## 1 Introduction
With a dual-satellite configuration, Sentinel-2 also has a 5-day temporal resolution, further making it useful for monitoring phytoplankton concentrations in New York State lakes. Prior studies to estimate in situ reflectance measurement have used a variety of data types including multi-spectral (e.g., [7; 19; 20; 21]) and hyperspectral (e.g., [8; 22; 23]) satellite images, as well as terrestrial radiometers (e.g., [9; 24; 25]). The Operational Land Imager (OLI) and OLI-2 onboard Landsat 8 and 9, respectively, have similar spectral coverage as the MSI with a 30 m GSD and provide data for observing both land and aquatic systems. Unfortunately, even with sensors on-board two satellites, the moderate spatial resolution in conjunction with a combined 8 day repeat interval on Landsat 8 and 9 reduces application for phytoplankton concentration monitoring in small lakes [26]. Although the higher spectral resolution in hyperspectral sensors may prove valuable for monitoring water quality,the limited spatial coverage restricts their broad application. Some studies (e.g., [27; 28]) have measured surface reflectance over lakes using terrestrial hyperspectral radiometers, often to provide data to calibrate or evaluate models as an alternative or to complement in situ data. The algorithms used to estimate pigment concentration (usually Chl-a) from satellite images are often divided into two categories: (1) physical and (2) empirical. Physical-based algorithms, e.g., the Bio-Optical Model-Based tool for Estimating water quality and bottom properties from Remote sensing images (BOMBER; [20; 29; 30]), Water Color Simulator (WASI; [31]), S2 Spectral Response Function (S2-SRF; [32]), use bio-optical models to simulate the radiance of the spectrum with specified water constituents. These algorithms are highly sensitive but are computationally demanding. Empirical algorithms use statistical methods such as regression to establish a relationship between parameters such as Chl-a or phycocyanin and spectral reflectance. Empirical approaches are less sensitive than bio-optical models, but their development and application are easier and thus they are often considered more practical for implementation [33; 34]. A substantial challenge in applying empirical algorithms is spatial heterogeneity in terms of the distribution or characteristics of the data or the phenomena being studied. Many studies have developed relationships between ground observations and satellite data in inland waters, but most calibrate retrieval models for specific waterbodies [8; 19; 24; 27] as opposed to establishing models with broad applicability. Researchers have developed empirical algorithms to estimate Chl-a concentration from remote sensing data using many different band ratios and multi-band indices. Two-band ratios (\(\lambda_{1}/\lambda_{2}\)) consider spectral response from different pairs (\(\lambda_{1}\) and \(\lambda_{2}\)) of image bands that are selected based on specific waterbody characteristics. For example, [PERSON] et al. [5] found a blue-green two-band ratio worked successfully for 'Case-1' waters, which typically includes most open ocean waters, where phytoplankton dominates the inherent optical properties (IOPs) of the water [9]. This model uses blue-green wavelengths (440-550 nanometers (nm)) because the first absorption peak of Chl-a is around 440 nm (\(\lambda_{1}\)) and minimal absorption occurs around 550 nm (\(\lambda_{2}\)) [35]. However, NYS inland waterbodies tend to be 'Case-2' waters, whose optical properties are dominated by other constituents like colored dissolved organic matter (CDOM) and total suspended matter (TSM). In Case-2 waters, the blue-green two-band ratio is not appropriate because of absorption and scattering due to CDOM and TSM [36]. The second Chl-a absorption peak is near 675 nm for water dominated by CDOM and TSM, so a NIR/red two-band ratio is widely used [37; 38] with \(\lambda_{1}\) from wavelengths of 700-720 nm and \(\lambda_{2}\) from the second Chl-a absorption peak, in the range of 660-690 nm [39].