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).