## 4 Discussion  
The focus of this study is on simple, portable, semi-analytical algorithms based on the phyococyanin and Chl-a spectral signatures. CASI, WorldView and MERIS are capable of sensing the narrow 620 nm phyococyanin absorption feature (Figure 1, Table 1). Algorithms specifically tuned to this phyococyanin feature had the best performance for 1 m CASI imagery (CASIBe152 BsubPhy, r\({}^{2}\) = 0.776) and 1.8 m WorldView imagery (WV2 Be162 Bsub, r\({}^{2}\) = 0.790) and the second best for 300 m MERIS imagery (MERISBe16 NDphyI, r\({}^{2}\) = 0.852) (Table S4). An indirect BGA index algorithm, MERISMM092 BDA (r\({}^{2}\) = 0.863), designed to measure Chl-a had the best performance with regard to BGA estimation with simulated MERIS/OLCI imagery. [PERSON] et al. (2009) observed similar results for MERIS in Lake Champlain, New York, USA, with a stronger correlation of BGA to NIR reflectance than 620 nm absorption. They ascribed this result to non-linear absorption at 620 nm, spectral interference of Chl and other pigments with the 620 nm feature, due in part to surface scums, and temporal/environmental differences in phyococyanin  
Figure 12: Results of MERISMM092 BDA algorithm ([PERSON] and [PERSON], 2009) as raw BGA index values as applied to synthetic MERIS imagery with brighter pixels in the reservoir indicating higher BGA values (**a**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. predicted (calculated) raw BGA index values (MERISMM092 BDA) values with [PERSON]’s r\({}^{2}\) (r\({}^{2}\) = 0.863, \(p\) < 0.001, \(n\) = 9 due to large pixels) (**b**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. normalized predicted (calculated) BGA (MERISMM092 BDA) values with [PERSON]’s r\({}^{2}\) (r\({}^{2}\) = 0.863, \(p\) < 0.001, \(n\) = 9 due to large pixels) (**c**). Details of the synthetic bands and band math are available in Tables 1 and 2, respectively. absorption efficiency. Our results suggest that the large pixel sizes of MERIS may contribute to the masking of the 620 nm phycocyanin absorption feature, although the phycocyanin-tuned MERISBe16 NDPyI algorithm still had excellent performance (r\({}^{2}\) = 0.852) probably due to the strong algal and turbidity contrasts between the eastern and western basins of Harsha Lake at the time of aircraft image acquisition. In addition, MERIS does not capture the spatial variation observed in higher resolution imagery discussed above accordingly [6]. We conclude that phycocyanin specific algorithms with strong performance are available for CASI, WorldView and MERIS-like imagers. In contrast, Sentinel-2, Landsat-8 and MODIS are not capable of sensing the narrow 620 nm phycocyanin absorption feature (Figure 1, Table 1). We relied on Chl-a proxy algorithms summarized in [6] for BGA estimation with these imagers accordingly. The best performing Chl-a proxy algorithms for BGA were S2 Be16 Bsub for Sentinel-2 (r\({}^{2}\) = 0.704), L8 Be15 Flhviolet for Landsat-8 (r\({}^{2}\) = 0.339) and MODISMMNDCI12 for MODIS (r\({}^{2}\) = 0.183). The strong performance of Sentinel-2 for BGA estimation via the S2 Be16 Bsub Chl-a algorithm makes it a valuable component for cyanobacterial monitoring in at least some inland water bodies similar to Harsha Lake. The poor performance of these semi-analytical Chl-a proxy algorithms for BGA estimation with Landsat-8 and MODIS mirrors their poor performance for Chl-a estimation due to their wide bands relative to distinctive parts of the Chl-a spectral signature in water [6]. Indirect empirical local algorithms based on one or several other co-varying water quality parameters may be necessary for Landsat-8 and MODIS algorithms accordingly and will require local tuning. [PERSON] et al. [14] completed a performance review of reflectance based algorithms for predicting phycocyanin values in waters with both field and laboratory fluorometry. For this study we used field-based fluorometery using a calibrated YSI BGA probe.