^a StateKey Laboratory of Lake Science and Environment, Nanjing Institute ofGeography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China

^b Collegeof Marine Science, University of South Florida, 140 Seventh Avenue, South, St.Petersburg, FL 33701, USA

Volume 126, November 2012, Pages 126–135

 Using reflectance data and water sample data from 75 stations in several eutrophiclakes of East China (Lake Taihu, Lake Gehu, Lake Dongjiu) between 23 April and 3 May2010, we evaluated several recently proposedremote sensing algorithms to estimate chlorophyll-a concentrations (Chla, 1.0 –42 μg/L) and phycocyanin pigment concentrations (PC, 0.1 – 7.7 μg/L). Theselakes experience phytoplankton blooms of the cyanobacteria Microcystis aeruginosa every year due to eutrophication. It was found that after local tuning ofthe algorithm parameterizations,most algorithms yielded acceptable results for Chla retrievals while accuratePC retrievals were more challenging due to changing speciescomposition (PC:Chla ratios) and low PC concentrations. For the dataranges in the study region, the best Chla algorithm yieldedRMSE_rel (RelativeRoot MeanSquare Error) of ~46% (R²=0.92,n=75)and the best PC algorithm yielded RMSE_relof ~83% (R²=0.88, n=75). Based on these observations,it is recommended that local tuning of algorithm parametersshould be performed for remote sensingapplications, and future efforts should emphasize on application of the algorithms to satellite data.