Paper Info
Title
OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data.
Abstract
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 (R-2) 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 R(2)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.
Year
DOI
Venue
2020
10.3390/rs12142294
REMOTE SENSING
Keywords
DocType
Volume
remote sensing retrieval,artificial neural network,ocean heat content,deep ocean remote sensing
Journal
12
Issue
Citations 
PageRank 
14
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Hua Su102.03
Haojie Zhang200.34
Xupu Geng300.34
Tian Qin400.34
Wenfang Lu500.34
Xiao-Hai Yan6207.36