Reconstruction of Three-dimensional Temperature Field in South China Sea Based on Generative Adversarial Networks

Gu Hao-ran; YANG Jun-gang; CUI Wei; Wang Bin

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Gu Hao-ran，YANG Jun-gang，CUI Wei, et al. Reconstruction of Three-dimensional Temperature Field in South China Sea Based on Generative Adversarial Networks[J]. Haiyang Xuebao，2025, 47(x)：1–13

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Reconstruction of Three-dimensional Temperature Field in South China Sea Based on Generative Adversarial Networks

Gu Hao-ran^{1, 2, 3
,},
YANG Jun-gang^{1, 3
,
,},
CUI Wei^{1, 3},
Wang Bin²

1.
The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
2.
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, 266580, China
3.
Technology Innovation Center for Maritime Silk Road Marine Resources and Environment Networked Observation, MNR, Qingdao 265800, China

Funds: National Natural Science Foundation of China (No. 62231028)

Received Date: 2024-11-11
Rev Recd Date: 2025-02-21

Available Online: 2025-04-11

Abstract

Abstract

To address the scarcity of high-resolution ocean subsurface temperature field data in the South China Sea (SCS), this study proposes a Generative Adversarial Network (GAN) for reconstructing high-resolution three-dimensional ocean temperature field based on the spatiotemporal correlation between ocean surface remote sensing observations and subsurface ocean temperature. The proposed GAN model is trained by multi-source ocean surface remote sensing data from 2013 to 2017, including sea surface temperature, sea surface salinity, sea level anomaly, and sea surface wind. The three-dimensional ocean temperature fields for 19 depth layers shallower than 541 meters in SCS in 2018 are reconstructed using the trained model and ocean surface multi-source remote sensing data. The ocean temperature fields reconstruction results are compared with GLORYS12V1 reanalysis data and Argo profile data to assess the feasibility of the proposed model. The results of experiments show that the spatial distribution characteristics of the reconstructed temperature field at different depth layers are in good agreement with the GLORYS12V1 reanalysis data, and can reflect the seasonal variation features of typical vertical cross-sections in the central SCS. The comparison of ocean temperature time series at three different locations in the SCS verifies the stability and accuracy of the proposed model. The evaluation experiments based on Argo in-situ observations show that the model can accurately reconstruct the vertical variation of real ocean temperature, demonstrating the practical application value of the proposed method. The average RMSE of the reconstructed three-dimensional temperature field in the South China Sea for 2018 is 0.704℃, which outperforms the CNN (0.952℃) and U-net (0.863℃) models.
- three-dimensional temperature field,
- generative adversarial networks,
- satellite remote sensing,
- ocean reanalysis data

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References(30)

References

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