10 December 2014 A statistical algorithm for estimating chlorophyll concentration from MODIS data
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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.
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Guillaume Wattelez, Cécile Dupouy, Morgan Mangeas, Jérôme Lèfevre, T. Touraivane, Robert J. Frouin, "A statistical algorithm for estimating chlorophyll concentration from MODIS data", Proc. SPIE 9261, Ocean Remote Sensing and Monitoring from Space, 92611S (10 December 2014); doi: 10.1117/12.2086297; https://doi.org/10.1117/12.2086297
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