Articles | Volume 17, issue 3
https://doi.org/10.5194/tc-17-1411-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-1411-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2023
Research article |
| 31 Mar 2023
| 31 Mar 2023
Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC
NORCE Norwegian Research Centre, Tromsø, Norway
Steven Fons
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Arttu Jutila
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Nils Hutter
Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, WA, USA
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Kyle Duncan
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Sinead L. Farrell
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
Nathan T. Kurtz
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Renée Mie Fredensborg Hansen
Department of Geodesy and Earth Observation, DTU Space, Kongens Lyngby, Denmark
Department of Civil and Environmental Engineering, NTNU, Trondheim, Norway
Arctic Geophysics, University Centre in Svalbard (UNIS), Longyearbyen, Svalbard, Norway
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Cited
12 citations as recorded by crossref.
- Unsupervised surface water mapping with airborne LiDAR data by leveraging physical properties of water H. Song & J. Jung 10.1080/15481603.2024.2437252
- Snow depth estimation on leadless landfast ice using Cryo2Ice satellite observations M. Saha et al. 10.5194/tc-19-325-2025
- Impacts of air fraction increase on Arctic sea ice density, freeboard, and thickness estimation during the melt season E. Salganik et al. 10.5194/tc-19-1259-2025
- New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data A. Mchedlishvili et al. 10.5194/tc-17-4103-2023
- Digital elevation models of the sea-ice surface from airborne laser scanning during MOSAiC N. Hutter et al. 10.1038/s41597-023-02565-6
- Different mechanisms of Arctic first-year sea-ice ridge consolidation observed during the MOSAiC expedition E. Salganik et al. 10.1525/elementa.2023.00008
- Observations of preferential summer melt of Arctic sea-ice ridge keels from repeated multibeam sonar surveys E. Salganik et al. 10.5194/tc-17-4873-2023
- Monitoring Earth’s climate variables with satellite laser altimetry L. Magruder et al. 10.1038/s43017-023-00508-8
- Relating GNSS Reflected Signal Coherence to Ice Shelf Surface Deformation and Roughness S. Anderson et al. 10.1109/TGRS.2025.3538558
- Winter Arctic Sea Ice Surface Form Drag During 1999–2021: Satellite Retrieval and Spatiotemporal Variability Z. Zhang et al. 10.1109/TGRS.2023.3347694
- Smoother sea ice with fewer pressure ridges in a more dynamic Arctic T. Krumpen et al. 10.1038/s41558-024-02199-5
- Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning W. Liu et al. 10.1016/j.rse.2025.114607
12 citations as recorded by crossref.
- Unsupervised surface water mapping with airborne LiDAR data by leveraging physical properties of water H. Song & J. Jung 10.1080/15481603.2024.2437252
- Snow depth estimation on leadless landfast ice using Cryo2Ice satellite observations M. Saha et al. 10.5194/tc-19-325-2025
- Impacts of air fraction increase on Arctic sea ice density, freeboard, and thickness estimation during the melt season E. Salganik et al. 10.5194/tc-19-1259-2025
- New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data A. Mchedlishvili et al. 10.5194/tc-17-4103-2023
- Digital elevation models of the sea-ice surface from airborne laser scanning during MOSAiC N. Hutter et al. 10.1038/s41597-023-02565-6
- Different mechanisms of Arctic first-year sea-ice ridge consolidation observed during the MOSAiC expedition E. Salganik et al. 10.1525/elementa.2023.00008
- Observations of preferential summer melt of Arctic sea-ice ridge keels from repeated multibeam sonar surveys E. Salganik et al. 10.5194/tc-17-4873-2023
- Monitoring Earth’s climate variables with satellite laser altimetry L. Magruder et al. 10.1038/s43017-023-00508-8
- Relating GNSS Reflected Signal Coherence to Ice Shelf Surface Deformation and Roughness S. Anderson et al. 10.1109/TGRS.2025.3538558
- Winter Arctic Sea Ice Surface Form Drag During 1999–2021: Satellite Retrieval and Spatiotemporal Variability Z. Zhang et al. 10.1109/TGRS.2023.3347694
- Smoother sea ice with fewer pressure ridges in a more dynamic Arctic T. Krumpen et al. 10.1038/s41558-024-02199-5
- Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning W. Liu et al. 10.1016/j.rse.2025.114607
Latest update: 29 Mar 2025
Short summary
Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Information on sea ice surface topography is important for studies of sea ice as well as for...
