A novel support vector regression model to estimate the phycocyanin concentration in turbid inland waters from hyperspectral reflectance

This study develops a novel support vector regression (SVR) model for retrieving the specific cyanobacterial pigment C-phycocyanin (C-PC) concentrations in cyanobacteria-dominated large turbid lakes of China. Lake Taihu, Lake Chaohu, and Lake Dianchi in China were our study areas. Five field cruises were carried out to collect data sets of optical and water quality parameters. To retrieve the C-PC, three types of reflectance forms, including single band, band ratio, and three-band-combination, were compared. The band ratio was the best candidate to serve for algorithm development. On this basis, two types of models, including linear models and a SVR model, were originally established. The previous typical algorithms were also examined. The obtained results showed that the best-performing model was the SVR model. By our validation data set, the proposed SVR model also presented accurate prediction results, with the lowest errors among all methods. The novelty of the SVR model compared to the previous ones lies in the inclusion of band ratios that are located outside of the main pigment absorption peaks but hold information on inflection points, curvature, etc., into empirical optimization. The implications of these findings indicates the potential applicability of the SVR models in lakes of the similar type.