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    • Deep learning based sea surface temperature reconstruction and its application on the spatio-temporal analysis of SST variation in the South China Sea

    • “The latest research utilizes the I-DINCAE model and DNN correction to effectively reconstruct sea surface temperature data in the South China Sea, revealing its spatiotemporal variation characteristics and providing important basis for ocean research.”
    • Pages: 1-17(2025)   

      Published Online：21 February 2025，

    • DOI： 10.11834/jrs.20254493     

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  • Sun Zhiwei，Li Yunbo，Sun Shaojie，Chen Siyu，Zhang Dianjun. XXXX. Deep learning based sea surface temperature reconstruction and its application on the spatio-temporal analysis of SST variation in the South China Sea. National Remote Sensing Bulletin， XX（XX）：1-17 DOI： 10.11834/jrs.20254493.
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SHAO Pan 三峡大学 湖北省水电工程智能视觉监测重点实验室;三峡大学 计算机与信息学院
GAO Ziang 三峡大学 湖北省水电工程智能视觉监测重点实验室;三峡大学 计算机与信息学院
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LI Jiankang 南京信息工程大学 遥感与测绘工程学院
ZHANG Guixin 南京信息工程大学 地理科学学院
ZHU Shanyou 南京信息工程大学 遥感与测绘工程学院

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Hubei Key Laboratory of Intelligent Vision Monitoring for Hydroelectric Engineering, China Three Gorges University
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State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University
School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology
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