## 6 Conclusions  
Estimating snow depth distribution for small-scale areal extents in Arctic regions is of great importance for many applications ranging from scientific research to civil purposes. This study showcased a low-cost solution employing commonly available UASs and camera payloads to produce snow depth maps at high resolution for a variety of surveying conditions. The method was tested using two different UAS/payloads systems ranging from a minimal set-up (\(\sim\)500 USD employing a GoPro(r) Action camera) to a more complex and advanced solution (-1700 USD employing a DSLR camera) (Table 2). While the set-ups differed in terms of ease of use, cost, and image quality, the photosymmetric considerations were similar. Six areas were mapped, two in Svalbard and four in Greenland, covering areal extents from 1386 to 38,410 m\({}^{2}\). Overall, through appropriate image pre-processing approaches, DEMs for varying, snow surfaces types (from smooth to sastrugi sculpted snow), mapped under different light conditions (from overcast to clear sky), could be properly reconstructed using both UAS systems even for the worst case scenario (overcast sky with the minimal set-up). Although not considered a comprehensive evaluation of accuracy, the average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m.  
The resolutions of the snow depth maps ranged from 0.06 m to 0.01 m (Table 4) and were mostly limited by the inferior performance of the land-based surveys performed for some of the mapped snow underlying topographies. Based on theoretical photogrammetry formulas, the resolution can be further optimized depending on the desired output and equipment available to map greater areal extents, although inaccuracies associated with flying at higher altitudes would need to be evaluated. This study demonstrated the potential to overcome SfM limitations relating to featureless surfaces such as snow through the application of image pre-processing workflows and a proper image acquisition phase, which are ideally tuned for each specific circumstance. Image noise and the scale of the recognizable image features are key parameters to be considered when applying histogram stretching pre-processing techniques. The deployment of UASs in the Arctic is rapidly expanding due to the wide range of applications it offers for environmental and earth science research. Surveying polar environments is logistically challenging, requiring an extra degree of flexibility and ease of deployment when surveying highly variable surfaces like snow. The successful workflow outlined in this study provides important guidance for designing future surveys, with the specific direction dependent on desired outcome, available budget, and surveying conditions. The study was funded by the Arctic Field Grant (AFG), Gronlandsbanken and the Society for Arctic Research and Technology (SAFT). The Svalbard fieldwork campaigns were funded by the AFG. The Greenland fieldwork campaigns were funded by Grenlandsbanken and SAFT. Deepest gratitude to these institutions for lending the opportunity to work on such a fascinating project. We would like to thank [PERSON] and [PERSON] from the University Centre in Svalbard (UNIS) for their guidance and huge support on the Svalbard campaigns. Special thanks as well to [PERSON] from ARTEK for his support in the Greenland summer campaigns. Gratitude goes also to fellow UAS pilots [PERSON] and to [PERSON] for his UAS flying assistance. We would like to acknowledge [PERSON] from KONGSBERG Gruppen and [PERSON] from DTU Space for providing positioning data of the reference stations. Special thanks to [PERSON] for providing great advice and feedback on an earlier version of the manuscript. We would finally like to acknowledge the three anonymous reviewers for their highly constructive feedback provided during the reviewing process. [PERSON], [PERSON], [PERSON] and [PERSON] conceived, designed, and conducted the fieldwork experiments.