A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms
We present a novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. The water sample and remote sensing reflectance data used for PC3 calibration and validation were acquired from highly turbid productive catfish aquaculture ponds. Since the characteristic PC absorption feature at 620 nm is contaminated with residual chlorophyll-a (Chl-a) absorption, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating Chl-a absorption at 620 nm–665 nm enables PC3 to compensate for the confounding effect of Chl-a at the PC absorption band and considerably increases the accuracy of the PC prediction algorithm. In the current dataset, PC3 produced the lowest mean relative error of prediction among all PC algorithms considered in this research. Moreover, PC3 eliminates the nonlinear sensitivity issue of PC algorithms particularly at high PC range (>100 μg L−1). Therefore, introduction of PC3 will have an immediate positive impact on studies monitoring inland and coastal cyanobacterial harmful algal blooms.