Articles | Volume 17, issue 1
https://doi.org/10.5194/tc-17-279-2023
© 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-17-279-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jan 2023
Research article |
| 20 Jan 2023
| 20 Jan 2023
Inter-comparison and evaluation of Arctic sea ice type products
Yufang Ye
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Yanbing Luo
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Mohammed Shokr
Meteorological Research Division, Environment and Climate Change
Canada, Toronto, Canada
Signe Aaboe
Division for Remote Sensing and Data Management, Norwegian
Meteorological Institute, Tromsø, Norway
Fanny Girard-Ardhuin
Laboratoire d'Océanographie Physique et Spatiale (LOPS),
Ifremer-Univ. Brest-CNRS-IRD, IUEM, Plouzané, France
Fengming Hui
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Xiao Cheng
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Zhuoqi Chen
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
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Cited
18 citations as recorded by crossref.
- Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data L. He et al.
- Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery Y. Huang et al.
- Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness I. Sievers et al.
- The stepwise decrease of 4+ year ice extent and its linked survivability since around 2007 Q. Shan et al.
- MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation T. Ma et al.
- Toward a Multidecadal SAR Analysis of Sea Ice Types in the Atlantic Sector of the Arctic Ocean W. Guo et al.
- Enhancing and Interpreting Deep Learning for Sea Ice Charting using the AutoICE Benchmark S. Jalayer et al.
- Sea ice classification in the Arctic transition zone using Haiyang-2B microwave scatterometer and radiometer data S. Wu et al.
- GLFFuse: A Multimodal Feature-Level Fusion Network for Multitask Fine-Grained Recognition of Arctic Sea Ice T. Ma et al.
- Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning W. Liu et al.
- A Weakly Supervised Learning Approach for Sea Ice Stage of Development Classification From AI4Arctic Sea Ice Challenge Dataset X. Chen et al.
- Region-wise query-guided adaptive multimodal fusion network for fine-grained arctic sea ice recognition T. Ma et al.
- Smoother sea ice with fewer pressure ridges in a more dynamic Arctic T. Krumpen et al.
- Improvement of Arctic Sea Ice Type Concentration Retrieval From Microwave Remote Sensing Data Y. Luo et al.
- Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates I. Sievers et al.
- The Stepwise Reduction of Multiyear Sea Ice Area in the Arctic Ocean Since 1980 D. Babb et al.
- Winter Arctic Sea Ice Surface Form Drag During 1999–2021: Satellite Retrieval and Spatiotemporal Variability Z. Zhang et al.
- Intercomparison of Ku- and C-Band Backscatter Feature Parameters for Arctic Sea Ice Using Spaceborne FengYun-3E WindRAD Scatterometer X. Zhai et al.
18 citations as recorded by crossref.
- Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data L. He et al.
- Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery Y. Huang et al.
- Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness I. Sievers et al.
- The stepwise decrease of 4+ year ice extent and its linked survivability since around 2007 Q. Shan et al.
- MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation T. Ma et al.
- Toward a Multidecadal SAR Analysis of Sea Ice Types in the Atlantic Sector of the Arctic Ocean W. Guo et al.
- Enhancing and Interpreting Deep Learning for Sea Ice Charting using the AutoICE Benchmark S. Jalayer et al.
- Sea ice classification in the Arctic transition zone using Haiyang-2B microwave scatterometer and radiometer data S. Wu et al.
- GLFFuse: A Multimodal Feature-Level Fusion Network for Multitask Fine-Grained Recognition of Arctic Sea Ice T. Ma et al.
- Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning W. Liu et al.
- A Weakly Supervised Learning Approach for Sea Ice Stage of Development Classification From AI4Arctic Sea Ice Challenge Dataset X. Chen et al.
- Region-wise query-guided adaptive multimodal fusion network for fine-grained arctic sea ice recognition T. Ma et al.
- Smoother sea ice with fewer pressure ridges in a more dynamic Arctic T. Krumpen et al.
- Improvement of Arctic Sea Ice Type Concentration Retrieval From Microwave Remote Sensing Data Y. Luo et al.
- Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates I. Sievers et al.
- The Stepwise Reduction of Multiyear Sea Ice Area in the Arctic Ocean Since 1980 D. Babb et al.
- Winter Arctic Sea Ice Surface Form Drag During 1999–2021: Satellite Retrieval and Spatiotemporal Variability Z. Zhang et al.
- Intercomparison of Ku- and C-Band Backscatter Feature Parameters for Arctic Sea Ice Using Spaceborne FengYun-3E WindRAD Scatterometer X. Zhai et al.
Saved (final revised paper)
Latest update: 16 May 2026
Short summary
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study...
