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A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms
We present a novel three-band algorithm (PC _3 ) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. The water sample and remote sensing reflectance data used for PC _3 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 PC _3 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, PC _3 produced the lowest mean relative error of prediction among all PC algorithms considered in this research. Moreover, PC _3 eliminates the nonlinear sensitivity issue of PC algorithms particularly at high PC range (>100 μ g L ^−1 ). Therefore, introduction of PC _3 will have an immediate positive impact on studies monitoring inland and coastal cyanobacterial harmful algal blooms.
A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms
We present a novel three-band algorithm (PC _3 ) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. The water sample and remote sensing reflectance data used for PC _3 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 PC _3 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, PC _3 produced the lowest mean relative error of prediction among all PC algorithms considered in this research. Moreover, PC _3 eliminates the nonlinear sensitivity issue of PC algorithms particularly at high PC range (>100 μ g L ^−1 ). Therefore, introduction of PC _3 will have an immediate positive impact on studies monitoring inland and coastal cyanobacterial harmful algal blooms.
A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms
S Mishra (author) / D R Mishra (author)
2014
Article (Journal)
Electronic Resource
Unknown
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