Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water

Abstract

The pigment phycocyanin (PC) is a marker for cyanobacterial presence in eutrophic inland water. We present a reflectance band-ratio algorithm for retrieval of cyanobacterial PC. The model conforms to the band settings of the Medium Resolution Imaging Spectrometer. The parameters of the algorithm were optimized using reflectance and absorption data from two highly eutrophic lakes. Using measured specific absorption coefficients for PC [a (620)] * pc for every sample, the error in the predicted PC concentrations was 19.7% (r 2 ϭ 0.94, n ϭ 34) for measured PC concentrations up to 80 mg m Ϫ3 . Applying a fixed value of a (620) caused an overestimation of the PC content * pc that increased toward lower PC concentrations. The PC prediction best matched observed values during periods of high relative abundance of cyanobacteria in the plankton community. The results suggest strong seasonal variation in a (620). The presence of pigments other than PC and chlorophyll a (Chl a) and a variable influence of Chl a * pc on retrieved absorption at 620 nm are potential causes of errors in PC retrieval. The algorithm in its current form is considered to be suitable for detection of the PC concentration in turbid, cyanobacteria-dominated waters.Eutrophic inland waters often exhibit blooms of cyanobacteria. Notorious for their negative impact on water quality, cyanobacterial blooms have been increasingly subject of water management and scientific studies. The hazards of toxic cyanobacterial blooms call for frequent and rapid monitoring of water bodies. Remote sensing provides insights into the distribution of blooms for a large number of lakes or reservoirs simultaneously. The concentration of chlorophyll a (Chl a) as a general indicator for plankton biomass can be assessed using imagery from a wide range of air-and spaceborn sensors (Vos et al. 2003). Recent advances in spaceborn remote sensing technology broaden the perspectives of monitoring toward the identification and quantification of plankton groups. Algorithms for the retrieval of Chl a from turbid water reflectance were already being developed (Gons et al. 2002). Now, the retrieval of the pigment phycocyanin (PC), which is characteristic of the presence of cyanobacterial, is being attempted. It is known that the presence of PC can be detected from spectral reflectance (Dekker et al.

Extraction methods for phycocyanin determination in freshwater filamentous cyanobacteria and their application in a shallow lake

“…According to Simis et al (2005), 6 ml of phosphate buffer was enough to achieve maximum extraction in the concentration range of 2−50 µg l −1 . However, our study aimed to clarify whether this method was useful not only in oligotrophic lakes, but in hypertrophic waters dominated by blue-green algae.…”

“…Seppälä et al, 2007), sensitive fluorometric techniques (Gregor & Maršálek, 2005), remote sensing (e.g. Simis et al, 2005;Hunter et al, 2008aHunter et al, , 2008b, and in vitro extraction methods have all been used for cyanobacterial quantification (Sarada et al, 1999). A number of studies have been published on phycocyanin extraction methods and their comparative analyses (Zhu et al, 2007;Lawrenz et al, 2011;Zimba, 2012); however there is no standard protocol for the maximal extraction of phycocyanin from cyanobacteria cells.…”

Optical Spectra of Phytoplankton Cultures for Remote Sensing Applications: Focus on Harmful Algal Blooms

“…There are ongoing efforts to refine the monitoring of HABs into determining the dominant taxa of the bloom. Simis et al [5] used the strong absorption of phycocyanin as a bioindicator of cyanobacteria dominated blooms. Kutser et al [6] clearly showed the placement of specific optical wavelength bands on the multi-spectral sensor MERIS (medium resolution imaging spectrometer) mounted on ESA's ENVISAT enables routine monitoring of phytoplankton blooms and identifying if the blooms are cyanobacteria dominated.…”

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

“…The advances in optical sensor design, bio-optical algorithms and validation methods have also supported the retrieval of a wide range of new water quality parameters (e.g. phycocyanin, particulate organic carbon, phytoplankton size distribution) (Simis et al, 2005;Nair et al, 2008;Allison et al, 2010;Ciotti & Bricaud 2010;Hunter et al, 2010;Brewin et al, 2011;Sun et al, 2013).…”