## 1 Introduction
### Study Area  
For the sake of brevity, most of the details of our approach, study area (Figure 1), aircraft campaign, pre-processing, synthetic satellite data construction, image analysis, statistical regression techniques, combined error budgets, and coincident surface observations (Figure 2) are available in our companion Chl-a study [6]. Figure 1: Location maps of Harsha (East Fork) Lake near Cincinnati, Ohio: location in Ohio (**a**); and in detail (**b**). BGA algorithms based on the reflectance spectrum of phycocyanin should be better indicators of cyanobacteria than Chl-a algorithms because of their specificity, but are more difficult to adapt to existing satellite imaging systems due to much of the phycocyanin reflectance spectrum being masked by Chl-a and other pigments [11]. Only those imagers that sense a narrow absorption feature near 620 nm are well suited for the direct estimation of phycocyanin and BGA (Table 1). We have focused on relatively simple algorithms for BGA/PC estimation accordingly. Other water quality parameters such as Chl-a often co-occur and may co-vary with BGA and may be used as proxies for BGA in some water bodies [3; 4; 5; 6; 7; 8; 9; 10; 11; 14; 15; 17; 12; 13; 14; 15; 17; 23; 24; 28; 29; 30]. Even with direct BGA estimation based on phycocyanin spectral features, not all species of cyanobacteria produce toxins and even the same species of BGA may or may not produce toxins depending upon environmental conditions. Therefore, remote sensing of algal blooms and especially BGA blooms is a first-cut technology that can \"red flag\" cyanobacterial blooms but cannot assess toxicity directly [24]. In this study we balance accuracy with portability across a variety of satellite imagers to counter frequent cloud cover issues during the humid temperate summers of southwest Ohio, and to create a practical first-cut capability for potential HAB monitoring. The target users for this technology are water quality managers of smaller inland water bodies less than a few km across. Given that no current remote sensing technology can actually detect the toxins in algal blooms [24], our operational goal is to find groups of imager/algorithm combinations that appear promising for the detection of algal blooms. Once a significant algal bloom is detected it will be necessary to collect water samples to evaluate toxicity. Therefore we are not concerned about detecting very low values of chlorophyll-a or phycocyanin nor are we concerned about saturation at high Chl-a/BGA values or differentiating high Chl-a/BGA values from surface algal scums for the purposes of this near-term warning and monitoring system. Characterizing Chl-a [6] and/or BGA/PC (this study) values as low, medium or high will be sufficient in this context of smaller inland water bodies in temperate climates like that of Ohio due to frequent summer cloud cover. Interpretation of low, medium or high will depend on the algorithm index and imager in a regional context. Regional water quality managers will then be able to use their experience in combination with field-based toxicity data to determine thresholds and appropriate responses to algal blooms. Several studies have used high spectral resolution surface point and imaging spectroradiometers with numerous and narrow (\"hyperspectral\") band configurations to collect reflectance signatures of BGA laden inland waters in order to formulate algorithms to estimate BGA values based on the phycocyanin reflectance signature [7; 8; 10; 11; 13; 14; 17; 31; 32]. We have favored simple algorithms for the reasons above. In several cases, we have followed the band choices of previous workers in simplified versions of their algorithms [7; 8; 10] or transferred their band choices to new or variants of existing algorithms [7; 8; 9; 11; 17]. Surface phytoplankton values are influenced by wind (mixing and drifting) as well as changes in the nutrient flux and water temperature, among other variables.