A statistical algorithm for estimating chlorophyll concentration in the New Caledonian lagoon

TitleA statistical algorithm for estimating chlorophyll concentration in the New Caledonian lagoon
Publication TypeJournal Article
Year of Publication2016
AuthorsWattelez G., Dupouy C., Mangeas M., Lefevre J., , Frouin R.
JournalRemote Sensing
Volume8
Date Published2016/01
Type of ArticleArticle
ISBN Number2072-4292
Accession NumberWOS:000369494500026
Keywordsa; algorithm; chlorophyll-a concentration; coastal waters; concentrations; coral lagoon; events; great-barrier-reef; impact; MODerate resolution Imaging; New Caledonia; ocean color; oligotrophic waters; Remote sensing; retrieval; seawifs; south-west; Spectroradiometer (MODIS); statistical; vector machines
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.

DOI10.3390/rs8010045
Student Publication: 
No
sharknado