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Open AccessArticle

A Statistical Algorithm for Estimating Chlorophyll Concentration in the New Caledonian Lagoon

1
Sciences and Technologies Department, University of New Caledonia, Nouville Campus BP R4, Nouméa CEDEX 98851, New Caledonia
2
Aix-Marseille University, CNRS/INSU, University of Toulon, IRD, Mediterranean Institute of Oceanography (MIO), UM 110, Marseille 13288, France
3
Institut de Recherche Pour le Développement (IRD), BP A5 98848 Nouméa CEDEX 98848, New Caledonia
4
University of Montpellier II, University of Reunion, University of French West Indies, University of French Guiana, IRD, ESPACE-DEV UMR 228, Montpellier 34093, France
5
LEGOS, University of Toulouse CNES, CNRS, IRD, UPS, Toulouse 31401, France
6
Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Stuart Phinn, Chris Roelfsema, Raphael M. Kudela, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2016, 8(1), 45; https://doi.org/10.3390/rs8010045
Received: 16 September 2015 / Revised: 26 November 2015 / Accepted: 10 December 2015 / Published: 7 January 2016
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
Spatial and temporal dynamics of phytoplankton biomass and water turbidity can provide crucial information about the function, health and vulnerability of lagoon ecosystems (coral reefs, sea grasses, etc.). A statistical algorithm is proposed to estimate chlorophyll-a concentration ([chl-a]) in optically complex waters of the New Caledonian lagoon from MODIS-derived “remote-sensing” reflectance (Rrs). The algorithm is developed via supervised learning on match-ups gathered from 2002 to 2010. The best performance is obtained by combining two models, selected according to the ratio of Rrs in spectral bands centered on 488 and 555 nm: a log-linear model for low [chl-a] (AFLC) and a support vector machine (SVM) model or a classic model (OC3) for high [chl-a]. The log-linear model is developed based on SVM regression analysis. This approach outperforms the classical OC3 approach, especially in shallow waters, with a root mean squared error 30% lower. The proposed algorithm enables more accurate assessments of [chl-a] and its variability in this typical oligo- to meso-trophic tropical lagoon, from shallow coastal waters and nearby reefs to deeper waters and in the open ocean. View Full-Text
Keywords: chlorophyll-a concentration; MODerate resolution Imaging Spectroradiometer (MODIS); ocean color; remote sensing; statistical algorithm; oligotrophic waters; New Caledonia; coral lagoon chlorophyll-a concentration; MODerate resolution Imaging Spectroradiometer (MODIS); ocean color; remote sensing; statistical algorithm; oligotrophic waters; New Caledonia; coral lagoon
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MDPI and ACS Style

Wattelez, G.; Dupouy, C.; Mangeas, M.; Lefèvre, J.; Touraivane; Frouin, R. A Statistical Algorithm for Estimating Chlorophyll Concentration in the New Caledonian Lagoon. Remote Sens. 2016, 8, 45. https://doi.org/10.3390/rs8010045

AMA Style

Wattelez G, Dupouy C, Mangeas M, Lefèvre J, Touraivane, Frouin R. A Statistical Algorithm for Estimating Chlorophyll Concentration in the New Caledonian Lagoon. Remote Sensing. 2016; 8(1):45. https://doi.org/10.3390/rs8010045

Chicago/Turabian Style

Wattelez, Guillaume; Dupouy, Cécile; Mangeas, Morgan; Lefèvre, Jérôme; Touraivane; Frouin, Robert. 2016. "A Statistical Algorithm for Estimating Chlorophyll Concentration in the New Caledonian Lagoon" Remote Sens. 8, no. 1: 45. https://doi.org/10.3390/rs8010045

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