Seasonal-spatial variation and remote sensing of phytoplankton absorption in Lake Taihu, a large eutrophic and shallow lake in China

Shallow water, strong sediment resuspension, complex river inputs and frequent cyanobacterial blooms characterize the waters of Lake Taihu. In such shallow, eutrophic lakes, the remote sensing of phycocyanin (PC), a characteristic pigment of cyanobacteria, is dependent on the estimation precision of phytoplankton absorption. For Lake Taihu, we monitored the seasonal-spatial variation of phytoplankton absorption, and a three-band model was calibrated and validated to estimate phytoplankton absorption (a(ph)(665)) from a dataset of the spatial and temporal patterns of the bio-optical properties collected, during five cruises in January (winter), April (spring), July (summer), and October (autumn) in 2006 to 2007. Two distinct situations prevailed; in winter tripton strongly predominated over particulate matter, and in spring-summer-autumn phytoplankton made an important contribution. In winter, meteorology mainly determined the bio-optical properties of the water column, whereas in the spring-summer-autumn the biological activity was an additional active factor. The three-band remote sensing model >: remote sensing reflectance) of a(ph)(665) was calibrated and validated, and its performance was compared and assessed with the published band-ratio method. With the three-band model, the root mean square error and mean relative error were 0.150 m(-1) (50.5% accounting for the mean value) and 45.7% respectively; with the published band-ratio method, the values were 0.290 m(-1) (97.3% accounting for the mean value) and 213.0% respectively, based on an independent validation dataset. Furthermore, the three-band and band-ratio models worked well in estimating phytoplankton absorption with simulated MERIS bands data with higher precision for the three-band model in Lake Taihu. The result showed that the three-band model was superior to the published band-ratio method, and thus the former can be used to improve the estimation precision of remote sensing of PC.