Classification-informed estimation: the role of water-type clustering to improve neural network generalization for salinity and temperature estimation in coastal waters

White, Solomon ORCID: https://orcid.org/0000-0002-8714-8635; Medina-Lopez, Encarni; Silva, Tiago; Spyrakos, Evangelos; Amoudry, Laurent; Martin, Adrien. 2025 Classification-informed estimation: the role of water-type clustering to improve neural network generalization for salinity and temperature estimation in coastal waters. Environmental Data Science, 4. 10.1017/eds.2025.10005

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Text © The Author(s), 2025. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-Non Commercial licence (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use. 1-s2.0-S0012821X25002900-main.pdf - Published Version Available under License Creative Commons Attribution Non-commercial 4.0. Download (7MB) | Preview

Abstract/Summary

Sea surface salinity and temperature are essential climate variables in monitoring and modeling ocean health. Multispectral ocean color satellites allow the estimation of these properties at a resolution of 10 to 300 m, which is required to correctly represent their spatial variability in coastal waters. This paper investigates the effect of pre-applying an unsupervised classification in the performance of both temperature and salinity inversion. Two methodologies were explored: clustering based solely on spectral radiances, and clustering applied directly to satellite images. The former improved model generalization by identifying similar water clusters across different locations, reducing location dependency. It also demonstrated results correlating cluster type with salinity and temperature distributions thereby enhancing regression model performance and improving a global ocean color sea surface temperature regression model RMSE error by 10%. The latter approach, applying clustering directly to satellite images, incorporated spatial information into the models and enabled the identification of front boundaries and gradient information, improving global sea surface temperature models RMSE by 20% and sea surface salinity models by 30%, compared to the initial ocean color model. Beyond improving algorithm performance, optical water classification can be used to monitor and interpret changes to water optics, including algal blooms, sediment disturbance or other climate change or antropogenic disturbances. For example, the clusters have been used to show the impact of a category 4 hurricane landfall on the Mississippi estuarine region.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1017/eds.2025.10005
ISSN:	2634-4602
Additional Keywords:	classification, machine learning, oceanography, remote sensing, segmentation
Date made live:	06 Jul 2025 18:58 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539795

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