|Title||A statistical algorithm for estimating chlorophyll concentration in the New Caledonian lagoon|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Wattelez G., Dupouy C., Mangeas M., Lefevre J.,, Frouin R.|
|Type of Article||Article|
|Keywords||a; 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|
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.