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Abstract
Remote sensing techniques can offer powerful tools for measuring concentrations of chlorophyll-a (chl-a), which is an important proxy for water quality. However, remote estimates of chl-a can be difficult in water bodies that have high levels of total suspended matter (TSM). In this study, we examined the applicability of the synthetic chlorophyll index (SCI) and a parameter relevant to chlorophyll pigments (Hchl) used in conjunction with remote-sensing data to predict chl-a concentrations (Cchl-a) in Taihu Lake, a highly turbid hypereutrophic lake in eastern China. We sampled water quality and surface spectral properties at 250 field stations throughout the lake over five sampling periods spanning 2 years. Because data acquired at 31 stations could not be used due to equipment failure or blue-green algal blooms, we used data acquired at the remaining 219 stations. We then randomly selected parts of the spectral properties data (N = 164) to calibrate bands used in the SCI algorithm and established cubic polynomial models to estimate Cchl-a with SCI and Hchl as the independent variables. We evaluated the accuracy of these models using data from the remaining 55 stations that were not used for calibration. Our results showed the following trends: (1) the parameter of Hchl performed better than SCI in estimating Cchla in Taihu Lake; (2) Hchl showed optimal performance in winter, average performance in spring, and poor performance in summer and autumn; (3) Hchl was appropriate for the NAP-dominant waters with high CTSM and low Cchl-a, but was not suitable for organism-dominant waters with low CTSM; and (4) in short, Hchl had limited usability in turbid and eutrophic waters.
Acknowledgements
We would like to express our gratitude to the editors and reviewers for their useful comments and suggestions.
Funding
The work was jointly supported by National Natural Science Foundation of China [40901174], [41001205], [41001206]; National High Technology Research and Development Programme of China [2013AA12A302]; and Key Projects in the National Science & Technology Pillar Programme [2011BAH12B07].