Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-619-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-619-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers
Xiaoyi Shen
School of Geography and Ocean Science, Nanjing University, Nanjing,
210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, Nanjing University, Nanjing, 210023, China
Chang-Qing Ke
CORRESPONDING AUTHOR
School of Geography and Ocean Science, Nanjing University, Nanjing,
210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, Nanjing University, Nanjing, 210023, China
School of Geography and Ocean Science, Nanjing University, Nanjing,
210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science
and Technology, Nanjing University, Nanjing, 210023, China
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Cited
17 citations as recorded by crossref.
- Advances in Machine Learning Approaches for UAV-Based Remote Sensing in Data-Deficient Antarctic Environments B. Gorry et al. https://doi.org/10.3390/rs18030459
- Triple Collocation-Based Merging of Winter Snow Depth Retrievals on Arctic Sea Ice Derived From Three Different Algorithms Using AMSR2 L. He et al. https://doi.org/10.1109/TGRS.2023.3290073
- Monitoring the Spatiotemporal Dynamics of Arctic Winter Snow/Ice with Moonlight Remote Sensing: Systematic Evaluation in Svalbard D. Liu et al. https://doi.org/10.3390/rs15051255
- Polar oceans and sea ice in a changing climate M. Willis et al. https://doi.org/10.1525/elementa.2023.00056
- Bridging temporal and spatial gaps in passive microwave observations for global snow depth estimation Q. Gu et al. https://doi.org/10.1016/j.jag.2026.105369
- On the Synergy of SMAP and AMSR2 for Estimating Snow Depth on Arctic Sea Ice L. He et al. https://doi.org/10.1109/LGRS.2022.3188001
- Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies Y. Sun et al. https://doi.org/10.3390/rs15204940
- A combined multi-source data and deep learning approach for retrieving snow depth on Antarctic Sea ice during the melting season Z. Yan et al. https://doi.org/10.1080/17538947.2024.2376260
- Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data L. He et al. https://doi.org/10.1109/TGRS.2024.3358340
- Improved snow depth retrieval over Arctic sea ice from FY-3 satellites using machine learning and its future scenario projection C. Ke et al. https://doi.org/10.1016/j.jhydrol.2024.132105
- An improved Antarctic sea-ice snow depth product from FY-3 MWRI with enhanced seasonal adaptability Z. Yan et al. https://doi.org/10.1080/20964471.2026.2620844
- Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data Z. Yan et al. https://doi.org/10.1007/s13131-023-2179-5
- Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice Q. Ji et al. https://doi.org/10.3390/rs16173253
- Global ocean indicators: Marking pathways at the science-policy nexus K. von Schuckmann et al. https://doi.org/10.1016/j.marpol.2025.106922
- Heat stored in the Earth system 1960–2020: where does the energy go? K. von Schuckmann et al. https://doi.org/10.5194/essd-15-1675-2023
- AdaSA-SD (v1.0): An Adaptive Seasonal Algorithm for Snow Depth Retrieval Over Arctic Sea Ice Y. Zhou et al. https://doi.org/10.1109/TGRS.2025.3593433
- Spatial and Interannual Variability of Antarctic Sea Ice Bottom Algal Habitat, 2004–2019 S. Lim et al. https://doi.org/10.1029/2023JC020055
17 citations as recorded by crossref.
- Advances in Machine Learning Approaches for UAV-Based Remote Sensing in Data-Deficient Antarctic Environments B. Gorry et al. https://doi.org/10.3390/rs18030459
- Triple Collocation-Based Merging of Winter Snow Depth Retrievals on Arctic Sea Ice Derived From Three Different Algorithms Using AMSR2 L. He et al. https://doi.org/10.1109/TGRS.2023.3290073
- Monitoring the Spatiotemporal Dynamics of Arctic Winter Snow/Ice with Moonlight Remote Sensing: Systematic Evaluation in Svalbard D. Liu et al. https://doi.org/10.3390/rs15051255
- Polar oceans and sea ice in a changing climate M. Willis et al. https://doi.org/10.1525/elementa.2023.00056
- Bridging temporal and spatial gaps in passive microwave observations for global snow depth estimation Q. Gu et al. https://doi.org/10.1016/j.jag.2026.105369
- On the Synergy of SMAP and AMSR2 for Estimating Snow Depth on Arctic Sea Ice L. He et al. https://doi.org/10.1109/LGRS.2022.3188001
- Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies Y. Sun et al. https://doi.org/10.3390/rs15204940
- A combined multi-source data and deep learning approach for retrieving snow depth on Antarctic Sea ice during the melting season Z. Yan et al. https://doi.org/10.1080/17538947.2024.2376260
- Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data L. He et al. https://doi.org/10.1109/TGRS.2024.3358340
- Improved snow depth retrieval over Arctic sea ice from FY-3 satellites using machine learning and its future scenario projection C. Ke et al. https://doi.org/10.1016/j.jhydrol.2024.132105
- An improved Antarctic sea-ice snow depth product from FY-3 MWRI with enhanced seasonal adaptability Z. Yan et al. https://doi.org/10.1080/20964471.2026.2620844
- Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data Z. Yan et al. https://doi.org/10.1007/s13131-023-2179-5
- Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice Q. Ji et al. https://doi.org/10.3390/rs16173253
- Global ocean indicators: Marking pathways at the science-policy nexus K. von Schuckmann et al. https://doi.org/10.1016/j.marpol.2025.106922
- Heat stored in the Earth system 1960–2020: where does the energy go? K. von Schuckmann et al. https://doi.org/10.5194/essd-15-1675-2023
- AdaSA-SD (v1.0): An Adaptive Seasonal Algorithm for Snow Depth Retrieval Over Arctic Sea Ice Y. Zhou et al. https://doi.org/10.1109/TGRS.2025.3593433
- Spatial and Interannual Variability of Antarctic Sea Ice Bottom Algal Habitat, 2004–2019 S. Lim et al. https://doi.org/10.1029/2023JC020055
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
Snow over Antarctic sea ice controls energy budgets and thus has essential effects on the climate. Here, we estimated snow depth using microwave radiometers and a newly constructed, robust method by incorporating lower frequencies, which have been available from AMSR-E and AMSR-2. Comparing the new retrieval with in situ and shipborne snow depth measurements showed that this method outperformed the previously available method.
Snow over Antarctic sea ice controls energy budgets and thus has essential effects on the...
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