Learning and inferring the diurnal variability of cyanobacterial blooms from high-frequency time-series satellite-based observations.

Li, Hu; Qin, Chengxin; He, Weiqi; Sun, Fu; Du, Pengfei

Li, Hu; Qin, Chengxin; He, Weiqi; Sun, Fu; Du, Pengfei.

Afiliación

Li H; State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing China.
Qin C; State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing China.
He W; Research Institute of Environmental Innovation (Suzhou), Tsinghua University, 215163, Suzhou China. Electronic address: heweiqi@tsinghua.edu.cn.
Sun F; State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing China.
Du P; State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing China. Electronic address: dupf@tsinghua.edu.cn.

Harmful Algae ; 123: 102383, 2023 03.

Article en En | MEDLINE | ID: mdl-36894206

Asunto(s)

Cianobacterias; Factores de Tiempo

Palabras clave

CyanoHABs; Diurnal variability; High-frequency remote sensing; Learning and inferring; Spatiotemporal deep learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cianobacterias Tipo de estudio: Prognostic_studies Idioma: En Revista: Harmful Algae Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos

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