Spatial-Temporal Siamese Convolutional Neural Network for Subsurface Temperature Reconstruction

被引:8
|
作者
Zhang, Shuyu [1 ]
Yang, Yizhou [1 ]
Xie, Kangwen [1 ]
Gao, Jiahao [1 ]
Zhang, Zhiyuan [2 ]
Niu, Qianru [3 ]
Wang, Gongjie [4 ]
Che, Zhihui [3 ]
Mu, Lin [3 ]
Jia, Sen [1 ]
机构
[1] Coll Comp Sci & Software Engn, Key Lab Geoenvironm Monitoring Coastal Zone, Minist Nat Resources, Guangdong Hong Kong Macau Joint Lab Smart Cities &, Hong Kong 518060, Peoples R China
[2] 91001 Unit PLA, Beijing 100080, Peoples R China
[3] Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China
[4] PLA 31526 Troops, Beijing 100080, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2024年 / 62卷
基金
中国国家自然科学基金;
关键词
Indian ocean; remote sensing; Siamese convolutional neural network (CNN); spatial-temporal feature extraction; subsurface temperature anomaly (STA); INDIAN-OCEAN; WARMING HIATUS; IN-SITU;
D O I
10.1109/TGRS.2023.3348653
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The reconstruction of subsurface ocean temperature using sea surface observations and in situ Argo measurements is an important yet challenging task. The availability of long-term and high-resolution sea surface remote sensing, combined with advancements in deep learning technology, has opened new opportunities for studying subsurface temperature (ST) reconstruction. In this study, a novel spatial-temporal Siamese convolutional neural network (SSCNN) is proposed to improve the accuracy of ST reconstruction in the Indian Ocean. First, considering the distinctions of temperature characteristics among different sea areas, a multiscale division scheme based on the correlation coefficient of integral ST is designed for refined reconstruction modeling. Second, since ocean heat is significantly affected by solar radiation, asymmetric convolutional operation with rectangular patches and kernels is designed to capture the information characteristics in longitude and latitude directions, respectively. Third, given the temporal changes and correlations of ocean temperature, an SSCNN with shared parameters is proposed for multiview feature mining and accurate temperature structure reconstruction. The reconstructed results provide a precise depiction of the subsurface Indian Ocean dipole (sub-IOD)'s evolution, including the spatial distribution of positive and negative anomaly signals and its temporal changes. It demonstrates that the subsurface dipole index series obtained from SSCNN reconstruction is consistent with that from International Pacific Research Center (IPRC) observation, remaining within a reasonable error range. Comparative experiments indicate that the SSCNN model surpasses other existing methods in terms of higher accuracy and smaller error. Overall, this study provides a promising approach for effectively reconstructing the ST using deep learning methods and offers valuable insights for analyzing the evolution of subsurface positive dipole in Indian Ocean.
引用
收藏
页码:1 / 16
页数:16
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