of VP samples was very low, different slope inclinations correspondingly showed increased drift accumulation. These did not display any particular increase in error in relation to whether the VPs were located in steep or moderate slope areas (Figure 4).  
Surveys in the Arctic are characterized by a low solar zenith angle that creates positive shadows (patterns) for most surface irregularities. A recommendation for future surveys would be to collect the observations at the local solar noon. This would limit the noise generated by large-scale topography-induced shadows (Figure 6c), but maintain shadowing of small snow features (e.g., snow ripples), which generates contrasting patterns that aid in the reconstruction process.  
As previously discussed, a major limitation in relation to underlying topography arose from the vegetation cover, as shown for the Greenland areas. This was because of the inability of SfM to reconstruct the topography beneath the vegetation, resulting in a reconstructed surface above the height of the vegetation. Combining SfM with laser scanning has proven successful in mapping absolute terrain heights for low canopy closures [44], but as in this and previous studies, thick bushes still remain a problem [26]. One solution would be to survey the summertime terrain DEM with LiDAR systems and then repeatedly use the UAS-SfM method for the winter DEMs. The terrain DEM could then be reused in subsequent years if the underlying topography does not change in a significant way. Another option would be to classify the terrain orthophotos based on vegetation type and subtract average vegetation heights, even though additional error would arise due to the potential variability in vegetation heights.  
Land-based SfM can be used as an alternative when technical issues with UAS deployments are experienced, if suitable vantage points are available. However, it is considerably more labor-intensive and time-consuming, and ultimately the SfM reconstruction results are compromised due to the sub-optimal image perspective when compared to orthogonal imagery [35]. An additional drawback of land-based SfM is its inability to capture information behind obstructing features (e.g., boulders) due to poor interpolation, as seen in area Green1, resulting in erroneous snow depth estimates (Figure 6).