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, R rs( λ). Classification crite...

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Bibliographic Details
Published in:	Remote sensing of environment Vol. 101; no. 1; pp. 13 - 24
Main Authors:	Cannizzaro, Jennifer Patch, Carder, Kendall L.
Format:	Journal Article
Language:	English
Published:	New York, NY Elsevier Inc 15-03-2006 Elsevier Science
Subjects:	Algorithm Animal, plant and microbial ecology Applied geophysics Biological and medical sciences Chlorophyll Earth sciences Earth, ocean, space Empirical Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Internal geophysics Marine geology Ocean color Remote sensing Shallow Teledetection and vegetation maps Florida United States Antilles West Indies Bahamas ASW, USA, Florida USA, Florida Chlorophyll Shallow Ocean color Algorithm Empirical Remote sensing algorithms color North America new methods quantitative analysis Reflected light Space remote sensing chlorophyll concentration classification Caribbean region Regression coefficient scientific cruises depth marine environment ocean Individual techniques standard samples Performance shallow-water environment errors standard deviation Reflectance
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Summary:	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, R rs( λ). Classification criteria for determining bottom reflectance contributions for shipboard R rs( λ) data from the west Florida shelf and Bahamian waters (1998–2001; n = 451) were established using the relationship between R rs(412)/ R rs(670) and the spectral curvature about 555 nm, [ R rs(412) ⁎ R rs(670)]/ R rs(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 R rs(490)/ R rs(555) and R rs(412)/ R rs(670), respectively. Concentrations for transitional data were calculated from weighted averages of the two derived values. The root-mean-square error (RMSE log10) 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 RMSE log10 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0034-4257 1879-0704
DOI:	10.1016/j.rse.2005.12.002