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A statistical algorithm for estimating chlorophyll concentration in the New Caledonian lagoon
Abstract : 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 (R rs). 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 R rs 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.
Cited literature [40 references]
https://hal.ird.fr/ird-01253540
Contributor : Isabelle Gasser <>
Submitted on : Monday, January 11, 2016 - 5:58:40 AM
Last modification on : Monday, June 29, 2020 - 11:02:03 PM
Document(s) archivé(s) le : Tuesday, April 12, 2016 - 11:03:54 AM
Contributor : Isabelle Gasser <>
Submitted on : Monday, January 11, 2016 - 5:58:40 AM
Last modification on : Monday, June 29, 2020 - 11:02:03 PM
Document(s) archivé(s) le : Tuesday, April 12, 2016 - 11:03:54 AM
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remotesensing-08-00045.pdf
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