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| Martinez, Elodie; Brini, Anouar; Gorgues, Thomas; Drumetz, Lucas; Roussillon, Joana; Tandeo, Pierre; Maze, Guillaume; Fablet, Ronan. |
| Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific... |
| Tipo: Text |
Palavras-chave: Phytoplankton time-series reconstruction; Ocean color; Neural networks; Support vector regression; Multi-layer perceptron; Physical predictors. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00667/77871/80017.pdf |
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