Martinez Elodie auteur aut IRD Brini A. auteur aut IRD Gorgues Thomas auteur aut IRD Drumetz L. auteur aut IRD Roussillon J. auteur aut IRD Tandeo P. auteur aut IRD Maeze G. auteur aut IRD Fablet R. auteur aut IRD text journalArticle eng text/pdf reformatted digital access 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 behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP outperforms the SVR to capture satellite Chl (correlation of 0.6 vs. 0.17 on a global scale, respectively) along with its seasonal and interannual variability, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series. specialized phytoplankton time-series reconstruction ocean color neural networks support vector regression multi-layer perceptron physical predictors MONDE 032 036 020 126 12 24 art. 4156 [14 ] [+ Correction 2022, art. 5669, 7 p.] 2020 https://www.documentation.ird.fr/hor/fdi:010080581 10.3390/rs12244156 [F B010080581] https://www.documentation.ird.fr/hor/fdi:010080581 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers21-01/010080581.pdf IRD - Base Horizon / Pleins textes 2021-02-08 2025-02-24 fdi:010080581 fre