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Article

OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

by 1, 1, 2,3,4,5, 1, 1,* and 2,3,6
1
Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Centre of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China
2
Joint Institute for Coastal Research and Management, University of Delaware, Newark, DE 19716, USA
3
Joint Institute for Coastal Research and Management, Xiamen University, Xiamen 361102, China
4
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
5
Fujian Engineering Research Center for Ocean Remote Sensing Big Data, Xiamen University, Xiamen 361102, China
6
Center for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2294; https://doi.org/10.3390/rs12142294
Received: 1 June 2020 / Revised: 9 July 2020 / Accepted: 14 July 2020 / Published: 17 July 2020
(This article belongs to the Section Ocean Remote Sensing)
Retrieving information concerning the interior of the ocean using satellite remote sensing data has a major impact on studies of ocean dynamic and climate changes; however, the lack of information within the ocean limits such studies about the global ocean. In this paper, an artificial neural network, combined with satellite data and gridded Argo product, is used to estimate the ocean heat content (OHC) anomalies over four different depths down to 2000 m covering the near-global ocean, excluding the polar regions. Our method allows for the temporal hindcast of the OHC to other periods beyond the 2005–2018 training period. By applying an ensemble technique, the hindcasting uncertainty could also be estimated by using different 9-year periods for training and then calculating the standard deviation across six ensemble members. This new OHC product is called the Ocean Projection and Extension neural Network (OPEN) product. The accuracy of the product is accessed using the coefficient of determination (R2) and the relative root-mean-square error (RRMSE). The feature combinations and network architecture are optimized via a series of experiments. Overall, intercomparison with several routinely analyzed OHC products shows that the OPEN OHC has an R2 larger than 0.95 and an RRMSE of <0.20 and presents notably accurate trends and variabilities. The OPEN product can therefore provide a valuable complement for studies of global climate changes. View Full-Text
Keywords: remote sensing retrieval; artificial neural network; ocean heat content; deep ocean remote sensing remote sensing retrieval; artificial neural network; ocean heat content; deep ocean remote sensing
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MDPI and ACS Style

Su, H.; Zhang, H.; Geng, X.; Qin, T.; Lu, W.; Yan, X.-H. OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data. Remote Sens. 2020, 12, 2294. https://doi.org/10.3390/rs12142294

AMA Style

Su H, Zhang H, Geng X, Qin T, Lu W, Yan X-H. OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data. Remote Sensing. 2020; 12(14):2294. https://doi.org/10.3390/rs12142294

Chicago/Turabian Style

Su, Hua, Haojie Zhang, Xupu Geng, Tian Qin, Wenfang Lu, and Xiao-Hai Yan. 2020. "OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data" Remote Sensing 12, no. 14: 2294. https://doi.org/10.3390/rs12142294

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