Improvement in Spatiotemporal Chl-a Data in the South China Sea Using the Random-Forest-Based Geo-Imputation Method and Ocean Dynamics Data

Improvement in Spatiotemporal Chl-a Data in the South China Sea Using the Random-Forest-Based Geo-Imputation Method and Ocean Dynamics Data

The accurate estimation of the spatial and temporal distribution of chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is crucial for understanding marine ecosystem dynamics and water quality assessment. However, the challenge of missing values in satellite-derived Chl-a data has hind...

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Bibliographic Details
Main Authors: Ao Li, Tiantai Shao, Zhen Zhang, Weiwei Fang, Wenjie Li, Jinrun Xu, Yujie Jiang, Chan Shu
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Journal of Marine Science and Engineering
Subjects:
South China Sea
chlorophyll-a
imputation
ocean dynamics data
machine learning
Online Access:https://www.mdpi.com/2077-1312/12/1/13

Internet

https://www.mdpi.com/2077-1312/12/1/13

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