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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 progress shows that experts have effectively reconstructed and analyzed sea surface temperature data in the South China Sea using I-DINCAE models and DNN correction techniques, 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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相关作者

TAO Shengli 北京大学 城市与环境学院 生态研究中心 植被结构功能与建造全国重点实验室
WANG Di 西安交通大学 软件学院
XIE Huan 同济大学 测绘与地理信息学院
ZHANG Wuming 中山大学 测绘科学与技术学院
ZHANG Zhiming 云南大学 生态与环境学院
DONG Xiujun 成都理工大学 环境与土木工程学院
CHEN Yiping 中山大学 测绘科学与技术学院
QI Jianbo 北京师范大学 地理科学学部

相关机构

Institute of Ecology, College of Urban and Environmental Sciences, and State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), Peking University
School of Software Engineering, Xi'an Jiaotong University
College of Surveying and Geo-Informatics, Tongji University
School of Geospatial Engineering and Science, Sun Yat-sen University
School of Ecology and Environmental Sciences, Yunnan University
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