Validation of a synthetic chlorophyll index for remote estimates of chlorophyll-a in a turbid hypereutrophic lake

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 (H_chl) used in conjunction with remote-sensing data to predict chl-a concentrations (C_chl-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 C_chl-a with SCI and H_chl 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 H_chl performed better than SCI in estimating C_chla in Taihu Lake; (2) H_chl showed optimal performance in winter, average performance in spring, and poor performance in summer and autumn; (3) H_chl was appropriate for the NAP-dominant waters with high C_TSM and low C_chl-a, but was not suitable for organism-dominant waters with low C_TSM; and (4) in short, H_chl had limited usability in turbid and eutrophic waters.