Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-2723-2021
© 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/essd-13-2723-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Jun 2021
Data description paper |
| 15 Jun 2021
| 15 Jun 2021
Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning
Yi-Ran Wang
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing, 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing, 100094, China
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- Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges W. Li et al. 10.3390/rs16203764
- Data Augmentation for SAR Sea Ice and Water Classification Based on Per-Class Backscatter Variation With Incidence Angle Q. Wang et al. 10.1109/TGRS.2023.3291927
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- Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery E. Silveira et al. 10.1016/j.rse.2022.113391
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- Deep Learning Based Sea Ice Classification with Gaofen-3 Fully Polarimetric SAR Data T. Zhang et al. 10.3390/rs13081452
- Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data L. Shi et al. 10.3390/rs13112174
46 citations as recorded by crossref.
- Vectorized dataset of roadside noise barriers in China using street view imagery Z. Qian et al. 10.5194/essd-14-4057-2022
- A Meta-Analysis of Sea Ice Monitoring Using Spaceborne Polarimetric SAR: Advances in the Last Decade H. Lyu et al. 10.1109/JSTARS.2022.3194324
- Light from space illuminating the polar silk road X. Li et al. 10.1080/17538947.2022.2139865
- Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges W. Huang et al. 10.3390/rs16050842
- Mesoscale Westward Eddy Trajectory Prediction With Memory Augmented Neural Network W. Kan et al. 10.1109/JSTARS.2024.3502676
- Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data S. Kern et al. 10.5194/tc-16-349-2022
- High Resolution Sea Ice Concentration Using a Sentinel-1 U-Net Ice-Water Classifier J. Rusin et al. 10.1109/JSTARS.2025.3553623
- Monthly precipitation prediction at regional scale using deep convolutional neural networks L. Ni et al. 10.1002/hyp.14954
- Pan-Arctic ocean wind and wave data by spaceborne SAR X. Li et al. 10.1080/20964471.2021.1996858
- Impacts of Climate Change on Oceans and Ocean-Based Solutions: A Comprehensive Review from the Deep Learning Perspective X. Qiao et al. 10.3390/rs17132306
- A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery B. Wang et al. 10.3390/rs13193945
- Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada B. Montpetit et al. 10.1080/07038992.2023.2247091
- Global Oceanic Mesoscale Eddies Trajectories Prediction With Knowledge-Fused Neural Network X. Zhang et al. 10.1109/TGRS.2024.3388040
- Pan-Arctic sea ice concentration from SAR and passive microwave T. Wulf et al. 10.5194/tc-18-5277-2024
- Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions R. Pires de Lima et al. 10.1080/01431161.2023.2248560
- Intelligent Sparse2Dense Profile Reconstruction for Predicting Global Subsurface Chlorophyll Maxima Y. Yu et al. 10.1109/TGRS.2024.3464850
- Deep-Learning-Based Sea Ice Classification With Sentinel-1 and AMSR-2 Data L. Zhao et al. 10.1109/JSTARS.2023.3285857
- IceBench: A Benchmark for Deep-Learning-Based Sea-Ice Type Classification S. Alkaee Taleghan et al. 10.3390/rs17091646
- Enhancing and Interpreting Deep Learning for Sea Ice Charting using the AutoICE Benchmark S. Jalayer et al. 10.1016/j.rsase.2025.101538
- Improving Monthly Streamflow Prediction by Deep Learning Model With Physics‐Based Rules L. Ni et al. 10.1002/hyp.70123
- Integrated Retrieval of Surface and Atmospheric Variables in the Arctic From FY-3D MWRI With a Time-Constraint Optimal Estimation Method Z. Yan et al. 10.1109/TGRS.2024.3468309
- Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery Y. Huang et al. 10.1016/j.rse.2024.114204
- X- and C-Band SAR Signatures of Sea Ice in the Bohai Sea Y. Qiu et al. 10.1109/LGRS.2024.3468407
- Influence of Radiative Transfer Model-Based Atmospheric Correction and Dynamic Tie Points on Sea Ice Concentration Retrieval From Near-90 GHz Algorithm With FY-3D MWRI Data Y. Ye et al. 10.1109/TGRS.2025.3551941
- Automatic High-Accuracy Sea Ice Monitoring in the Arctic Using MODIS Data L. Jiang et al. 10.1109/TGRS.2023.3279405
- Sea ice detection using concurrent multispectral and synthetic aperture radar imagery M. Rogers et al. 10.1016/j.rse.2024.114073
- Synthetic Aperture Radar (SAR) for Ocean: A Review R. Asiyabi et al. 10.1109/JSTARS.2023.3310363
- Partial Label Learning With Focal Loss for Sea Ice Classification Based on Ice Charts B. Vahedi et al. 10.1109/JSTARS.2024.3413003
- Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges W. Li et al. 10.3390/rs16203764
- Data Augmentation for SAR Sea Ice and Water Classification Based on Per-Class Backscatter Variation With Incidence Angle Q. Wang et al. 10.1109/TGRS.2023.3291927
- Arctic Sea Ice and Open Water Classification From Spaceborne Fully Polarimetric Synthetic Aperture Radar Y. Lu et al. 10.1109/TGRS.2023.3266158
- Recent developments of artificial intelligence methods for sea ice concentration monitoring using high-resolution imaging datasets M. Stofa et al. 10.1016/j.ecoinf.2025.103132
- SeaIceNet: Sea Ice Recognition via Global-Local Transformer in Optical Remote Sensing Images W. Hong et al. 10.1109/TGRS.2024.3493121
- Evaluation of the AMSR2 Ice Extent at the Arctic Sea Ice Edge Using an SAR-Based Ice Extent Product Y. Sun et al. 10.1109/TGRS.2023.3281594
- Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized SAR Sea Ice Imagery X. Chen et al. 10.1109/TGRS.2022.3233871
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- SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition K. Kortum et al. 10.5194/tc-18-2207-2024
- Overview of the studies on the interactions between atmosphere, sea ice, and ocean in the Arctic Ocean and its climatic effects: contributions from Chinese scientists R. Lei et al. 10.1007/s13131-025-2466-4
- Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery E. Silveira et al. 10.1016/j.rse.2022.113391
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- Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR K. Yang et al. 10.3390/rs15092399
- Utilization of the U-Net Convolutional Neural Network and Its Modifications for Segmentation of Tundra Lakes in Satellite Optical Images I. Abramova et al. 10.1134/S1024856024700404
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- Deep Learning Based Sea Ice Classification with Gaofen-3 Fully Polarimetric SAR Data T. Zhang et al. 10.3390/rs13081452
- Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data L. Shi et al. 10.3390/rs13112174
Latest update: 13 Jul 2025
Short summary
Sea ice cover is the most fundamental factor that indicates the underlying great changes in the Arctic. We propose novel sea ice cover data in high resolution of a few hundred meters by spaceborne synthetic aperture radar, which is more than 10 times that of the operational sea ice cover and concentration data. The method is based on a deep learning architecture of U-Net. We have been processing data acquired by Sentinel-1 since 2014 to obtain high-quality sea ice cover data in the Arctic.
Sea ice cover is the most fundamental factor that indicates the underlying great changes in the...
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