Evaluation of Bio-Optical Lake Algorithms with Regards to Cyanobacteria Blooms
Moore, Timothy¹; Sullivan, James²; Bradt, Shane¹; Twardowski, Michael²; Ruiz-Verdu, Antonio³; Mouw, Colleen^4
1University of New Hampshire, UNITED STATES; ²WET Labs, Inc., UNITED STATES; ³CEDEX/INTA, SPAIN; ⁴Michigan Tech. University, UNITED STATES

In recent years, there has been an increasing emphasis on the advancement of freshwater lake bio-optical algorithms as related to cyanobacteria blooms and other high biomass conditions. The traditional use of open ocean bio-optical algorithms that focus on the blue and green spectral region often are not suitable for these conditions. Freshwater algorithms tend to focus on using reflectance data in the red and NIR region of the spectrum which are more suited to high biomass conditions. In this work, we evaluate leading bio-optical algorithms designed for high biomass conditions in freshwaters by examining algorithm performance using in situ and extracted match-up data sets with MERIS and Modis Aqua satellite overpasses. We also present a new bio-optical algorithm based on red/NIR channels that is suitable for ocean color satellites. This new algorithm is compared with existing algorithms, and we also evaluate the potential for detecting a specific phycacyanin concentration. Ultimately, performance of any algorithm will depend on the quality and quantity of match-up data sets, bio-optical algorithm characteristics, and ocean color image quality.