###### Abstract  
The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410 m\({}^{2}\). The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond. s 2017 9 1144 10.3390/rs9111144 10.  
permafrost conditions [5; 6], (iii) development of numerical models and retrieval algorithms for remote sensing of sea-ice properties and ecosystem research [7; 8], and (iv) generation of local snow depth distribution predictive models and for their validation [9; 10; 11]. Finally, for civil engineering applications, information on the small-scale evolution of the snowpack is useful for avalanche prediction [12; 13] and snow drift modeling around buildings [14]. Accurate and spatially continuous measurements of snow depth are challenging due to the high spatial and temporal variability at different spatial scales [11; 15]. Increased variability is also characteristic of observing snow depth at finer mapping scales and for shallower areas. In the Arctic, seasonal snow cover is present for most of the year due to the low temperature and shorter melting season. Strong winds and scarce snowfall produce a snow cover that is typically quite shallow and much more variable in space (about 30-40 cm except in drifts and gullies) and time (hourly and daily) [16] compared to thicker alpine snowpack. Capturing this variability, particularly under complex underlying topographies, requires easily repeatable and increased resolution estimates [1; 17; 11]. Various techniques have been developed over the last century to monitor snow depth, each one presenting a characteristic set of advantages and limitations. Point or line measurements of snow depth using probing or ground penetrating radar both give spatially incomplete measurements, cannot be automated, cannot be used in steep slopes or avalanche/hazard risk zones, and are not suitable for the characterization of decimeter-scale variability beyond plot scale [18]. Snow pillows or sonic rangers are fully automated but give point measurements with questionable representativeness [19; 20]. Outside the range of satellite remote sensing products that are aimed at large regional and catchment wise applications, airborne or terrestrial Light Detection and Ranging (LiDAR) is the most advanced technique. LiDAR is capable of accurately mapping continuous swaths of snow depth for large areal extents and at high spatial resolutions depending on the approach taken, e.g., aerial or terrestrial [18; 21]. However, if small areas are to be surveyed, airborne LiDAR techniques come at relatively high cost, and require specific equipment and a certain level of expertise both in the survey and data processing phase.