Data/hora: 12/06/2024 17:43:30

Provedor de dados: | 5
País: | France
Título: | Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean
Autores: | Martinez, Elodie; Brini, Anouar; Gorgues, Thomas; Drumetz, Lucas; Roussillon, Joana; Tandeo, Pierre; Maze, Guillaume; Fablet, Ronan.
Data: | 2020-12
Ano: | 2020
Palavras-chave: | Phytoplankton time-series reconstruction; Ocean color; Neural networks; Support vector regression; Multi-layer perceptron; Physical predictors.
Resumo: | 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.
Tipo: | Text
Idioma: | Inglês

Identificador:

https://archimer.ifremer.fr/doc/00667/77871/80017.pdf

https://archimer.ifremer.fr/doc/00667/77871/80018.pdf

DOI:10.3390/rs12244156

https://archimer.ifremer.fr/doc/00667/77871/

Editor: | MDPI AG
Formato: | application/pdf
Fonte: | Remote Sensing (2072-4292) (MDPI AG), 2020-12 , Vol. 12 , N. 24 , P. 4156 (14p.)

Fonte:

info:eu-repo/semantics/openAccess

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