Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

The MultiSpectral Instrument (MSI) on-board Sentinel-2 provides satellite images at spatiotemporal resolutions suitable for chlorophyll a (Chla) retrieval from inland and coastal waters. Machine-learning (ML) algorithms including light gradient boosting machine (LGBM) were employed for Chl a retriev...

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Bibliographic Details
Main Authors:	Young Woo Kim, TaeHo Kim, Jihoon Shin, Dae-Seong Lee, Young-Seuk Park, Yeji Kim, YoonKyung Cha
Format:	Article
Language:	English
Published:	Elsevier 2022-04-01
Series:	Ecological Indicators
Subjects:	MSI on-board Sentinel-2 Chlorophyll a Inland and coastal waters Machine learning Land cover Multiscale
Online Access:	http://www.sciencedirect.com/science/article/pii/S1470160X22002084

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