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    • Sea surface temperature reconstruction based on deep learning and its application on the spatiotemporal analysis of SST variation in the South China Sea

    • “The latest research utilizes the I-DINCAE model and DNN technology to successfully reconstruct sea surface temperature data in the South China Sea, revealing its spatiotemporal variation characteristics.”
    • Vol. 29, Issue 7, Pages: 2382-2398(2025)   

      Received：07 November 2024，

      Published：07 July 2025

    • DOI： 10.11834/jrs.20254493     

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  • Sun Z W，Li Y B，Zhang D J，Sun S J and Chen S Y. 2025. Sea surface temperature reconstruction based on deep learning and its application on the spatiotemporal analysis of SST variation in the South China Sea. National Remote Sensing Bulletin， 29（7）：2382-2398 DOI： 10.11834/jrs.20254493.
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Chen Siyu 中山大学海洋科学学院
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