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