## 1 Introduction  
The eutrophication of freshwater bodies is a global environmental concern. It is driven primarily by nutrient enrichment, most often accelerated by anthropogenic activities such as agricultural fertilizer application, wastewater discharge, and urbanization [1]. Cultural eutrophication promotes excessive growth of algae, leading to the deterioration of water quality and overall ecosystem health, and the loss of aquatic habitats [2]. In extreme cases, harmful algal blooms (HABs) occur through the rapid proliferation of algae, in particular cyanobacteria [3]. The occurrence of HABs is influenced by a complex interplay of biogeochemical and physical factors, including nutrient availability (mainly phosphorus and nitrogen), water temperature, and hydrodynamics [4]. In the western portion of Lake Ontario, algal blooms are a particular concern in nearshore areas including Hamilton Harbour (HH), the Toronto shoreline, and the Niagara River outlet [5]. Eutrophication in these areas is driven by excess nutrient runoff associated with rapid urbanization of the Greater Toronto Area [6] and surrounding agricultural and industrial activities [7]. Other contributing factors include nutrient-rich inflow from upstream Lake Erie via the connecting Niagara River [8], warmer water temperatures, and changes in circulation patterns [9], as well as ecological disturbances such as the invasion by nuisance mussels [9]. Given these rapidly changing dynamics, continued water quality monitoring of the nearshore and offshore waters of Western Lake Ontario (WLO) remains essential. Traditional field-based water quality monitoring methods have played an integral role in tracking and assessing trophic changes in large lakes over the past several decades. While these methods are essential, they face limitations due to their high costs and personnel requirements, as well as their restricted spatial and temporal coverage. In this context, remote sensing (RS) techniques have emerged as a valuable complementary tool, enhancing traditional water quality monitoring with their ability to provide large-scale, frequent, and cost-effective observations of water bodies [10]. Multispectral satellites like Landsat 5, 7, 8, and Sentinel-2 are now extensively used for lake eutrophication monitoring at a global scale [11]. In RS research, chlorophyll-_a_ (Chl-_a_) concentration, as the key photosynthetic pigment, is widely used to map algal abundance in diverse aquatic environments, including coastal areas, lakes, and rivers, providing a valuable tool for monitoring eutrophication trends [12; 13; 14]. The accuracy of Chl-_a_ estimations depends highly on the applied atmospheric correction and the selection of appropriate retrieval algorithms [15]. By carefully addressing these aspects, RS can provide essential data on water quality trends and dynamics. The estimation of Chl-_a_ concentrations using multispectral satellite data has been growing in recent years [16]. This trend is notably tied to the launch of the Sentinel-2 satellite constellation in 2015, which added a red-edge band and increased spatial resolution down to 10 m. Achieving even higher spatial resolutions will require expanded data storage capacities, advanced sensors, or lower orbital altitudes. At present, medium spatial resolution, defined as 10 to 30 m, offers an adequate balance between detail and practicality for retrieving Chl-_a_ concentrations from large water surfaces, including challenging nearshore areas where land adjacency can be an issue. Further improvements are expected in the near future with the anticipated launch of NASA's 10 m super-spectral Landsat Next in 2030, which promises even finer spectral resolution, likely improving water colour remote sensing. A limited number of recent studies in the medium-spatial-resolution range have compared the performance of different atmospheric corrections and Chl-_a_ concentration retrieval indexes (Table 1).