Comparison of Machine Learning Approaches for Reconstructing Sea Subsurface Salinity Using Synthetic Data

Comparison of Machine Learning Approaches for Reconstructing Sea Subsurface Salinity Using Synthetic Data

There is a growing interest in using sparse in situ salinity data to reconstruct high-resolution three-dimensional subsurface salinity with global coverage. However, in areas with no observations, there is a lack of observation data for comparison with reconstructed fields, leading to challenges in...

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Bibliographic Details
Main Authors: Tian Tian, Hongze Leng, Gongjie Wang, Guancheng Li, Junqiang Song, Jiang Zhu, Yuzhu An
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Remote Sensing
Subjects:
CNRM-CM6-1-HR model
synthetic data
ocean salinity
machine learning
data reconstruction
remote sensing
Online Access:https://www.mdpi.com/2072-4292/14/22/5650

Internet

https://www.mdpi.com/2072-4292/14/22/5650

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