A Novel MERIS Algorithm to Derive Cyanobacterial Phycocyanin Pigment Concentrations in a Eutrophic Lake: Theoretical Basis and Practical Considerations

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Remote sensing, Phycocyanin, Microcystis aeruginosa, MERIS, Chlorophyll-a, Lake Taihu, Sun glint, Aerosols, Clouds

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A novel algorithm is developed and validated to estimate phycocyanin (PC) pigment concentrations of the most common cyanobaterium, Microcystis aeruginosa, in Taihu Lake (China's third largest freshwater lake) using data from the Medium Resolution Imaging Spectrometer (MERIS). First, a PC index (PCI) is defined as remote-sensing reflectance (Rrs, sr− 1) at 620 nm normalized against a baseline formed linearly between Rrs(560) and Rrs(665). Because PC has a local absorption peak at ~ 620 nm, PCI is shown in both theory and model simulations to exhibit a monotonic functional relationship with PC concentration. Field measurements also support this hypothesis, from which an empirical algorithm is developed to estimate PC concentrations between ~ 1 and 300 μg/L with unbiased RMS uncertainties of 58%. Radiative transfer simulations further show that such a field-based algorithm can be applied directly to MERIS Rayleigh-corrected reflectance (Rrc) after adjusting algorithm coefficients. Spectral analyses and image comparisons indicate that such an algorithm is nearly immune to perturbations from thick aerosols, thin clouds, significant sun glint, and extreme water turbidity. Mean usable data coverage increases from < 1% to > 50% when using Rrc as compared to Rrs processed using standard atmospheric correction algorithms in SeaDAS. The robust algorithm performance and significantly improved data coverage lead to the establishment of a long-term (2002–2012) time-series of PC concentrations in Taihu Lake, which show both seasonal and inter-annual changes. Test of the algorithm over other lakes, including Dianchi Lake (a typical plateau lake of China), suggests that the same band-subtraction approach might be applicable to other inland water bodies, although local bio-optical conditions due to different sediment and phytoplankton compositions need to be considered when applying such an empirical approach.

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Remote Sensing of Environment, v. 154, p. 298-317