Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach.

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Date
2020Author
Martinez, Elodie
Gorgues, Thomas
Lengaigne, Matthieu
Fontana, Clement
Sauzède, Raphaëlle
Menkes, Christophe
Uitz, Julia
Di Lorenzo, Emanuele
Fablet, Ronan
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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 from a 32-
years global physical-biogeochemical simulation can generally be skillfully reproduced
with a SVR using the model surface variables as input parameters. We then apply
the SVR to reconstruct satellite Chl observat.....
Journal
Frontiers in Marine ScienceVolume
7Issue
Article 464Page Range
20pp.Document Language
enSustainable Development Goals (SDG)
14Essential Ocean Variables (EOV)
Phytoplankton biomass and diversityBest Practice Type
Manual (incl. handbook, guide, cookbook etc)DOI Original
10.3389/fmars.2020.00464Citation
Martinez, E.; Gorgues, T.; Lengaigne, M.; Fontana, C.; Sauzède, R.; Menkes, C.; Uitz, J.; Di Lorenzo, E. and Fablet, R. (2020) Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach. Frontiers in Marine Science, 7:464, 20pp. DOI: 10.3389/fmars.2020.00464Collections
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