## 6 Conclusions  
This research marks an initial exploration into whether Sentinel-2-based empirical models can be employed as a tool to support the monitoring and assessing of water quality in small- and mid-size lakes (below \(\sim\)200 km\({}^{2}\)) across New York State. By utilizing the Sentinel-2 L2A (surface reflectance) image collection in GEE and employing linear regression methods, we estimated the concentration of Chl-a and phycocyanin, which serve as key indicators of algal blooms. This approach can enable the identification of lakes with average values above a threshold of concern (e.g., the boundary between mesotrophic and eutrophic lakes at 8 \(\mu\)g/L, or HAB alert levels at 12, 24, 25, 30 \(\mu\)g/L or any other national level [78]) and holds promise for facilitating targeted field assessments, thereby supporting proactive measures for mitigating potential environmental impacts. In testing different in situ measurements, we found better model fit when field points were limited to observations above 8 \(\mu\)g/L. Of the variety of Sentinel-2 indices used as input to our linear regression models to correlate field-based observations with the Sentinel-2 L2A image collection in GEE, we had the greatest success with the MPH index when used with phycocyanin observations. We explored two approaches for temporally matching in situ and satellite data points. Expanding the temporal window had the advantage of increasing the amount of data available for training the models. Model fit decreased substantially with the two-day window, but this was less problematic for the one-day time window. MBE increased for all models using data from the one-day time window vs. an exact temporal match, but R\({}^{2}\) was often not negatively impacted. In fact, using the one-day window improved the R\({}^{2}\) and RMSE for the MPH model in comparison to concurrent data, which is likely associated with the larger data volume available for training. Understanding the importance of temporally matching field and satellite data is critical to providing guidance, for example to CSLAP volunteers, about intentionally aligning field sampling schedules with satellite overpass dates. To explore different approaches to mitigating the impact of clouds in Sentinel-2 images, we tested cloud masking using the ESA QA60 bitmask and the Cloud Score+ approaches. Our best model for estimating phycocyanin from Sentinel-2 images filtered by the QA60 bitmask cloud detection used the MPH index with an R\({}^{2}\) of 0.63 for concurrent data and 0.71 for a one-day time window when tested on data not used in model calibration. Using Cloud Score+ cloud detection on the corresponding model resulted in an R\({}^{2}\) of 0.84 for the concurrent data. These models had strong predictive power when tested on lakes that were known to have high phycocyanin concentrations, with very few false negatives, but only using field observations above 8 \(\mu\)g/L did not enable assessment of lakes with low phycocyanin levels; hence, the potential for false positives was not known. While erring on the side of minimizing false negatives seems appropriate, refining the model to also mitigate false positives will better focus resources. We used the randomly sampled testing dataset to validate the MPH linear regression model by determining the prediction and confidence intervals. This analysis is important to understand the confidence in the model output. The results of this study provide a method for estimating Chl-a and phycocyanin concentrations to monitor small- and mid-sized heterogeneous lakes across a broad spatial area, to detect those at high risk of algal blooms. Such remote sensing-based methods are critical to maximize the efficiency of limited resources for field-based evaluation. While the model fit is weaker for Chl-a, an important component of the method presented is considering statistical measures to describe the model application and estimate uncertainty in model predictions.