Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5277-2024
© 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-18-5277-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Nov 2024
Research article |
| 19 Nov 2024
| 19 Nov 2024
Pan-Arctic sea ice concentration from SAR and passive microwave
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
Jørgen Buus-Hinkler
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
Suman Singha
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
Hoyeon Shi
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
Matilde Brandt Kreiner
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
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Cited
16 citations as recorded by crossref.
- MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic C. Palerme et al. https://doi.org/10.5194/gmd-18-9751-2025
- A Weakly Supervised Learning Approach for Sea Ice Stage of Development Classification From AI4Arctic Sea Ice Challenge Dataset X. Chen et al. https://doi.org/10.1109/TGRS.2025.3542803
- Topography-Enhanced Multilevel Residual Attention U-Net Model for Sea Ice Concentration Spatial Super-Resolution Prediction J. He et al. https://doi.org/10.1109/JSTARS.2025.3594761
- GLFFuse: A Multimodal Feature-Level Fusion Network for Multitask Fine-Grained Recognition of Arctic Sea Ice T. Ma et al. https://doi.org/10.1109/JSTARS.2026.3660828
- 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
- Geographically-weighted weakly supervised Bayesian High-Resolution Transformer for 200 m resolution pan-Arctic sea ice concentration mapping and uncertainty estimation using Sentinel-1, RCM, and AMSR2 data M. Heffring & L. Xu https://doi.org/10.1016/j.isprsjprs.2026.05.032
- Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
- Global satellite-based sea and sea-ice surface temperatures since 1982 P. Englyst et al. https://doi.org/10.1038/s41597-026-07363-4
- 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
- Sea ice concentration estimation via physical information-guided multi-source data fusion and spatial continuity preservation X. Liu et al. https://doi.org/10.1016/j.jag.2026.105339
- Uncertainty Estimation of Lake Ice Cover Maps From a Random Forest Classifier Using MODIS TOA Reflectance Data N. Saberi et al. https://doi.org/10.1109/JSTARS.2024.3518306
- Enhancing Polar Sea Ice Estimation: Deep SARU-Net for Spatiotemporal Super-Resolution Approach J. He et al. https://doi.org/10.3390/rs17233839
- SLAP-HiFNet: A Stage-Linked Active–Passive Microwave Hierarchical Fusion Network With Label-Efficient Transfer Learning for Sea Ice Mapping Y. Yang et al. https://doi.org/10.1109/TGRS.2026.3708091
- A decade of sea ice concentration retrieved from sentinel-1 T. Wulf et al. https://doi.org/10.1016/j.rse.2026.115252
- Region-wise query-guided adaptive multimodal fusion network for fine-grained arctic sea ice recognition T. Ma et al. https://doi.org/10.1080/17538947.2026.2658300
- A Sentinel-1 Dual-Polarimetric Scattering-Regime Framework with AMSR2 Consistency Assessment for Interannual Sea Ice Characterization in the Southern Sea of Okhotsk D. Sin & C. Kim https://doi.org/10.3390/rs18152498
16 citations as recorded by crossref.
- MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic C. Palerme et al. https://doi.org/10.5194/gmd-18-9751-2025
- A Weakly Supervised Learning Approach for Sea Ice Stage of Development Classification From AI4Arctic Sea Ice Challenge Dataset X. Chen et al. https://doi.org/10.1109/TGRS.2025.3542803
- Topography-Enhanced Multilevel Residual Attention U-Net Model for Sea Ice Concentration Spatial Super-Resolution Prediction J. He et al. https://doi.org/10.1109/JSTARS.2025.3594761
- GLFFuse: A Multimodal Feature-Level Fusion Network for Multitask Fine-Grained Recognition of Arctic Sea Ice T. Ma et al. https://doi.org/10.1109/JSTARS.2026.3660828
- 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
- Geographically-weighted weakly supervised Bayesian High-Resolution Transformer for 200 m resolution pan-Arctic sea ice concentration mapping and uncertainty estimation using Sentinel-1, RCM, and AMSR2 data M. Heffring & L. Xu https://doi.org/10.1016/j.isprsjprs.2026.05.032
- Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
- Global satellite-based sea and sea-ice surface temperatures since 1982 P. Englyst et al. https://doi.org/10.1038/s41597-026-07363-4
- 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
- Sea ice concentration estimation via physical information-guided multi-source data fusion and spatial continuity preservation X. Liu et al. https://doi.org/10.1016/j.jag.2026.105339
- Uncertainty Estimation of Lake Ice Cover Maps From a Random Forest Classifier Using MODIS TOA Reflectance Data N. Saberi et al. https://doi.org/10.1109/JSTARS.2024.3518306
- Enhancing Polar Sea Ice Estimation: Deep SARU-Net for Spatiotemporal Super-Resolution Approach J. He et al. https://doi.org/10.3390/rs17233839
- SLAP-HiFNet: A Stage-Linked Active–Passive Microwave Hierarchical Fusion Network With Label-Efficient Transfer Learning for Sea Ice Mapping Y. Yang et al. https://doi.org/10.1109/TGRS.2026.3708091
- A decade of sea ice concentration retrieved from sentinel-1 T. Wulf et al. https://doi.org/10.1016/j.rse.2026.115252
- Region-wise query-guided adaptive multimodal fusion network for fine-grained arctic sea ice recognition T. Ma et al. https://doi.org/10.1080/17538947.2026.2658300
- A Sentinel-1 Dual-Polarimetric Scattering-Regime Framework with AMSR2 Consistency Assessment for Interannual Sea Ice Characterization in the Southern Sea of Okhotsk D. Sin & C. Kim https://doi.org/10.3390/rs18152498
Saved (final revised paper)
Latest update: 27 Aug 2026
Short summary
Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from satellite-based active and passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and well-established passive-microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve...
