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Article

Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites

School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230000, China
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Academic Editor: Francesco Gallerano
Water 2021, 13(3), 388; https://doi.org/10.3390/w13030388
Received: 22 December 2020 / Revised: 18 January 2021 / Accepted: 29 January 2021 / Published: 2 February 2021
(This article belongs to the Section Oceans and Coastal Zones)
The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean. View Full-Text
Keywords: artificial neural networks; subsurface temperatures; satellite measurements; North Pacific Ocean artificial neural networks; subsurface temperatures; satellite measurements; North Pacific Ocean
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MDPI and ACS Style

Cheng, H.; Sun, L.; Li, J. Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites. Water 2021, 13, 388. https://doi.org/10.3390/w13030388

AMA Style

Cheng H, Sun L, Li J. Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites. Water. 2021; 13(3):388. https://doi.org/10.3390/w13030388

Chicago/Turabian Style

Cheng, Hao, Liang Sun, and Jiagen Li. 2021. "Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites" Water 13, no. 3: 388. https://doi.org/10.3390/w13030388

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