Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

The presence and thickness of snow overlying lake ice affects both the timing of melt and ice-free conditions, can contribute to overall ice thickness through its insulative capacity, and fosters the development of variable ice types. The use of UAVs to retrieve snow depths with high spatial resolution is necessary for the next generation of ultra-fine hydrological models, as the direct contribution of water from snow on lake ice is unknown. Such information is critical to the understanding of the physical processes of snow redistribution and capture in catchments on small lakes in the Arctic, which has been historically estimated from its relationship to terrestrial snowpack properties. In this study, we use a quad-copter UAV and SfM principles to retrieve and map snow depth at the winter maximum at high resolution over a the freshwater West Twin Lake on the Arctic Coastal Plain of northern Alaska. The accuracy of the snow depth retrievals is assessed using in-situ observations (n = 1,044), applying corrections to account for the freeboard of floating ice. The average snow depth from in-situ observations was used calculate a correction factor based on the freeboard of the ice to retrieve snow depth from UAV acquisitions (RMSE = 0.06 and 0.07 m for two transects on the lake. The retrieved snow depth map exhibits drift structures that have height deviations with a root mean square (RMS) of 0.08 m (correlation length = 13.8 m) for a transect on the west side of the lake, and an RMS of 0.07 m (correlation length = 18.7 m) on the east. Snow drifts present on the lake also correspond to previous investigations regarding the variability of snow on lakes, with a periodicity (separation) of 20 and 16 m for the west and east side of the lake, respectively. This study represents the first retrieval of snow depth on a frozen lake surface from a UAV using photogrammetry, and promotes the potential for high-resolution snow depth retrieval on small ponds and lakes that comprise a significant portion of landcover in Arctic environments.

Overall the paper is very well written and I believe will be ready for publication with minor revision. There are some improvements that can be made with respect to the presentation of results, especially when having the capability to compare individual measurements to their associated snow depth retrieval. In the results section box plots are used to compare UAVderived snow depths to the DoDs and snow line measurements. There is a place for including the box plot as it does present the distribution of the data, however it is difficult to ascertain where the most disagreement is occurring in the retrievals. I would expect to see a 1:1 scatterplot that compares the UAV-derived snow depths to in-situ observations. That would allow the reader to understand where the highest deviations are occurring. Are they occurring in areas of thin snowpack, or deep snow? I would also recommend showing the deviations on the mapare they locally clustered? Or homogeneously distributed about the map?
Response: Thank you for the suggestion. We can certainly provide the 1:1 scatterplots comparing UAS-derived and in-situ observation snow depths in the supplement. However, the suggested maps are somewhat problematic since it is difficult to display multiple values on one map. Due to the dataset containing three different UAS, two different baselines (snow-free models) for each, three different subplots, and four different dates, the amount of maps would be quite high. We would suggest that plotting the difference between UASderived snow depths and in-situ observations (Δhst) against the stake number (or alternatively distance along the transect) would essentially provide the same information. This, while still being able to provide more information on one graph and retaining readability. See examples below. The Discussion section was well written, however quite long. Section 4.2, specifically lines 434 to 470 read more like a review of issues to consider when doing topographic or snow mapping with UAVs, discussion lens focal length, light conditions, horizontal/vertical accuracy, battery life, etc. I would recommend either shortening or removing this section for clarify of the results. However, lines 471 to 499 are relevant to the research being conducted in this manuscript, so I would suggest keeping it.
Response: We will shorten the suggested part and describe the issues more briefly while relying on providing suitable references for further info. Referee #1 asked for more emphasis and focus on recommendations and best practices, such as "the best platform for accuracy/ease of use (RTK vs. GCPs), general recommendations on GCP use, environmental operating suggestions (cold temps and wind), appropriate baselines, and operations in low light conditions". Some of the aspects discussed in lines 434 to 470 are still relevant in that regard.

Specific Comments:
Page 2 Line 30: "snowlines" I see this more commonly referred to as snow transects in snow literature. I'm not sure if this is something that requires changing.
Response: In our view, both terms are used, but perhaps this is a more common practice in the Nordic countries. Nevertheless, all instances of "snowlines" changed to "snow transects" as suggested.
Page 2 Lines 45 -49: There have also been some UAS work on freshwater lake ice to retrieve snow depth from structure from motion (Gunn et al., 2022), which could also be included in your description of UAS work in arctic conditions on line 54.