Comparative Evaluation of Machine Learning Models for Satellite Chlorophyll-a Gap Reconstruction in the Chesapeake Bay

Harmful algal blooms (HABs) are increasing in frequency in the Chesapeake Bay, posing risks to marine ecosystems, water quality, and public health. Chlorophyll-a (Chl-a) is a widely used indicator of algal biomass, and satellite observations such as Sentinel-3 Ocean and Land Color Instrument (OLCI)...

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主要な著者:	Rakshita Chidananda, Anusha Srirenganathan Malarvizhi, Samir Ahmed, Elena Zhang, Chaowei Phil Yang
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2026-05-01
シリーズ:	Remote Sensing
主題:	harmful algal blooms chlorophyll-a gap filling machine learning remote sensing Chesapeake Bay
オンライン･アクセス:	https://www.mdpi.com/2072-4292/18/11/1736
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