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We propose to develop bio-optical algorithms and products using hyperspectral data under program element 2.4 to generate an improved application for the detection of freshwater cyanobacteria that are often toxic, belonging to a larger family of algal conditions referred to as harmful algal blooms (HABs). We propose to develop a new detection application for freshwater cyanobacteria HABs (CHABs) by integrating information from several different approaches, some of which expand upon current operational algorithms but which are not based on hyperspectral data. We will produce a suite of advanced bio-optical products, including from semi-analytic algorithms (SAAs). We believe that hyperspectral remote sensing will improve the ability of water quality algorithms to quantify CHABs. For the SAAs, we propose to develop separate products based on optical model inversion incorporating cyanobacteria properties. Cyanobacteria have unique absorption and scattering properties, which make them difficult to invert using standard SAAs models, but our approach will use these characteristics to create a unique identification of cyanobacteria populations, among other phytoplankton groups that are typically also present before, during and after CHAB events. Toxicity is often highly variable within CHABs is not directly detectable with optical methods, however, we note that CHAB surface scums, which we will detect, are often toxic. The main application question we are addressing is: can we improve CHAB detection and assessment including early stages with hyperspectral data? We believe that a combination of approaches based on hyperspectral data will provide necessary discrimination to monitor CHAB populations, as well as other phytoplankton groups with many applications to freshwater quality. Thus, our ultimate application product will be a comprehensive CHAB assessment that includes confidence levels tied to bio-optical algorithm uncertainties. Our specific objectives are to: