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| Martinez, Elodie; Gorgues, Thomas; Lengaigne, Matthieu; Fontana, Clement; Sauzède, Raphaëlle; Menkes, Christophe; Uitz, Julia; Di Lorenzo, Emanuele; Fablet, Ronan. |
| Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability... |
| Tipo: Text |
Palavras-chave: Machine learning; Phytoplankton variability; Satellite ocean color; Decadel variability; Global scale. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00641/75314/75810.pdf |
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