###### Abstract
This makes them of limited use considering the wide, logistically limiting, and sparsely populated Arctic. In this context, this work aims to assess a procedure combining Unmanned Aerial Systems (UASs) with Structure from Motion (SfM) digital photogrammetry, as a cost- and labor-efficient technique capable of capturing the spatial distribution of snow depth for a range of different Arctic surveying conditions. SfM is capable of reconstructing 3D models of topography from a set of overlapping pictures acquired with consumer grade digital cameras [22; 23]. The 3D models can then be georeferenced using a set of Ground Control Points (GCPs) with known geographic positions producing a Digital Elevation Model (DEM) [24]. If digital cameras are equipped on aerial platforms such as manned or Unmanned Aerial Systems (UASs), considerable areal extents can then be covered at a reduced effort, making it an effective tool from a geosciences perspective [24; 25]. Recent studies have shown the potential of UAS-borne SfM for snow mapping [26; 27; 28; 29]. The method consists of the subtraction of an underlying topography DEM (TDEM) from its SfM-generated snow surface cover DEMs (SDEMs). Snow surface reconstruction with SfM is, however, considered problematic due to the lack of texture (lack of image contrast) in the acquired photographs, which hampers the algorithm's ability to feature-track, identify, and match pictures [23; 24; 30]. As image texture is heavily influenced by the snow surface type (e.g., fresh, wind-packed, etc. ), more investigation is required to assess the impact of snow's physical properties on SDEM reconstructions [26; 28]. In addition, light (luminance) is also very important for the SfM workflow as it enhances the ability to discriminate fine detail by augmenting image contrast [31]. Light availability consequently affects the ability of the method to perform over a range of sky conditions (e.g., flat lighting due to overcast sky) and requires further testing [28; 29]. Beyond the need to test the methodology under different surveying conditions, considerable improvements can be made with sensor choice (resolution, payload weight, and cost trade-offs) and workflow optimization (e.g., in image acquisition and pre-processing) [26]. These are particularly important at high latitudes where there are reduced operational windows and high logistical costs. The aim of this study was to assess the feasibility of UAS-SfM for capturing Arctic snow depth variability at high resolutions and for a set of the aforementioned challenging scenarios. Tests were conducted using two different budget UAS-camera solutions (<1700 USD) deployed at two sites near Longyearbyen, Svalbard and at four sites near Sisimiut, West Greenland. Multiple sites suited the overall objective of investigating the applicability of this method under a range of different mapping scenarios that are suspected to be detrimental for its performance and require further scientific effort. These include diverse snow surface types, light availability, and underlying topography complexity. Multiple payloads suited the aim of further optimizing the method from a procedural and cost perspective. The specific objectives can be summarized as follows:  
1. Investigating improved workflow solutions in the image pre-processing phase aiming to boost SfM reconstruction performances and correct systematic errors. 2. Assessing the effect of lighting conditions, snow surface type, and camera equipment on the SDEM generation process. This includes investigating the achievable spatial resolutions for each case with the tested equipment. 3. Evaluating the overall feasibility and performance of the proposed low-cost method to capture snow depth variability for different underlying topographies. This is done by a comparison of the snow depth estimates with traditional snow probing and over snow-free areas.