## 3 Results  
Single-band output from the Band Math function in ENVI for each BGA algorithm (ENVI Band Math Field in Table 2 and Table S1) was point sampled using the coincident surface observation locations (Figure 2) to extract BGA index (image) values for comparison with measured YSI sonde BGA/PC relative fluorescence unit values (BGA_PC_RFU) collected within 1 h of the CASI overflight. Strong correlations (Pearson's r\({}^{2}\) > 0.6; \(p\) < 0.001) between image derived indices and dense coincident surface observations of BGA during this experiment indicate that Be162 BsubPhy, SI052 BDA, Be162B700 sub601, Be16 NDPhyI, Gi033 BDA, Da052 BDA, SM122 BDA, Ku15 PhyCI, MM092 BDA, MI092 BDA, Wy08 CI, Zh10 FLH, and MM12 NDCI BGA algorithms worked well with CASI imagery. The Be162 BsubPhy, SI052 BDA, Am092 Bsub, Be16 NDPhyI, Mi092 BDA, and MM12 NDCI BGA algorithms worked well ([PERSON]'s r\({}^{2}\) > 0.6; \(p\) < 0.001) with simulated WorldView-2 and -3, imagery; the Be162 Bsub algorithm with simulated Sentinel-2 imagery; the Be16 FLHviolet with simulated Landsat-8 imagery; and the MM092 BDA, Be16 NDPhyI, MM12 NDCI, Go04 MCI, Ku15 PhyCI, Wy08 CI, SI052 BDA, Be162 BsubPhy, and Hu103 BDA BGA algorithms with simulated Sentinel-3/MERIS/OLCI imagery. The Be162 BsubPhy algorithm was the most widely applicable BGA algorithm with good performance for CASI, WorldView-2 and -3, Sentinel-2 and MERIS-like imagery and limited performance with MODIS imagery. The Be16 FLHviolet \"greenness\" algorithm yielded the best (although poor) BGA/PC estimates with simulated Landsat-8 imagery. We also conducted an extensive survey of other water quality parameters in the lake at the time of acquisition in order to determine why some proxy indices such as Chl-a may also work with regard to BGA estimation. Details of our statistical treatment of the data are available in [PERSON] et al. (2016). Type 1 ([PERSON]'s) r\({}^{2}\) values were used to evaluate algorithm performance with raw index values vs. BGA/PC relative fluorescence units (BGA_PC_RFU) values measured with YSI BGA optical sondes in the water (Table S2) and as index values normalized to BGA values vs. BGA_PC_RFU values (Table S3) to facilitate comparison. We used a critical _p_-value of 0.001 for all Pearson's r Type 1 regression tests. Some researchers prefer Standard Error of Regression (Standard Error of Estimate or S) values to Type 1 (Pearson's) r\({}^{2}\) values so we have also included them for the top performing algorithms from Table S3 for comparison (Table 3). Other researchers prefer Type 2 regressions (Kal \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithms By Satellite/Sensor** & **Spatial n** & **Geometric** & **Geometric** & **Geometric** & **Geometric** & **Standard** & **Standard** \\ \hline
**(Band Math in nm)** & **Res. (m)** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Deviation of** & **Deviation of** \\ \hline  & & & **Slope** & **Intercept** & **Correlation** & **Correlation** & **Slope** & **Y-intercept** \\ \hline  & & & & & & & & **Cofficient** & & \\ \hline  & & & & & & & & & **Squared** & \\ \hline CASIBe152 BushPry/15 sub615 & 1 & 29 & 1.141 & \(-\)1.500 & 0.881 & 0.777 & 0.107 & 1.199 \\ (float7515) - (float61515) & 1 & 29 & 1.128 & \(-\)1.376 & 0.889 & 0.700 & 0.102 & 1.150 \\ (float702) - (float6068) & 29 & 1.194 & \(-\)2.087 & 0.899 & 0.704 & 0.130 & 1.456 \\ (float702) - (float6068) & & & & & & & \\ (float702) - (float6068) & & & & & & & \\ (float702) - (float6068) & & & & & & & \\ (float703) - (float70455) & & & & & & & \\ (float705) - (float70681) & & & & & & & \\ \hline  & & & & & & & & \\ \end{tabular}
\end{table} 
Table 4: Performance of Algorithms for BGA Estimation at Harsha (East Fork) Lake with all results normalized to calculated BGA values with additional Type 2 Geometric Mean Tests for top performing algorithms by Type 1 Regression tests.