SPIE Journal Paper | 3 April 2012
Stefani Novoa, Guillem Chust, Caroline Petus, Javier Franco, Ángel Borja, Jean-Marie Froidefond, Emma Orive, Sergio Seoane
KEYWORDS: Algorithm development, Water, Magnesium, In situ metrology, Reflectivity, Ocean optics, Sensors, Luminescence, Remote sensing, Mouth
Accurate estimation of chlorophyll-a (chl-a), a proxy of the eutrophication risk, is necessary in coastal areas for the assessment of water quality in accordance with European Directives. Local parameterization of remote sensing algorithms is useful to cope with the variability and specificity of optically-active in-water constituents. Using the Bay of Biscay coastal waters, affected by Basque river runoffs, as a case study, the objectives of this investigation are to: 1. develop an empirical algorithm to estimate water surface chl-a for the optically-complex Basque coastal waters; 2. explore the influence of suspended matter, phytoplankton species, and pigment content on the algorithm developed for medium resolution imaging spectrometer instrument (MERIS) imagery; 3. compare the local algorithm to three ocean color algorithms (OC4v6, Gitelson's algorithm, and the OC5); and 4. apply the local algorithm to the MERIS images. For this purpose, two surveys were undertaken within the study area, the Batel-1 survey in 2007, and the Batel-2, in 2009. The empirical algorithm was developed with remote sensing reflectances (Rrs), undertaken with a TriOS field spectrometer, and chl-a measured in situ from the Batel-2 survey. The algorithm was not affected by different concentrations of suspended matter in surface waters, within the range from 0.0 to 6.6 g·m−3. There was no significant effect of 23 accessory pigments found in the area on the algorithm. Eighty-four Rrs and chl-a measurements from the Batel-1 survey were used to validate the local algorithm and to compare it with output of the other algorithms. The local algorithm provided the lowest root-mean-square difference (RMS = 1.7 mg·m−3), the best correlation with the observed data (R = 0.8), together with the best slope-intercept combination between predicted and observed chl-a (slope = 0.5, intercept = 0.6).
