## 6 Conclusions
### Implications for Operational BGA Monitoring with Existing and Near-Future Satellites  
For imagers with appropriate bandwidths and positions relative to the BGA phycocyanin reflectance signature and pixel sizes appropriate for smaller inland water bodies less than a few km across, simple algorithms that subtract or ratio bands and therefore inherently normalize reflectanceperformed well and appear to be relatively portable between most satellite imaging systems for the estimation of BGA in smaller inland water bodies. Therefore, near-term steps toward operational water quality monitoring systems for inland water bodies smaller than a few kilometers across should focus on Sentinel-2 A and B and to a lesser extent Landsat-8 with augmentation from WorldView-2 and -3 or hyperspectral imagery when required. Good performance of both BGA and Chl-a algorithms for BGA estimation suggests that early efforts to operationalize regional algal monitoring can use whichever set of algorithms are most appropriate for the sources of imagery at hand (Sentinel-2/3, WorldView-2/3 and to a lesser extent Landsat-8). Sentinel-3/MERIS/OLCI may also be useful for heavily masked time-series monitoring but will capture very little of the spatial heterogeneity of BGA and CSTB values in smaller inland water bodies. The somewhat better performance of phycocyanin specific semi-analytical algorithms for BGA and CSTB estimation suggests that future satellite imaging systems for cyanobacterial monitoring in smaller inland water bodies that are often sources of drinking water should include a narrow band focused on the 620 nm absorption feature [7; 8; 11; 22] and spatial resolutions of 20-90 m. Such systems should also include \"water vapor\" bands at 770-870 nm and 870-1020 nm to facilitate atmospheric correction to reflectance [52] for real time monitoring of smaller inland water bodies [53]. The following are available online at www.mdpi.com/2072-4292/9/6/538/s1. Table S1. Band math with band number and band wavelengths in nm for each algorithm used for BGA/PC relative fluorescence unit (BGA_PC_RFU) estimation at Harsha Lake using phycocyanin indices and nearest center band wavelengths in current and near-future satellite imaging systems. Float refers to floating point values of relative reflectance in the ENVI band math we used in this study at the specified wavelengths in nm from atmospherically corrected imagery. Float is not a variable, it is an IDL function used to prevent byte overflow errors during calculation. Band number and band wavelengths are listed for each algorithm sequentially, Table S2. Performance of algorithms for BGA estimation at Harsha Lake using image derived phycocyanin indices according to [PERSON]'s r test (Type 1) linear regressions. Satellite/Algorithm combinations with asterisk measure the 620 nm phycocyanin absorption feature directly. Y-axis (dependent variable) and intercept have units of BGA_PC_RFU. X-axis is a unit less numerical index, Slope has units of BGA_PC_RFU. Table S3. Performance of Algorithms for BGA Estimation at Harsha (East Fork) Lake with all results normalized to calculated BGA/PC values. Satellite/Algorithm combinations with asterisk measure the 620 nm phycocyanin absorption feature directly. Y-axis (dependent variable) and intercept have units of BGA_PC_RFU. X-axis also has units of BGA_PC_RFU. Slope is unit less, Table S4. Direct and Indirect BGA Indices Ranked by Performance (Pearson's r2). Satellite/Algorithm combinations with asterisk measure the 620 nm phycocyanin absorption feature directly. Y-axis (dependent variable) and intercept have units of BGA_PC_RFU. X-axis also has units of BGA_PC_RFU. X-axis also has units of BGA_PC_RFU. Slope is unit less. **Acknowledgments:** This study was funded by the U.S. Army Corps of Engineers. The U.S. Environmental Protection Agency and the Kentucky Department of Environmental Protection, Division of Water provided valuable in-kind services.