Articles | Volume 18, issue 5
https://doi.org/10.5194/tc-18-2207-2024
© Author(s) 2024. 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-18-2207-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2024
Research article |
| 03 May 2024
| 03 May 2024
SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition
Remote Sensing Technology Institute, German Aerospace Center (DLR), Bremen, Germany
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Suman Singha
National Center for Climate Research, Danish Meteorological Institute (DMI), Copenhagen, Denmark
Department of Geography, University of Calgary, Calgary, Canada
Gunnar Spreen
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Nils Hutter
Sea Ice Physics, Alfred-Wegener-Institut (AWI), Bremerhaven, Germany
Arttu Jutila
Finnish Meteorological Institute (FMI), Helsinki, Finland
Sea Ice Physics, Alfred-Wegener-Institut (AWI), Bremerhaven, Germany
Christian Haas
Sea Ice Physics, Alfred-Wegener-Institut (AWI), Bremerhaven, Germany
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Cited
14 citations as recorded by crossref.
- Pan-Arctic sea ice concentration from SAR and passive microwave T. Wulf et al. https://doi.org/10.5194/tc-18-5277-2024
- Year-round movement of emperor penguin colonies observed from space G. Macdonald et al. https://doi.org/10.1038/s43247-026-03906-0
- High Resolution Sea Ice Concentration Using a Sentinel-1 U-Net Ice-Water Classifier J. Rusin et al. https://doi.org/10.1109/JSTARS.2025.3553623
- A Method for Predicting Arctic Sea Ice Concentration Based on Multimodal Feature Fusion and Temporal Trend Analysis L. Huang et al. https://doi.org/10.3390/jmse14110993
- Sea Ice Classification Enhancement Using Calibration-Focused Loss Functions N. Ahmadian et al. https://doi.org/10.3390/rs18050810
- Impacts of air fraction increase on Arctic sea ice density, freeboard, and thickness estimation during the melt season E. Salganik et al. https://doi.org/10.5194/tc-19-1259-2025
- Sea ice freeboard extrapolation from ICESat-2 to Sentinel-1 K. Kortum et al. https://doi.org/10.5194/tc-19-4701-2025
- Seasonal Variability of Sea Ice Bulk Density During the MOSAiC Expedition: Results From Synergistic In Situ, Airborne, and Satellite Observations Y. Zhou et al. https://doi.org/10.1109/TGRS.2025.3650353
- A decade of sea ice concentration retrieved from sentinel-1 T. Wulf et al. https://doi.org/10.1016/j.rse.2026.115252
- Formation and fate of freshwater on an ice floe in the Central Arctic M. Smith et al. https://doi.org/10.5194/tc-19-619-2025
- Toward Higher Resolution Arctic Winter Sea Ice Concentration: Addressing Sentinel-1 Misclassification With AMSR2 Data Fusion J. Rusin et al. https://doi.org/10.1109/JSTARS.2026.3676222
- MFDA: Unified Multi-Task Architecture for Cross-Scene Sea Ice Classification Y. Chen et al. https://doi.org/10.1109/TGRS.2024.3491190
- An Arctic Sea Ice Thickness Inversion Method Based on Deep Learning Two-Branch Architecture and Multisource Remote Sensing Data Fusion R. Huang et al. https://doi.org/10.1109/JSTARS.2025.3636999
- Lightweight sea-ice segmentation for resource-constrained polar navigation applications Z. Wang et al. https://doi.org/10.1016/j.oceaneng.2026.128827
14 citations as recorded by crossref.
- Pan-Arctic sea ice concentration from SAR and passive microwave T. Wulf et al. https://doi.org/10.5194/tc-18-5277-2024
- Year-round movement of emperor penguin colonies observed from space G. Macdonald et al. https://doi.org/10.1038/s43247-026-03906-0
- High Resolution Sea Ice Concentration Using a Sentinel-1 U-Net Ice-Water Classifier J. Rusin et al. https://doi.org/10.1109/JSTARS.2025.3553623
- A Method for Predicting Arctic Sea Ice Concentration Based on Multimodal Feature Fusion and Temporal Trend Analysis L. Huang et al. https://doi.org/10.3390/jmse14110993
- Sea Ice Classification Enhancement Using Calibration-Focused Loss Functions N. Ahmadian et al. https://doi.org/10.3390/rs18050810
- Impacts of air fraction increase on Arctic sea ice density, freeboard, and thickness estimation during the melt season E. Salganik et al. https://doi.org/10.5194/tc-19-1259-2025
- Sea ice freeboard extrapolation from ICESat-2 to Sentinel-1 K. Kortum et al. https://doi.org/10.5194/tc-19-4701-2025
- Seasonal Variability of Sea Ice Bulk Density During the MOSAiC Expedition: Results From Synergistic In Situ, Airborne, and Satellite Observations Y. Zhou et al. https://doi.org/10.1109/TGRS.2025.3650353
- A decade of sea ice concentration retrieved from sentinel-1 T. Wulf et al. https://doi.org/10.1016/j.rse.2026.115252
- Formation and fate of freshwater on an ice floe in the Central Arctic M. Smith et al. https://doi.org/10.5194/tc-19-619-2025
- Toward Higher Resolution Arctic Winter Sea Ice Concentration: Addressing Sentinel-1 Misclassification With AMSR2 Data Fusion J. Rusin et al. https://doi.org/10.1109/JSTARS.2026.3676222
- MFDA: Unified Multi-Task Architecture for Cross-Scene Sea Ice Classification Y. Chen et al. https://doi.org/10.1109/TGRS.2024.3491190
- An Arctic Sea Ice Thickness Inversion Method Based on Deep Learning Two-Branch Architecture and Multisource Remote Sensing Data Fusion R. Huang et al. https://doi.org/10.1109/JSTARS.2025.3636999
- Lightweight sea-ice segmentation for resource-constrained polar navigation applications Z. Wang et al. https://doi.org/10.1016/j.oceaneng.2026.128827
Saved (final revised paper)
Latest update: 03 Nov 2026
Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of...
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