## 4 Results
### Main Phase
#### 4.2.2 Assessment of Model Prediction Accuracy  
The indices that led to the best model fit of Chl-a and phycocyanin concentrations used the linear equations shown in Table 3. These models were analyzed to evaluate prediction accuracy of field observations known to have concentrations above 8 \(\upmu\)g/L using Sentinel-2 data filtered by the QA60 bitmask using both concurrent data and a one-day time window. Table 4 summarizes the contingency tables when the models shown in Table 3 were applied to the testing dataset. In this table, we categorized the field and predicted data as high (>24 \(\upmu\)g/L), moderate (8-24 \(\upmu\)g/L), and low (<8 \(\upmu\)g/L), noting that these are not intended as regulatory guidelines. There are no consistent thresholds for such classification in the literature, but the 8 \(\upmu\)g/L and 24 \(\upmu\)g/L levels for Chl-a were selected based on trophic state and a common HAB alert level, respectively. As noted previously, the same thresholds were applied to phycocyanin for consistency. We note that the model training excluded in situ points below 8 \(\upmu\)g/L; hence, the accuracy assessment using the training and testing data can only evaluate the accuracy of field points with'moderate' and 'high' phycocyanin reports. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Chl-a Prediction**} & \multicolumn{6}{c}{**Phycocyanin Prediction**} \\ \hline  & \multicolumn{3}{c}{**Concurrent**} & \multicolumn{3}{c}{**One-Day Time Window**} & \multicolumn{3}{c}{**Concurrent**} & \multicolumn{3}{c}{**One-Day Time Window**} \\  & \multicolumn{3}{c}{**n = 81**} & \multicolumn{3}{c}{**n = 179**} & \multicolumn{3}{c}{**n = 27**} & \multicolumn{3}{c}{**n = 60**} \\ \hline
**Field** & **Low** & **Moderate** & **High** & **Low** & **Moderate** & **High** & **Low** & **Moderate** & **High** & **Low** & **Moderate** & **High** \\ \hline Moderate & 0 & 35 & 15 & 1 & 77 & 26 & 1 & 14 & 5 & 2 & 29 & 4 \\ \hline High & 0 & 11 & 20 & 0 & 22 & 53 & 0 & 1 & 6 & 1 & 0 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy assessment of estimation model based on CSLAP testing data, ESA QA60 bitmask cloud detection, with a division between points with low (<8 \(\upmu\)g/L), moderate (8–24 \(\upmu\)g/L), and high (>24 \(\upmu\)g/L) concentration levels. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Chl-a**} & \multicolumn{6}{c}{**Phycocyanin Index**} \\ \hline
**Time Window** & **Metric** & **2 BDA** & **2 BDA** & **3 BDA** & **NDCI** & **CI** & **MCI** & **MPH** & **SABI** \\ \hline \multirow{3}{*}{Concurrent} & R2 & 0.43 & 0.42 & 0.13 & 0.40 & 0.45 & 0.30 & 0.63 & 0.002 \\ \cline{2-10}  & RMSE & 21 & 32.4 & 39.8 & 32.9 & 31.6 & 36.2 & 27.0 & 42.6 \\ \cline{2-10}  & MBE & 0.66 & \(-\)0.27 & \(-\)1.43 & 0.42 & \(-\)1.02 & \(-\)1.51 & 0.34 & \(-\)1.96 \\ \hline \multirow{3}{*}{One-day time window} & R2 & 0.29 & 0.31 & \(-\)0.01 & 0.35 & 0.48 & 0.43 & 0.71 & \(-\)0.04 \\ \cline{2-10}  & RMSE & 24.6 & 31.9 & 38.6 & 30.9 & 27.8 & 29 & 22 & 39.2 \\ \cline{1-1} \cline{2-10}  & MBE & \(-\)3.05 & \(-\)4.91 & \(-\)6.09 & \(-\)5.71 & \(-\)6.12 & \(-\)4.59 & \(-\)4.45 & \(-\)7.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of R2, RMSE and MBE values for different phycocyanin (above 8 \(\upmu\)g/L), models based on a concurrent (n = 27) and one-day window (n = 60) for the testing dataset (2019 and 2020). This is contrasted to the best Chl-a model (2 BDA) for concurrent (n = 81) and one-day (n = 179) identified during the preliminary phase. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Time Window** & **Prediction Model (\(\upmu\)g/L)** \\ \hline Chl-a & Concurrent & 43.78 \(\times\) 2 BDA \(-\) 25.860 \\ \hline Chl-a & One-day & 33.75 \(\times\) 2 BDA \(-\) 15.078 \\ \hline Phycocyanin & Concurrent & 2486.33 \(\times\) MPH + 7.146 \\ \hline Phycocyanin & One-day & 2191.88 \(\times\) MPH + 6.891 \\ \hline \hline \end{tabular}
\end{table}