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Proceedings Paper

A statistical algorithm for estimating chlorophyll concentration from MODIS data
Author(s): Guillaume Wattelez; Cécile Dupouy; Morgan Mangeas; Jérôme Lèfevre; T. Touraivane; Robert J. Frouin
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Paper Abstract

We propose a statistical algorithm to assess chlorophyll-a concentration ([chl-a]) using remote sensing reflectance (Rrs) derived from MODerate Resolution Imaging Spectroradiometer (MODIS) data. This algorithm is a combination of two models: one for low [chl-a] (oligotrophic waters) and one for high [chl-a]. A satellite pixel is classified as low or high [chla] according to the Rrs ratio (488 and 555 nm channels). If a pixel is considered as a low [chl-a] pixel, a log-linear model is applied; otherwise, a more sophisticated model (Support Vector Machine) is applied. The log-linear model was developed thanks to supervised learning on Rrs and [chl-a] data from SeaBASS and more than 15 campaigns accomplished from 2002 to 2010 around New Caledonia. Several models to assess high [chl-a] were also tested with statistical methods. This novel approach outperforms the standard reflectance ratio approach. Compared with algorithms such as the current NASA OC3, Root Mean Square Error is 30% lower in New Caledonian waters.

Paper Details

Date Published: 10 December 2014
PDF: 15 pages
Proc. SPIE 9261, Ocean Remote Sensing and Monitoring from Space, 92611S (10 December 2014); doi: 10.1117/12.2086297
Show Author Affiliations
Guillaume Wattelez, Univ. de la Nouvelle-Calédonie (New Caledonia)
Cécile Dupouy, Institut de Recherche pour le Développement (New Caledonia)
Morgan Mangeas, ESPACE-DEV, IRD (New Caledonia)
Jérôme Lèfevre, LEGOS, IRD (New Caledonia)
T. Touraivane, Univ. de la Nouvelle-Calédonie (New Caledonia)
Robert J. Frouin, Scripps Institution of Oceanography, Univ. of California San Diego (United States)


Published in SPIE Proceedings Vol. 9261:
Ocean Remote Sensing and Monitoring from Space
Robert J. Frouin; Delu Pan; Hiroshi Murakami, Editor(s)

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