Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological
status of inland waterbodies and for human and animal health. In this study we examined the performance
of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data
acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer
for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were
compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing
algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the
ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a
marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the
retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The
concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell
numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins
(R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were
able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning
thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute
to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional
and global scales.