A novel method to estimate the 3D chlorophyll a distribution in the South China Sea surface waters using hydrometeorological parameters

Abstract Chlorophyll a (Chl-a) is a key indicator of marine ecosystems, and certain hydro-meteorological parameters (HMPs) are highly correlated with its fluctuations. Here, relevant and accessible HMPs were used as inputs, combined with machine learning (ML) algorithms for estimating 3D Chl-a in th...

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Главные авторы:	Yuanning Zheng, Cai Li, Wen Zhou, Zhantang Xu, Xianqing Zhang, Wenxi Cao, Zeming Yang, Changjian Liu
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Nature Portfolio 2024-10-01
Серии:	Scientific Reports
Предметы:	Water quality monitoring Machine learning Chlorophyll a Hydro-meteorology South China Sea
Online-ссылка:	https://doi.org/10.1038/s41598-024-76748-5
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