Articles | Volume 13, issue 6
Earth Syst. Sci. Data, 13, 2723–2742, 2021
https://doi.org/10.5194/essd-13-2723-2021
Earth Syst. Sci. Data, 13, 2723–2742, 2021
https://doi.org/10.5194/essd-13-2723-2021

Data description paper 15 Jun 2021

Data description paper | 15 Jun 2021

Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning

Yi-Ran Wang and Xiao-Ming Li

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Cited articles

Aldenhoff, W., Heuzé, C., and Eriksson, L. E.: Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar, Remote Sensing, 11, 2602, https://doi.org/10.3390/rs11222602, 2019. 
Boulze, H., Korosov, A., and Brajard, J.: Classification of sea ice types in Sentinel-1 SAR data using convolutional neural networks, Remote Sensing, 12, 2165, https://doi.org/10.3390/rs12132165, 2020. 
Clausi, D. A.: Comparison and fusion of co-occurrence, Gabor and MRF texture features for classification of SAR sea-ice imagery, Atmos.-Ocean, 39, 183–194, https://doi.org/10.1080/07055900, 2001. 
Clausi, D. A. and Zhao, Y.: Grey level co-occurrence integrated algorithm (GLCIA): a superior computational method to rapidly determine co-occurrence probability texture features, Comput. Geosci., 29, 837–850, https://doi.org/10.1016/S0098-3004(03)00089-X, 2003. 
Comiso, J. and Sullivan, C.: Satellite microwave and in situ observations of the Weddell Sea ice cover and its marginal ice zone, J. Geophys. Res.-Oceans, 91, 9663–9681, https://doi.org/10.1029/jc091ic08p09663, 1986. 
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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.