Location: Warmwater Aquaculture Research Unit
Title: Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: a quasi-analytical approach Authors
|Mishra, Sachidadanda -|
|Mishra, Depak -|
|Lee, Zhongping -|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 6, 2013
Publication Date: March 21, 2013
Citation: Mishra, S., Mishra, D.R., Lee, Z., Tucker, C.S. 2013. Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: a quasi-analytical approach. Remote Sensing of Environment. 113:141-151. Interpretive Summary: Some species of cyanobacteria, also known as blue-green algae, produce potent toxins that can kill birds, mammals, or fish that either ingest toxin-producing cells or absorb the toxin from water. Many cyanobacteria also produce intensely odorous compounds that cause earthy or musty odors and flavors in drinking water and fish. Because of health risks and economic impacts associated with cyanobacterial blooms, cost-effective monitoring solutions should be developed for early warning in the affected regions. Remote sensing is a more viable option for this kind of environmental monitoring because of low cost and the broad-scale nature of monitoring capabilities. Optical properties of phycocyanin, the characteristic cyanobacterial photosynthetic pigment, in visible and near-infrared wavelengths can be used to develop models to remotely quantify cyanobacterial biomass in natural waters. In this study, we developed a conceptual model to determine cyanobacterial phycocyanin concentration in turbid and productive waters and optimized and validated model parameters using measurements from productive aquaculture ponds. The newly developed model shows good potential of quantifying phycocyanin concentrations in optically complex turbid and productive waters.
Technical Abstract: In this research, we present a novel technique to monitor cyanobacterial algal bloom using remote sensing measurements. We have used a multi-band quasi analytical algorithm that determines phytoplankton absorption coefficients, aF('), from above-surface remote sensing reflectance, Rrs('). In situ data including remote sensing reflectance, phytoplankton pigment concentration, and absorption coefficients of optically active constituents in the water were collected from highly turbid and productive aquaculture ponds These shallow (<1.5 m) ponds in northwestern Mississippi, USA, were used for channel catfish Ictalurus punctatus aquaculture and had high nitrogen and phosphorus loading rates from manufactured feeds added to ponds to promote rapid fish growth. These practices resulted in high phytoplankton biomass (Chlorophyll-a concentrations = 59.4-1376.6 mg m-3) with communities dominated by filamentous, gas vacuolate cyanobacteria. A novel technique was developed to further decompose the aF to obtain phycocyanin absorption coefficient, apC, at 620 nm, a primary peak of phycocyanin absorption spectrum. Validation of the model produced a mean and median absolute relative error of 36.2% and 22.0%. Overall, the model performance was higher in the higher range of PC concentration (>150 µg l-1). Results demonstrate that the new approach will be suitable for quantifying phycocyanin concentration in cyanobacteria dominated turbid productive waters.