Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data, Remote Sensing of Environment
Padmanava Dash, Nan Walker, D. Mishra, C. Hu, J. Pickney, and Eurico D'Sa.
Cyanobacteria represent a major harmful algal group in fresh to brackish water environments. Lac des Allemands, a freshwater lake of 49 km2 southwest of NewOrleans, Louisiana on the upper end of the Barataria Estuary, provides a natural laboratory for remote characterization of cyanobacterial blooms because of their seasonal occurrence. The Oceansat-1 satellite Ocean ColourMonitor (OCM) provides measurements similar to SeaWiFS but with higher spatial resolution, and this work is the first attempt to use OCM measurements to quantify cyanobacterial pigments. The satellite signal was first vicariously calibrated using SeaWiFS as a reference, and then corrected to remove the atmospheric effects using a customized atmospheric correction procedure. Then, empirical inversion algorithms were developed to convert the OCM remote sensing reflectance (Rrs) at bands 4 and 5 (centered at 510.6 and 556.4 nm, respectively) to concentrations of phycocyanin (PC), the primary cyanobacterial pigment. A holistic approach was used to minimize the influence of other optically active constituents on the PC algorithm. Similarly, empirical algorithms to estimate chlorophyll a (Chl a) concentrations were developed using OCM bands 5 and 6 (centered at 556.4 and 669 nm, respectively). The best PC algorithm(R2=0.7450, pb0.0001, n=72) yielded a rootmean square error (RMSE) of 36.92 mu-g/L with a relative RMSE of 10.27% (PC from 2.75 to 363.50 mu-g/L, n=48). The best algorithm for Chl a (R2=0.7510, pb0.0001, n=72) produced an RMSE of 31.19 mu-g/L with a relative RMSE of 16.56% (Chl a from 9.46 to 212.76 mu-g/L, n=48). While more !eld data are required to further validate the long-term performance of these algorithms, currently they represent the best protocol for establishing a long time-series of cyanobacterial blooms in the Lac des Allemands using OCM data.
Ref:
Remote Sensing of Environment ,
Jan. 1, 2011
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