Machine Learning-Based Fusion Technique for Sea Surface Temperature in the Bohai and Yellow Seas

Zhang Jie; Lin Zhijia; Cai Wenbo

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Zhang Jie，Lin Zhijia，Cai Wenbo. Machine Learning-Based Fusion Technique for Sea Surface Temperature in the Bohai and Yellow Seas[J]. Haiyang Xuebao，2026, 48(x)：1–14

Citation:

Zhang Jie，Lin Zhijia，Cai Wenbo. Machine Learning-Based Fusion Technique for Sea Surface Temperature in the Bohai and Yellow Seas[J]. Haiyang Xuebao，2026, 48(x)：1–14

Citation:

Zhang Jie，Lin Zhijia，Cai Wenbo. Machine Learning-Based Fusion Technique for Sea Surface Temperature in the Bohai and Yellow Seas[J]. Haiyang Xuebao，2026, 48(x)：1–14

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Machine Learning-Based Fusion Technique for Sea Surface Temperature in the Bohai and Yellow Seas

Received Date: 2025-08-22
Accepted Date: 2026-03-20

Available Online: 2026-03-24

Abstract

Abstract

This study is based on sea surface temperature (SST) data from MODIS and AMSR2 satellite observations. Three machine learning models—backpropagation neural network (BPNN), random forest (RF), and convolutional neural network (CNN)—were constructed to conduct research on SST data fusion technology. In terms of model input design, two differentiated schemes are proposed: the basic scheme includes only latitude, longitude, and raw SST data, while the enhanced scheme introduces a time parameter on top of this, resulting in six fusion schemes: BP_without_time, BP_with_time, RF_without_time, RF_with_time, CNN_without_time, and CNN_with_time. Experimental test results show that among the three machine learning models, CNN demonstrates the most outstanding performance, while the RF model performs relatively weakly. In comparative tests of the three models, the enhanced schemes incorporating time parameters significantly outperform the basic schemes without time parameters. Validation results based on 2023–2024 buoy measurement data indicate that the accuracy of the fused SST data is slightly lower than that of AMSR2_SST, but it shows a significant improvement in accuracy compared to MODIS_SST. The monthly coverage of the fused data has been significantly improved compared to the original data. The minimum coverage for 2023–2024 increased from 19.79% for MODIS and 32.10% for AMSR2 to over 49.56%. Additionally, the high-resolution fused results can capture more detailed temperature distribution characteristics, providing richer spatial details compared to the 10 km resolution AMSR2 data.
- Sea Surface Temperature Fusion,
- Machine Learning,
- BPNN,
- RF,
- CNN

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References(28)

References

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