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This dataset provides a comprehensive, field-validated Synthetic Aperture Radar (SAR) dataset for Arctic lake ice classification, with a particular emphasis on under-ice water salinity. It includes in situ measurements from 104 lakes (132 measurement sites) across northern Alaska collected in May 2024, capturing data on lake ice thickness, snow depth, lake depth, and specific conductance of unfrozen water beneath the ice. These field observations are integrated with multi-season Sentinel-1 SAR imagery from early winter (January) to late winter (May), along with additional geospatial datasets such as Interferometric Synthetic Aperture Radar (IfSAR)-derived elevation models and summer ice-off timing. The dataset enables improved differentiation of bedfast and floating ice lakes, particularly identifying lakes with brackish to saline water that were previously misclassified as bedfast ice lakes using traditional SAR-based remote sensing approaches. This resource supports research in permafrost stability, Arctic hydrology, climate change impacts, and winter water resource availability. This work was supported by grants from the U.S. National Science Foundation (OPP-2336164 and OPP-2336165) and the European Research Council project No. 951288 (Q-Arctic). Additional support was provided under a Broad Agency Announcement award from ERDC-CRREL, PE 0603119A.
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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.
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- Award ID(s):
- 1806213
- PAR ID:
- 10277617
- Date Published:
- Journal Name:
- Frontiers in Remote Sensing
- Volume:
- 2
- ISSN:
- 2673-6187
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frsen.2021.675846
