## 3 Results
### MODIS (Synthetic)  
We applied three existing algorithms (NDCI, 2 BDA and Am092 Bsub) in the form of six variants to synthetic MODIS bands 1 and 2 [43] for BGA estimation with limited success (Tables S1, S2 and S4, Figure 11). None of the spectrally-oriented semi-analytical algorithms considered here had acceptable performance (Pearson's r\({}^{2}\) > 0.6; \(p\) < 0.001) with regard to BGA estimation with this sensor in this experiment due to the wide MODIS bands. While MODIS could be a part of operational monitoring systems, its wide bands and coarse spatial resolution suggest that it will have limited value in operational monitoring systems for inland water quality, especially for smaller water bodies less than a  
Figure 10: Results of new FLH Violet algorithm [6] as raw index values as applied to synthetic Landsat 8 imagery with brighter pixels in the reservoir indicating higher BGA values (**a**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. predicted (calculated) raw index value (L8 Be15 Flhviolet) with [PERSON]’s r\({}^{2}\) (r\({}^{2}\) = 0.339, \(p\) < 0.001, \(n\) = 29 to avoid shorelines) (**b**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. normalized predicted (calculated) BGA values (L8_FlhvioletPHY) with [PERSON]’s r\({}^{2}\) (r\({}^{2}\) = 0.339, \(p\) < 0.001, \(n\) = 29 to avoid shorelines) (**c**). Details of the synthetic bands and band math are available in Tables 1 and 2, respectively.  
few kilometers across. MODIS also lacks a narrow band capable of measuring the 620 nm phycocyanin absorption feature [50].  
Figure 11: Results of MODISM12 NDCI4 algorithm [32] converted to BGA values as applied to synthetic MODIS imagery with brighter pixels in the reservoir indicating higher BGA values (**a**). Evaluation as raw index values as applied to synthetic MODIS imagery and its evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. predicted (calculated) raw index value (MODISMM12 NDCI4) with [PERSON]’s \(\mathbf{r}^{2}\) (\(\mathbf{r}^{2}\) = 0.183, \(p\) = 0.126, \(n\) = 9 due to large pixels) (**b**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. normalized predicted (calculated) BGA values (MODISMM12 NDCI4 PHY) with [PERSON]’s \(\mathbf{r}^{2}\) (\(\mathbf{r}^{2}\) = 0.183, \(p\) = 0.126, \(n\) = 9 due to large pixels) (**c**). Details of the synthetic bands and band math are available in Tables 1 and 2, respectively.  
The performance of each algorithm in Table 2 and Table S1 applied to the synthetic MODIS imagery was evaluated using nine coincident surface observations. These nine points were chosen to avoid pixels that mixed land and water at 250 and 300 m spatial resolutions. MODIS bands 1 and 2 were simulated with the CASI data at 250 m spatial resolution to facilitate comparison of algorithm performance. MODIS bands 1 and 2 are commonly available at the 250 m spatial resolution and are part of the better performing algorithm (NDCI). This suggests that MODIS NDCI may have some modest utility with regard to BGA estimation and algal bloom monitoring in some larger inland water bodies. The large pixel sizes associated with MODIS make it more appropriate for relatively large water bodies using other water quality parameters such as Chl-a or turbidity as proxies for BGA/PC. MODIS imagery will also require severe masking to avoid shorelines (mixed land and water pixels) (Figure 9a). Index imagery with raw and normalized index Type 1 (Pearson's r) linear regressions are shown for the best performing algorithm (MODISMM12 NDCI4) in Figure 11.