A Statistical Algorithm for Estimating Chlorophyll Concentration in the New Caledonian Lagoon

Remote Sensing

Guillaume Wattelez³  Cຜile Dupouy¹  Morgan Mangeas²  Jérôme Lefèvre²  rib-type="author">Touraivane1Touraivane³  Robert Frouin⁴  Stuart Phinn⁵  Chris Roelfsema⁵  Raphael M. Kudela⁵  Xiaofeng Li⁵ 

[1] Aix-Marseille University, CNRS/INSU, University of Toulon, IRD, Mediterranean Institute of Oceanography (MIO), UM 110, Marseille 13288, France;Institut de Recherche Pour le Développement (IRD), BP A5 98848 Nouméa CEDEX 98848, New Caledonia;Sciences and Technologies Department, University of New Caledonia, Nouville Campus BP R4, Nouméa CEDEX 98851, New Caledonia;Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA;;Sciences and Technologies Department, University of New Caledonia, Nouville Campus BP R4, Nouméa CEDEX 98851, New Caledonia

关键词: chlorophyll-a concentration;    MODerate resolution Imaging Spectroradiometer (MODIS);    ocean color;    remote sensing;    statistical algorithm;    oligotrophic waters;    New Caledonia;    coral lagoon;   

DOI  :  10.3390/rs8010045

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2016 by the authors; licensee MDPI, Basel, Switzerland.