Construction of a continuous spatiotemporal sea ice concentration dataset for the Bohai Sea based on sub-pixel convolutional neural network super-resolution technology

Liu Yongqi; Su Jie; Qu Zhifeng

doi:10.12284/hyxb20260018

Volume 48 Issue 2

Feb. 2026

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Liu Yongqi，Su Jie，Qu Zhifeng. Construction of a continuous spatiotemporal sea ice concentration dataset for the Bohai Sea based on sub-pixel convolutional neural network super-resolution technology[J]. Haiyang Xuebao，2026, 48(2)：95–113 doi: 10.12284/hyxb20260018

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Construction of a continuous spatiotemporal sea ice concentration dataset for the Bohai Sea based on sub-pixel convolutional neural network super-resolution technology

doi: 10.12284/hyxb20260018

Liu Yongqi^{1, 2, 3
,},
Su Jie^{1, 2, 3
,
,},
Qu Zhifeng^{1, 2, 3}

1.
Frontier Science Center for Deep Ocean Multispheres and Earth System/Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao 266100, China
2.
Shandong Key Laboratory of Transparent Arctic, Qingdao 266100, China
3.
Laboratory for Ocean Dynamics and Climate, Qingdao Marine Science and Technology Center, Qingdao 266100, China

Received Date: 2026-02-25
Rev Recd Date: 2026-04-08
Publish Date: 2026-02-28

Abstract

Abstract

Constructing continuous spatiotemporal sequences of sea ice is a prerequisite for achieving more accurate, timely, and high-resolution sea ice predictions in the Bohai Sea. To address the inherent limitations of visible light and passive microwave data in sea ice monitoring, this paper proposes a technical approach based on multi-source synergy and super-resolution fusion. First, the DT-ASI algorithm is optimized and local tie points for the Bohai Sea are established to obtain time-series AMSR data at 6.25 km resolution. Subsequently, a sub-pixel convolutional neural network (Pixel Shuffle) is employed for super-resolution reconstruction, identifying the multi-stage Pixel Shuffle strategy as optimal. This approach reduces the mean absolute error by 8.79% and increases the correlation coefficient by 0.19. By integrating the super-resolution AMSR results with MODIS data, a highly continuous spatiotemporal sequence at 1 km resolution from 2002 to 2025 is constructed, with ice coverage during the ice season rising from less than 28.31% to over 95.86%. The dataset reveals preliminary trends of sea ice area reduction, a shortened ice season, and enhanced interannual variability over the past decade. During the 2024–2025 ice season, the Bohai Sea exhibited a distinctive cyclic pattern of “developing-almost completely melting-redeveloping”. This study, through a synergistic and fusion-based approach, overcomes the limitations of single data sources and provides a spatiotemporally continuous data foundation for refined sea ice monitoring and prediction in the Bohai Sea.
- Bohai Sea sea ice,
- continuous spatiotemporal sequence,
- sub-pixel convolutional neural network,
- super-resolution

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References(34)

References

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