## 5 Discussion  
The primary objective of this study was to develop a model for estimating and monitoring Chl-a and phycocyanin concentrations across NYS lakes. After encountering early challenges related to the inaccuracy in estimating parameters with low concentrations, we implemented a threshold based on the trophic index utilized by NYSDEC for eutrophic lakes [60], restricting the model to in situ Chl-a and phycocyanin data above 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L. While studies commonly model Chl-a (e.g., [8; 9; 51; 69; 70]), our training analysis demonstrated a stronger correlation between satellite data and phycocyanin concentrations, and we found the phycocyanin model fit also improved when in situ data below 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L were excluded, likely because below 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L there is insufficient pigment in the water to give a clear spectral response. Other studies have also found it advantageous to apply thresholds on field data when modeling water quality. [PERSON] et al. [21] classified inland and coastal waters in the boreal region into five optical water types (OWTs)--clear, moderate, turbid, very turbid, and brown--and identified the best model for each class separately. [PERSON] et al. [71] employed two different thresholds on field data based on recommendations for cyanobacteria-dominant recreational water. They used 10 \(\mathrm{\SIUnitSymbolMicro g}\)/L and 24 \(\mathrm{\SIUnitSymbolMicro g}\)/L as the thresholds for low-risk and alert levels in recreational waters, respectively. They did not report statistics, but rather divided the initial dataset in different ways to visually determine the best estimation models for each subset. [PERSON] et al. [70] utilized OWTs to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Time Window** & **Prediction Model (\(\mathrm{\SIUnitSymbolMicro g}\)/L)** \\ \hline Chl-a & Concurrent & \(47.48\times 2\mathrm{BDA}-28.794\) \\ \hline Chl-a & One-day & \(37.36\times 2\mathrm{BDA}-17.712\) \\ \hline Phycocyanin & Concurrent & \(3101.5\times\mathrm{MPH}+2.992\) \\ \hline Phycocyanin & One-day & \(3047.6\times\mathrm{MPH}+0.626\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of linear models used for Chl-a and phycocyanin estimation (using Sentinel-2 data filtered by Cloud Score+). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Chl-a Prediction**} & \multicolumn{4}{c}{**Phycocyanin Prediction**} \\ \hline  & \multicolumn{4}{c}{**Concurrent**} & \multicolumn{4}{c}{**One-Day Time Window**} & \multicolumn{4}{c}{**Concurrent**} & \multicolumn{4}{c}{**One-Day Time Window**} \\  & \multicolumn{4}{c}{**n = 65**} & \multicolumn{4}{c}{**n = 144**} & \multicolumn{4}{c}{**n = 22**} & \multicolumn{4}{c}{**n = 48**} \\ \hline
**Field** & **Low** & **Moderate** & **High** & **Low** & **Moderate** & **High** & **Low** & **Moderate** & **High** \\ \hline Moderate & 0 & 22 & 17 & 1 & 66 & 26 & 5 & 9 & 4 & 8 & 15 & 5 \\ \hline High & 0 & 8 & 18 & 0 & 14 & 37 & 0 & 0 & 4 & 0 & 1 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy assessment of Chl-a and phycocyanin estimation models based on CSLAP testing data and Sentinel-2 data filtered by Cloud Score+, with a division between points with low (<8 \(\mathrm{\SIUnitSymbolMicro g}\)/L), moderate (8-24 \(\mathrm{\SIUnitSymbolMicro g}\)/L), and high (>24 \(\mathrm{\SIUnitSymbolMicro g}\)/L) concentration levels. classify lakes into 13 classes and applied different retrieval models for each class. In this study, we considered data points above and below the established threshold; however, there was no specific trend for field points below 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L, hence we directed our focus towards field points above 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L. We used Chl-a and phycocyanin concentrations above the 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L threshold as we tested a variety of model inputs.