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.
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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- Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized SAR Sea Ice Imagery X. Chen et al. 10.1109/TGRS.2022.3233871
- Pan-Arctic sea ice concentration from SAR and passive microwave T. Wulf et al. 10.5194/tc-18-5277-2024
- A review of artificial intelligence in marine science T. Song et al. 10.3389/feart.2023.1090185
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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
- 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
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- Multi-band SAR intercomparison study in the Antarctic Peninsula for sea ice and iceberg detection C. Salvó et al. 10.3389/fmars.2023.1255425
- 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
- Automatic High-Accuracy Sea Ice Monitoring in the Arctic Using MODIS Data L. Jiang et al. 10.1109/TGRS.2023.3279405
- 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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Latest update: 13 Dec 2024
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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