Articles | Volume 15, issue 3
https://doi.org/10.5194/tc-15-1551-2021
© Author(s) 2021. 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-15-1551-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Mar 2021
Research article |
| 26 Mar 2021
| 26 Mar 2021
Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Deutsches Geodätisches Forschungsinstitut (DGFI), Technical University of Munich, Munich, Germany
Marcus Huntemann
Department of Environmental Physics, University of Bremen, Bremen, Germany
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Cited
19 citations as recorded by crossref.
- Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations K. Nakata et al. https://doi.org/10.3390/rs17010171
- Surface Broadband Radiation Data from a Bipolar Perspective: Assessing Climate Change Through Machine Learning A. Cavaliere et al. https://doi.org/10.3390/cli13070147
- Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval T. Zhang et al. https://doi.org/10.1007/s13131-023-2210-x
- Cloud Identification and Phase Classification by Submillimeter and Infrared Synergistic Observations in the Arctic S. Li et al. https://doi.org/10.1109/TGRS.2025.3617890
- A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS A. Preußer et al. https://doi.org/10.3390/rs14092036
- A new sea ice concentration retrieval algorithm from thermal infrared imagery Y. Ye et al. https://doi.org/10.1080/17538947.2024.2353116
- High-resolution maps of Arctic surface skin temperature and type retrieved from airborne thermal infrared imagery collected during the HALO–(𝒜 𝒞)3 campaign J. Müller et al. https://doi.org/10.5194/amt-18-4695-2025
- A neural network-based method for satellite-based mapping of sediment-laden sea ice in the Arctic H. Waga et al. https://doi.org/10.1016/j.rse.2021.112861
- Machine learning for estimating phytoplankton size structure from satellite ocean color imagery in optically complex Pacific Arctic waters H. Waga et al. https://doi.org/10.5194/bg-23-1043-2026
- DynIceData: a gridded ice–water classification dataset at short-time intervals based on observations from multiple satellites over the marginal ice zone L. Huang et al. https://doi.org/10.1080/20964471.2023.2230714
- Eighteen-year record of circum-Antarctic landfast-sea-ice distribution allows detailed baseline characterisation and reveals trends and variability A. Fraser et al. https://doi.org/10.5194/tc-15-5061-2021
- A Machine-Learning-Based Cloud Detection and Cloud-Top Thermodynamic Phase Algorithm over the Arctic Using FY3D/MERSI-II C. Yu et al. https://doi.org/10.3390/rs17183128
- Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized SAR Sea Ice Imagery X. Chen et al. https://doi.org/10.1109/TGRS.2022.3233871
- A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion Y. Liang et al. https://doi.org/10.3390/rs18040536
- Optical Classification of Water Types in Cook Inlet, Alaska H. Waga & M. Johnson https://doi.org/10.4236/ars.2025.143009
- Cloud-Tolerant Multiwidth Arctic Sea-Ice Lead Detection Using FY-3D MERSI-II 250-m TIR Data L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3631915
- Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements E. Alerskans et al. https://doi.org/10.1016/j.rse.2022.113220
- An Accurate Sea Ice Identification Method for Chinese FY-3F MERSI-III Based on Fuzzy C-Means Clustering H. Wang et al. https://doi.org/10.1109/ACCESS.2026.3683556
- A New Normalized Difference Ice Index (NDII) Based on GOCI Imagery for Accurate Sea Ice Extraction: Application in Peter the Great Bay, Sea of Japan Q. Hou et al. https://doi.org/10.1109/JSTARS.2025.3600991
19 citations as recorded by crossref.
- Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations K. Nakata et al. https://doi.org/10.3390/rs17010171
- Surface Broadband Radiation Data from a Bipolar Perspective: Assessing Climate Change Through Machine Learning A. Cavaliere et al. https://doi.org/10.3390/cli13070147
- Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval T. Zhang et al. https://doi.org/10.1007/s13131-023-2210-x
- Cloud Identification and Phase Classification by Submillimeter and Infrared Synergistic Observations in the Arctic S. Li et al. https://doi.org/10.1109/TGRS.2025.3617890
- A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS A. Preußer et al. https://doi.org/10.3390/rs14092036
- A new sea ice concentration retrieval algorithm from thermal infrared imagery Y. Ye et al. https://doi.org/10.1080/17538947.2024.2353116
- High-resolution maps of Arctic surface skin temperature and type retrieved from airborne thermal infrared imagery collected during the HALO–(𝒜 𝒞)3 campaign J. Müller et al. https://doi.org/10.5194/amt-18-4695-2025
- A neural network-based method for satellite-based mapping of sediment-laden sea ice in the Arctic H. Waga et al. https://doi.org/10.1016/j.rse.2021.112861
- Machine learning for estimating phytoplankton size structure from satellite ocean color imagery in optically complex Pacific Arctic waters H. Waga et al. https://doi.org/10.5194/bg-23-1043-2026
- DynIceData: a gridded ice–water classification dataset at short-time intervals based on observations from multiple satellites over the marginal ice zone L. Huang et al. https://doi.org/10.1080/20964471.2023.2230714
- Eighteen-year record of circum-Antarctic landfast-sea-ice distribution allows detailed baseline characterisation and reveals trends and variability A. Fraser et al. https://doi.org/10.5194/tc-15-5061-2021
- A Machine-Learning-Based Cloud Detection and Cloud-Top Thermodynamic Phase Algorithm over the Arctic Using FY3D/MERSI-II C. Yu et al. https://doi.org/10.3390/rs17183128
- Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized SAR Sea Ice Imagery X. Chen et al. https://doi.org/10.1109/TGRS.2022.3233871
- A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion Y. Liang et al. https://doi.org/10.3390/rs18040536
- Optical Classification of Water Types in Cook Inlet, Alaska H. Waga & M. Johnson https://doi.org/10.4236/ars.2025.143009
- Cloud-Tolerant Multiwidth Arctic Sea-Ice Lead Detection Using FY-3D MERSI-II 250-m TIR Data L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3631915
- Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements E. Alerskans et al. https://doi.org/10.1016/j.rse.2022.113220
- An Accurate Sea Ice Identification Method for Chinese FY-3F MERSI-III Based on Fuzzy C-Means Clustering H. Wang et al. https://doi.org/10.1109/ACCESS.2026.3683556
- A New Normalized Difference Ice Index (NDII) Based on GOCI Imagery for Accurate Sea Ice Extraction: Application in Peter the Great Bay, Sea of Japan Q. Hou et al. https://doi.org/10.1109/JSTARS.2025.3600991
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.
Cloud cover in the polar regions is difficult to identify at night when using only...
