%0 Book Section
%9 OS CH : Chapitres d'ouvrages scientifiques
%A Wattelez, G.
%A Dupouy, Cécile
%A Mangeas, Morgan
%A Lefèvre, Jérôme
%A Touraivane, T.
%A Frouin, R.
%T A statistical algorithm for estimating chlorophyll concentration from MODIS data
%B Ocean remote sensing and monitoring from space
%C Bellingham WA
%D 2014
%E Frouin, R.J.
%E Pan, D.
%E Murakami, H.
%L fdi:010063838
%G ENG
%I SPIE
%K TELEDETECTION SPATIALE ; DONNEES SATELLITE ; ALGORITHME ; REFLECTANCE ; CHLOROPHYLLE ; LAGON
%K COULEUR DE L'OCEAN ; OLIGOTROPHIE ; MODIS
%K NOUVELLE CALEDONIE ; ZONE TROPICALE
%N 92611S
%P 92611S/1-92611S/15
%R 10.1117/12.2086297
%U https://www.documentation.ird.fr/hor/fdi:010063838
%> https://www.documentation.ird.fr/intranet/publi/depot/2015-07-23/010063838.pdf
%V 9261
%W Horizon (IRD)
%X 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.
%S Proceedings of SPIE
%B SPIE Remote Sensing Conference
%8 2014/10/13
%$ 126TELAPP05