Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters

A multi-spectral classification and quantification technique is developed for estimating chlorophyll a concentrations, Chl, in shallow oceanic waters where light reflected by the bottom can contribute significantly to the above-water remote-sensing reflectance spectra, Rrs(λ). Classification criteria for determining bottom reflectance contributions for shipboard Rrs(λ) data from the west Florida shelf and Bahamian waters (1998–2001; n = 451) were established using the relationship between Rrs(412)/Rrs(670) and the spectral curvature about 555 nm, [Rrs(412) ⁎ Rrs(670)]/Rrs(555)2. Chlorophyll concentrations for data classified as “optically deep” and “optically shallow” were derived separately using best-fit cubic polynomial functions developed from the band-ratios Rrs(490)/Rrs(555) and Rrs(412)/Rrs(670), respectively. Concentrations for transitional data were calculated from weighted averages of the two derived values. The root-mean-square error (RMSElog10) calculated for the entire data set using the new technique was 14% lower than the lowest error derived using the best individual band-ratio. The standard blue-to-green, band-ratio algorithm yields a 26% higher RMSElog10 than that calculated using the new method. This study demonstrates the potential of quantifying chlorophyll a concentrations more accurately from multi-spectral satellite ocean color data in oceanic regions containing optically shallow waters.