Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach.
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Di Lorenzo, Emanuele
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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.....
JournalFrontiers in Marine Science
Sustainable Development Goals (SDG)14
Essential Ocean Variables (EOV)Phytoplankton biomass and diversity
Best Practice TypeManual (incl. handbook, guide, cookbook etc)
CitationMartinez, 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.2020.00464
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