Detection of Suspended-Matter Concentrations in the Shallow Subtropical Lake Taihu, China, Using the SVR Model Based on DSFs

Accurate detection of suspended-matter concentrations in water columns is an important task in remotely sensing water color. This letter aims to identify an optimal model for estimating suspended-matter concentration in the optically complex Lake Taihu of China. Remote sensing reflectance R(rs)(lambda), inherent optical properties, and constituent concentrations of the Lake water were synchronously measured in November of 2007. After the effects of water constituents on R(rs)(lambda) were analyzed, the definitive spectral factors were determined, which are indicative primarily of total suspended matter (TSM). Several methods were compared in modeling the relationship between R(rs)(lambda) and TSM. Results show that the support vector regression (SVR) model performs best with a root-mean-square error of 4.7 mg . l(-1) (R(2) = 0.968). Its predictive errors in four seasons were also assessed with the mean absolute percentage errors varying in the range of 22.0%-60.0%. Thus, the SVR model can be used to reliably retrieve TSM concentrations in Lake Taihu. This finding offers new insights into the optical signals of in-water constituents in optically complex lakes.