A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers

A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers

Time series of satellite-derived chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass), continuously generated since 1997, are still too short to investigate the low-frequency variability of phytoplankton biomass (e.g. decadal variability). Machine learning models such as Support Vecto...

Full description

Bibliographic Details
Main Authors: Joana Roussillon, Ronan Fablet, Thomas Gorgues, Lucas Drumetz, Jean Littaye, Elodie Martinez
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-03-01
Series:Frontiers in Marine Science
Subjects:
Convolutional Neural Networks
attention mechanisms
satellite ocean color
phytoplankton physical drivers
biogeochemical regions
neural networks interpretability
Online Access:https://www.frontiersin.org/articles/10.3389/fmars.2023.1077623/full

Internet

https://www.frontiersin.org/articles/10.3389/fmars.2023.1077623/full

Similar Items