Abstract
This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River.
Highlights
Cyanobacteria, known as blue-green algae, are the most common organisms that form blooms in eutrophic inland waters
Unlike the other algorithms, applying Band-Ratio and Peak-Distance (BRPD) produced a relative error of less than 1 in the low PC concentration ranges. We inferred that such an improvement was caused by the exponential component of the equation in the BRPD algorithm, which clearly differentiates between high and low PC concentrations based on the distance at which the peak appears
Most algorithms used for estimating PC concentrations are known to have low accuracy at concentrations below 50 mg/m3 [12]
Summary
Cyanobacteria, known as blue-green algae, are the most common organisms that form blooms in eutrophic inland waters. Increasing incidence of cyanobacteriadominated blooms owing to the increase in anthropogenic nutrient inputs and perhaps enhanced by climate warming has become a critical global issue [4,5]. This problem can be more serious in countries such as the Republic of Korea, which depend heavily on surface water resources for their drinking water. Traditional methods to identify cyanobacterial profusion include cell counting under a microscope, high-pressure liquid chromatography (HPLC), and fluorometric methods. These methods provide relatively accurate results, but are labor-intensive and inadequately cover spatio-temporally heterogeneous algal blooms [6,7].
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