Cyanobacterial harmful algal blooms (CHABs) is a major water quality issue in surface water bodies because of its scum
and bad odor forming and toxin producing abilities. Terminations of blooms also cause oxygen depletion leading to
hypoxia and widespread fish kills. Therefore, continuous monitoring of CHABs in recreational water bodies and surface
drinking water sources is highly required for their early detection and subsequent issuance of a health warning and
reducing the economic loss. We present a comparative study between a modified quasi-analytical algorithm (QAA) and a
novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland
waters. An extensive dataset, consisting of radiometric measurements, absorption measurements of phytoplankton,
organic matter, detritus, and pigment concentration, was used to optimize the algorithms. The QAA algorithm isolates
the PC signal from the remote sensing reflectance data using a set of radiative transfer equations and retrieves PC
concentration in the water bodies through bio-optical inversion. Validation of the QAA algorithm, using an independent
dataset, produced a mean relative error (MRE) of 34%. For the PC3 algorithm, we propose a coefficient (ψ) for isolating
the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating chlorophyll-a (chla)
absorption at 620 nm to 665 nm enables PC3 to compensate for the confounding effect of chl-a and considerably
increases the accuracy of the PC prediction algorithm. The MRE of prediction for PC3 was 27%. Moreover, PC3
eliminates the nonlinear sensitivity issue of PC algorithms at high range.
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