## 4 Results
### Alternate Cloud Mask Evaluation
#### 4.3.2 Applying Cloud Score+ to 2019 and 2020 Data  
Table 6 summarizes the R\({}^{2}\), RMSE, and MBE for both Chl-a and phycocyanin models with concurrent and one-day time window data from the testing dataset. Models with concurrent data had better results for both Chl-a and phycocyanin. Using Cloud Score+ instead of the QA60 bitmask approach for phycocyanin improved the R\({}^{2}\) (from 0.63 to 0.84) and reduced the RMSE (from 27 ug/L to 14.9 ug/L), but the MBE magnitude increased from 0.34 to -3.39. For Chl-a, while the R\({}^{2}\) slightly improved (from 0.43 to 0.47), both the RMSE and MBE increased compared to the QA60 bitmask. Both the concurrent MPH model for phycocyanin and the 2 BDA model for Chl-a overestimated prediction values when using the QA60 bitmask for cloud mitigation, and underestimated when using Cloud Score+. Unlike the previous findings, expanding the dataset using a one-day time window did not improve results when using Cloud Score+, with the R\({}^{2}\) decreasing and the RMSE increasing. The MBE decreased for both Chl-a and phycocyanin estimation when expanding to the one-day window, with the models underestimating concentrations of both parameters. Figure 8 shows a scatterplot of in situ measurements vs. predicted values for the phycocyanin MPH model based on the concurrent testing dataset (CSLAP data for 2019 and 2020). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Chl-a-2 BDA**} & \multicolumn{4}{c}{**Phycocyanin-MPH**} \\ \hline
**Time Window** & **n** & **R\({}^{2}\)** & **RMSE** & **MBE** & **n** & **R\({}^{2}\)** & **RMSE** & **MBE** \\ \hline Concurrent & 65 & 0.466 & 22.5 & \(-\)1.96 & 22 & 0.839 & 14.9 & \(-\)3.87 \\ \hline One-day & 144 & 0.231 & 24 & \(-\)1.2 & 48 & 0.623 & 18.3 & \(-\)1.91 \\ \hline \end{tabular}
\end{table} 
Table 6: R\({}^{2}\), RMSE, and MBE for models of Chl-a and phycocyanin above 8 ug/l based on concurrent and one-day testing datasets (2019 and 2020). Figure 7: Scatterplots between phycocyanin and Sentinel-2 index for training data based on applying Cloud Score+ (50% clear threshold) with total matched Sentinel-2 data and (**a**) all in situ data, (**b**) in situ data above 8 ug/L. Figure 8: Scatterplot between the predicted values and in situ measurements of phycocyanin concentration for the MPH model based on a concurrent testing dataset (2019 and 2020 CSLAP data) and Cloud Score+ processing. The five individually labeled points correspond to those in Figure 9 where the predictions of low phycocyanin concentrations did not match the moderate field phycocyanin concentrations (8–24 \(\upmu\)g/L). Figure 9: _Cont._  
The indices that led to the best model fit of Chl-a and phycocyanin concentrations with the Cloud Score+ approach used the linear equations shown in Table 7. These models were analyzed to evaluate the prediction accuracy of field observations known to have concentrations above 8 \(\upmu\)g/L using Sentinel-2 data filtered by the Cloud Score+ method. The analysis of prediction accuracy focused on this model. Table 8 presents the contingency tables for the testing datasets, categorizing the field and predicted data as high (>24 \(\upmu\)g/L), moderate (8-24 \(\upmu\)g/L), or low (<8 \(\upmu\)g/L), noting as above that the model training excluded in situ points below 8 \(\upmu\)g/L. The results shown in Table 8 demonstrate that in the concurrent model, 56% and 69% of the predicted Chl-a values are correctly categorized as moderate and high, respectively, and 50% and 100% of the predicted phycocyanin values are correctly categorized as moderate and high, respectively. Using a one-day time window prediction model, the accuracies of the moderate and high levels of Chl-a concentrations  
Figure 9: Sentinel-2 images of five sample locations showing field samples (magenta marker) where points in the testing dataset predicted low phycocyanin concentrations despite high field concentrations (above 8 \(\upmu\)g/L).