Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks-Reference-Cited by-同舟云学术

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Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks

Published:2021-07 Issue: Volume:260 Page:112465
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Su Hua,Zhang Tianyi,Lin Mengjing,Lu Wenfang,Yan Xiao-Hai

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference54 articles.

1. Estimation of ocean subsurface thermal structure from surface parameters: a neural network approach;Ali;Geophys. Res. Lett.,2004

2. Towards quantifying uncertainty in ocean heat content changes using synthetic profiles;Allison;Environ. Res. Lett.,2019

3. Distinctive climate signals in reanalysis of global ocean heat content;Balmaseda;Geophys. Res. Lett.,2013

4. Salinity profile estimation in the Pacific Ocean from satellite surface salinity observations;Bao;J. Atmos. Ocean. Technol.,2019

5. DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations;Barth;Geosci. Model Dev.,2020

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