## 4 Discussion
Despite our differences in approaches (surface remote sensing reflectance (Rrs) vs. aircraft relative reflectance) our results are remarkably similar. [PERSON] et al. [14] simulated Hyperion, CHRIS and HyspIRI narrow band imager data from their surface spectroradiometer data. They found the SI05, MM09, and MI09 algorithms all performed well (strong r\({}^{2}\) values with modest RMSE values) with more modest results for the SC00 (SY00 in our study) [47] algorithm in their study. Our real calibrated aircraft relative reflectance based imagery showed the same pattern (Table S4). In fact, we found only one simple subtraction (Be162 BsubPhy715 sub615) algorithm that exceeds the performance of the simplified Simis algorithm [7] in our study. This simple subtraction algorithm leverages the band choices of [PERSON] et al. [11] for minimizing the Chl-a spectral overlap with that of the 620 nm phycocyanin absorption feature in their MI09 band ratio algorithm. The strong performance of our simple subtraction algorithm in this particular \"snap-shot\" study with real narrow band CASI data may not hold for time-series imagery with illumination variation between dates of acquisition. Like [PERSON] et al. [11] we expect their MI09 ratio-based algorithm (and other band ratio and shape metric algorithms) to be more robust across time-series imagery because they inherently damp variations in illumination. In addition, similar to [PERSON] et al. [11], we used the simplified version of the [PERSON] et al. [7] algorithm because we are trying to evaluate the portability of relatively simple algorithms between narrow, moderate and broad band satellite imagers [6], some of which have relatively limited band choices in order to create meta-constellations of current and near-term imagers for operational algal bloom monitoring. The performance of these algorithms with real imagers will vary due to differences in their on-orbit radiometry and for other water bodies due to variable values of BGA/PC and co-occurring water quality parameters including those described below. This study measured 41 water quality parameters (Table 5) at each of the 44 coincident surface observation sites during the hyperspectral overpass used to construct the coincident synthetic imagery for the satellite imaging systems considered above. These data helped to determine when and why some proxy algorithms such as those for Chl-a also work for BGA estimation. Of primary importance was quantifying the output of our YSI BGA fluorescence probes which recorded in BGA phycocyanin (PC) relative fluorescence units (BGA_PC_RFU) vs. total laboratory BGA cell counts(Figure 13). A comparison of these optical sonde BGA values (BGA-PC-RFU) vs. laboratory determined Cyanobacterial Sum of Total Biovolume (CSTB) values measured on the same day resulted in Pearson's \(r^{2}\) = 0.757 with \(p<0.001\) and \(n=39\) (Figure 13). We are confident in the validity of using optical sonde BGA_PC_RFU values as the basis for our coincident BGA surface observations accordingly. Our normalized phycocyanin index values for the best performing algorithms for all imagers except MODIS allow us to predict phycocyanin values (BGA_PC_RFU) and in turn cyanobacterial sum of total biovolume (CSTB). For example if we were to determine that a CASI pixel has an estimated normalized index value of 12 (Figure 7c) this would correspond to a BGA_PC_RFU value of 12 as well. This BGA_PC_RFU value of 12 would in turn correspond with a CSTB value of approximately 1.2 \(\upmu\)m\({}^{3}\)/L. One can make similar phycocyanin value estimates with WorldView-2 (Figure 8c), Sentinel-2 (Figure 9c), Landsat-8 (Figure 10c), and perhaps Sentinel-3/MERIS/OLCI (Figure 11c) and then CSTB estimates from Figure 13, respectively. We expect slopes and intercepts for normalized phycocyanin index values for the best performing algorithms derived from real WorldView-2, Sentinel-2, Landsat-8 (Figure 10c), and perhaps Sentinel-3/MERIS/OLCI reflectance imagery to differ somewhat from the synthetic results here.