## 1 Introduction  
Cyanobacteria, or blue-green algae, are microorganisms that are naturally found in lakes, rivers, ponds, and other surface waters. In certain conditions--notably warm water, wind-aligned fetch, and sufficient nutrients--they can rapidly form harmful algae blooms(CyanoHABs, or more simply hereafter, HABs). Some HABs can produce cyanotoxins that are harmful to human health, aquatic ecosystems, drinking water supplies, recreational activities, and the economy. New York State (NYS) has recorded an increased frequency and distribution of cyanobacterial harmful algae bloom reports in recent years, which is unfortunately also occurring in many regions around the world [1]. Toxic HABs represent a threat to human and animal health through ingestion, inhalation, and contact [2]. HABs are expected to become more commonplace around the world, given increasing temperatures [1; 3]. HABs in NYS are reported by professional scientists, trained citizen scientists, and members of the public, but reports are often limited to shoreline observations that are qualitative in describing the estimated extent of a bloom, e.g., 'local', 'widespread', 'lakewide' [4]. However, blooms are dynamic and ephemeral, so that an observation at a single point in space and time is not representative for most waterbodies. Moreover, while qualitative reports are useful, quantitative assessment is critical to manage potential risks associated with HABs. However, field-based monitoring of all NYS waters is not feasible due to constraints on time, money, and personnel. The need for a more objective and efficient measure of HABs' extent across all waterbodies has become apparent in recent years and prior research has explored satellite-based methods to meet this monitoring need by relating the remote sensing response to ground-based water quality measurements. While cyanobacteria concentration is best described by the concentration of the characteristic pigment phycocyanin, it can be approximated by the concentration of parameters such as chlorophyll-_a_ (Chl-a; [5; 6]), and models relating these parameters to spectral observations have been reported in the literature (e.g., [7; 8; 9]). The color of water is known to be strongly influenced by the presence and concentration of phytoplankton [10]. Chl-a absorbs light at the blue and red wavelengths, while reflecting green light; for this reason, water with higher concentration of chlorophyll-containing phytoplankton has strong green reflectance. This observation has been used to estimate Chl-a concentrations over large areas of water from aircraft [11], early satellites (NIMBUS-7 and the coastal zone scanner, [12; 13]; SeaWiFS, [14]), and low spatial resolution satellite-based sensors with targeted spectral bands (MODIS on Terra/Aqua, [15]; MERIS on ENVISAT-1, [16]). NYS has 2096 lakes with surface areas between 0.026 km\({}^{2}\) (the smallest size subject to regulation, 0.01 square miles) and 207 km\({}^{2}\), with 93% of those lakes under 2 km\({}^{2}\)[17]. This count excludes the Great Lakes (Ontario, Erie), and Lake Champlain, which shares a border with both Canada and the state of Vermont. This study aims to use remote sensing imagery to monitor these small- to medium-sized lakes (below \(\sim\)200 km\({}^{2}\)) across the state, and hence requires data that provides a compromise between having relatively broad geographic coverage and high spatial resolution within the waterbodies of interest. The launch of the European Space Agency (ESA) Sentinel-2 satellites in 2015 and 2017 has provided free data in the visible and near infrared (NIR) spectrum [18]. The use of Sentinel-2 data for water quality assessments has become increasingly common around the world. The Multi-Spectral Instrument (MSI) onboard Sentinel-2 has several bands with 10 m ground sample distance (GSD), making it a good choice for monitoring smaller waterbodies.