## 5 Discussion
Initially, we examined two- and three-band ratios utilizing red and red-edge bands before applying four indices--CI [65], MCI [66], MPH [67], and SABI [68]--that were developed for lower spatial resolution instruments like MODIS and MERIS. Researchers have found that there are advantages in using the spectral strengths of these indices while utilizing the higher spatial resolution of Sentinel-2's MSI sensor. For example, [PERSON] et al. [72] applied CI to estimate Chl-a levels in an estuary with highly variable turbidity using Sentinel-2 data, and [PERSON] et al. [70] employed MCI to assess Chl-a concentration using Sentinel-2 data. However, to the best of our knowledge, MPH application with Sentinel-2 data has not been reported in the literature. [PERSON] et al. [67] introduced the MPH algorithm for Chl-a estimation using a baseline subtraction technique to determine the switchable dominant peak's height across the red and NIR bands of the MERIS. This peak is mainly attributed to sun-induced chlorophyll fluorescence and particulate backscatter and corresponds to the MERIS bands at 681, 709, or 753 nm. [PERSON] et al. [67] considered three different scenarios for HAB-affected waters: (1) low-medium biomass (Chl-a \(<\) 30 \(\mathrm{\SIUnitSymbolMicro g}\)/L), where Chl-a concentrations are highly correlated with the red peak at 681 nm; (2) high biomass (Chl-a \(>\) 30 \(\mathrm{\SIUnitSymbolMicro g}\)/L), where the red peak shifts to a longer wavelength and there is a strong correlation between Chl-a and the peak at 709 nm; and (3) extremely high biomass (Chl-a \(>\) 500 \(\mathrm{\SIUnitSymbolMicro g}\)/L), where the red peak shifts to 750 nm. The CSLAP field data placed NYS lakes in the first two scenarios. However, as there is no Sentinel-2 band at 681 nm, we centered the MPH index at MSI band 5 (705 nm), capturing the second peak, with the baseline between band 4 (665 nm) and band 8A (865 nm). Moving to centering at the slightly higher wavelength may explain why the models relating in situ observations to the MPH values had better fit when values below 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L were excluded. These results are comparable to [PERSON] et al. [67], who applied the MERIS-based MPH model in waterbodies across different ranges of eutrophication levels (0.5 \(\mathrm{\SIUnitSymbolMicro g}\)/L \(<\) Chl-a \(<\) 30 \(\mathrm{\SIUnitSymbolMicro g}\)/L: \(R^{2}\) = 0.71, RMSE = 3.5 \(\mathrm{\SIUnitSymbolMicro g}\)/L, n=36; Chl-a \(>\) 30 \(\mathrm{\SIUnitSymbolMicro g}\)/L: \(R^{2}\) = 0.58 and RMSE = 46.6 \(\mathrm{\SIUnitSymbolMicro g}\)/L). Unfortunately, while MERIS has excellent spectral characteristics, the spatial resolution (300 m at nadir) is unsuitable for small- and medium-sized lakes. Despite the relatively high temporal resolution of the two-satellite Sentinel-2 constellation, the timing of in situ sampling did not always align with the overpass time of image acquisition, leading to a low number of concurrent ground and image points. We considered the application of one- and two-day time windows rather than only using concurrent in situ and remotely sensed data to help mitigate any data insufficiencies for model calibration and validation. While our results were poor when we expanded to the two-day window, we do not have sufficient evidence to recommend concurrent vs. a one-day time window, given the contradictory improvements in R\({}^{2}\) versus the worsening in MBE. This may be due to the sensitivity of the MBE to outliers in the data introduced when considering a one-day delay. [PERSON] et al. [69] applied different time windows for Chl-a modeling in ten reservoirs in Oklahoma, USA. They found models using a five-day time window had strong correlations between field and Sentinel-2 data. This was quite different from our findings that indicate that while a one-day time window may be applied, a two-day time window did not appear appropriate. [PERSON] et al.