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Article

Subsurface Temperature Reconstruction for the Global Ocean from 1993 to 2020 Using Satellite Observations and Deep Learning

1
Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China
2
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361005, China
3
Center for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Eric Maddy
Remote Sens. 2022, 14(13), 3198; https://doi.org/10.3390/rs14133198
Received: 26 May 2022 / Revised: 26 June 2022 / Accepted: 30 June 2022 / Published: 3 July 2022
(This article belongs to the Section Ocean Remote Sensing)
The reconstruction of the ocean’s 3D thermal structure is essential to the study of ocean interior processes and global climate change. Satellite remote sensing technology can collect large-scale, high-resolution ocean observation data, but only at the surface layer. Based on empirical statistical and artificial intelligence models, deep ocean remote sensing techniques allow us to retrieve and reconstruct the 3D ocean temperature structure by combining surface remote sensing observations with in situ float observations. This study proposed a new deep learning method, Convolutional Long Short-Term Memory (ConvLSTM) neural networks, which combines multisource remote sensing observations and Argo gridded data to reconstruct and produce a new long-time-series global ocean subsurface temperature (ST) dataset for the upper 2000 m from 1993 to 2020, which is named the Deep Ocean Remote Sensing (DORS) product. The data-driven ConvLSTM model can learn the spatiotemporal features of ocean observation data, significantly improves the model’s robustness and generalization ability, and outperforms the LighGBM model for the data reconstruction. The validation results show our DORS dataset has high accuracy with an average R2 and RMSE of 0.99/0.34 °C compared to the Argo gridded dataset, and the average R2 and NRMSE validated by the EN4-Profile dataset over the time series are 0.94/0.05 °C. Furthermore, the ST structure between DORS and Argo has good consistency in the 3D spatial morphology and distribution pattern, indicating that the DORS dataset has high quality and strong reliability, and well fills the pre-Argo data gaps. We effectively track the global ocean warming in the upper 2000 m from 1993 to 2020 based on the DORS dataset, and we further examine and understand the spatial patterns, evolution trends, and vertical characteristics of global ST changes. From 1993 to 2020, the average global ocean temperature warming trend is 0.063 °C/decade for the upper 2000 m. The 3D temperature trends revealed significant spatial heterogeneity across different ocean basins. Since 2005, the warming signal has become more significant in the subsurface and deeper ocean. From a remote sensing standpoint, the DORS product can provide new and robust data support for ocean interior process and climate change studies. View Full-Text
Keywords: subsurface temperature; remote sensing data; deep learning; ocean warming; DORS dataset subsurface temperature; remote sensing data; deep learning; ocean warming; DORS dataset
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MDPI and ACS Style

Su, H.; Jiang, J.; Wang, A.; Zhuang, W.; Yan, X.-H. Subsurface Temperature Reconstruction for the Global Ocean from 1993 to 2020 Using Satellite Observations and Deep Learning. Remote Sens. 2022, 14, 3198. https://doi.org/10.3390/rs14133198

AMA Style

Su H, Jiang J, Wang A, Zhuang W, Yan X-H. Subsurface Temperature Reconstruction for the Global Ocean from 1993 to 2020 Using Satellite Observations and Deep Learning. Remote Sensing. 2022; 14(13):3198. https://doi.org/10.3390/rs14133198

Chicago/Turabian Style

Su, Hua, Jinwen Jiang, An Wang, Wei Zhuang, and Xiao-Hai Yan. 2022. "Subsurface Temperature Reconstruction for the Global Ocean from 1993 to 2020 Using Satellite Observations and Deep Learning" Remote Sensing 14, no. 13: 3198. https://doi.org/10.3390/rs14133198

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