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

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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.”
- role：First author第一作者
- Affiliation：
  School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
  Beijing Geoway Info-Tech Co., Ltd., Beijing 100043, China
- Email：657399121@qq.com
- Introduction：孙志伟，研究方向为遥感智能处理技术与应用。E-mail：657399121@qq.com
SUN Zhiwei
14 * ,
- Affiliation：
  Unit No. 91001 of People Liberation Army, Beijing 100161, China
LI Yunbo
2 * ,
- role：Corresponding author通信作者
- Affiliation：
  School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
- Email：zhangdianjun@tju.edu.cn
- Introduction：张殿君，研究方向为海洋遥感与人工智能海洋学研究。E-mail：zhangdianjun@tju.edu.cn
ZHANG Dianjun
1 * ,
- Affiliation：
  School of Marine Science, SUN YAT-SEN University, Zhuhai 519082, China
SUN Shaojie
3 * ,
- Affiliation：
  School of Marine Science, SUN YAT-SEN University, Zhuhai 519082, China
CHEN Siyu
3 *
Vol. 29, Issue 7, Pages: 2382-2398(2025)
DOI：10.11834/jrs.20254493
Accepted：

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