title: Remote estimation of cyanobacteria-dominance in inland waters.
abstract: Remote sensing of the concentration ratio of phycocyanin (PC) to chlorophyll a (Chl-a) is important for water management, as it provides critical knowledge regarding the phytoplankton community. Using the observed in situ datasets, a simple empirical model was developed to estimate PC:Chl-a based on the band ratio index of R(rs)(550R(rs(620) (R(rs-): remote sensing reflectance) (R(2) = 0.84; RMSE = 1.01). This simple model exhibited relatively high validation accuracy using the independent validation dataset. In addition, the model can be successfully applied to AISA (Airborne Imaging Spectrometer for Application) image data, indicating the practicality of the developed model for determining the dominance of cyanobacteria among the total phytoplankton from airborne image data in inland waters. However, the present model cannot be used directly to estimate PC:Chl-a in extremely turbid waters with total suspended matter (TSM) concentrations higher than 25 mg/l. For these waters, the model parameters may require local optimization according to the conditions of the water under analysis. The findings of this study indicate that our proposed model is able to detect the dominance of cyanobacteria among the phytoplankton in inland waters, where the turbidity is not too much high. This study improves our understanding of the species composition of phytoplankton biomass in optically complex inland bodies of water.
authors.affiliation: Taihu Lake Laboratory Ecosystem Station, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Jiangsu 210008, China.
authors.forename: Kun
authors.initials: K
authors.lastname: Shi
authors.forename: Yunlin
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yunmei
authors.initials: Y
authors.lastname: Li
authors.forename: Lin
authors.initials: L
authors.lastname: Li
authors.forename: Heng
authors.initials: H
authors.lastname: Lv
authors.forename: Xiaohan
authors.initials: X
authors.lastname: Liu
mesh_terms: D002681:China; D000458:Cyanobacteria / Q000502:physiology*; D004784:Environmental Monitoring / Q000379:methods* / Q000592:standards; D007196:Indiana; D060106:Lakes / Q000382:microbiology*; D008962:Models, Theoretical; D010839:Phytoplankton / Q000382:microbiology*; D058998:Remote Sensing Technology; D014871:Water Microbiology