Martinez Elodie, Brini A., Gorgues Thomas, Drumetz L., Roussillon J., Tandeo P., Maeze G., Fablet R. (2020). Neural network approaches to reconstruct phytoplankton time-series in the global ocean [+ Correction 2022, art. 5669, 7 p.]. Remote Sensing, 12 (24), art. 4156 [14 p.] [+ Correction 2022, art. 5669, 7 p.].
Titre du document
Neural network approaches to reconstruct phytoplankton time-series in the global ocean [+ Correction 2022, art. 5669, 7 p.]
Année de publication
2020
Auteurs
Martinez Elodie, Brini A., Gorgues Thomas, Drumetz L., Roussillon J., Tandeo P., Maeze G., Fablet R.
Source
Remote Sensing, 2020,
12 (24), art. 4156 [14 p.] [+ Correction 2022, art. 5669, 7 p.]
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.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Limnologie physique / Océanographie physique [032]
;
Ecologie, systèmes aquatiques [036]
;
Télédétection [126]
Description Géographique
MONDE
Localisation
Fonds IRD [F B010080581]
Identifiant IRD
fdi:010080581
