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20 August 2009 Using a partial least squares (PLS) method for estimating cyanobacterial pigments in eutrophic inland waters
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Midwestern lakes and reservoirs are commonly exposed to anthropogenic eutrophication. Cyanobacteria thrive in these nutrient rich-waters and some species pose three threats: 1) taste & odor (drinking), 2) toxins (drinking + recreational) and 3) water treatment process disturbance. Managers for drinking water production are interested in the rapid identification of cyanobacterial blooms to minimize effects caused by harmful cyanobacteria. There is potential to monitor cyanobacteria through the remote sensing of two algal pigments: chlorophyll a (CHL) and phycocyanin (PC). Several empirical methods that develop spectral parameters (e.g., simple band ratio) sensitive to these two pigments and map reflectance to the pigment concentration have been used in a number of investigations using field-based spectroradiometers. This study tests a multivariate analysis approach, partial least squares (PLS) regression, for the estimation of CHL and PC. PLS models were trained with 35 spectra collected from three central Indiana reservoirs during a 2007 field campaign with dual-headed Ocean Optics USB4000 field spectroradiometers (355 - 802 nm, nominal 1.0 nm intervals), and CHL and PC concentrations of the corresponding water samples analyzed at Indiana University-Purdue University at Indianapolis. Validation of these models with 19 remaining spectra show that PLS (CHL R2=0.90, slope=0.91, RMSE=20.61 μg/L; PC R2=0.65, slope=1.15, RMSE=23.04. μg/L) performed equally well to the band tuning model based on Gitelson et al. 2005 (CHL: R2=0.75, slope=0.84, RMSE=40.16 μg/L; PC: R2=0.59, slope=1.14, RMSE=20.24 μg/L).
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. L. Robertson, L. Li, L. Tedesco, J. Wilson, and E. Soyeux "Using a partial least squares (PLS) method for estimating cyanobacterial pigments in eutrophic inland waters", Proc. SPIE 7454, Remote Sensing and Modeling of Ecosystems for Sustainability VI, 745408 (20 August 2009);

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