## 5 Conclusions  
This study assesses the influences of a variety of factors, including selection of satellites, in situ data categories, atmospheric correction processors, and retrieval indexes, on the performance of RS-derived Chl-\(a\) concentration retrieval models for the nearshore and offshore waters of the western basin of Lake Ontario, including Hamilton Harbour. We combine images from four satellites (Landsat 5, 7, 8, and Sentinel-2) with 600 in situ Chl-\(a\) matchups for the period 2000 to 2022. The matchups are divided into seven categories based on location, seasonality, and [PERSON]'s TSI, resulting in a total of 28 scenarios. For each scenario, we compare the performance of 27 Chl-\(a\) retrieval indexes paired to \(11\) atmospheric correction products. Sentinel-2 is found to systematically outperform other satellites in Chl-\(a\) concentration retrieval. The results further show the effectiveness of categorizing in situ Chl-\(a\) concentration data based on trophic state and tailoring algorithms to each category accordingly. The results also highlight the better performance of band ratio indexes, particularly the blue-to-green index. In contrast, normalized difference indexes and indexes with FLH formulations are less effective for our case study. While the green and blue bands emerge as the preferred bands for Chl-\(a\) concentration estimation, the effectiveness of spectral bands depends on the specific index, satellite, and atmospheric correction method used; these must therefore be carefully evaluated in the development of Chl-\(a\) concentration retrieval models. Overall, no single scheme yields a universally transferable retrieval model. That is, a model should be tailored to the study site and objectives, as well as the specific data availability, category, and satellite platform and sensor. Another key finding is that model complexity does not necessarily correlate with improved retrieval accuracy, implying that simpler models should be given appropriate consideration in RS water quality applications. Our study adds to the literature on semi-empirical RS Chl-\(a\) retrieval approaches that are emerging as essential tools in water quality monitoring that can help protect large freshwater lakes against the undesirable impacts of eutrophication. **Supplementary Materials:** The supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16091595/s1](https://www.mdpi.com/article/10.3390/rs16091595/s1)]([https://www.mdpi.com/article/10.3390/rs16091595/s1](https://www.mdpi.com/article/10.3390/rs16091595/s1)), Figure S1: Time series plot of in situ data, displaying Hamilton Harbour (HH, diamond markers) and Western Lake Ontario (WLO, square markers) alongside Landsat 5 (blue), 7 (red), 8 (yellow), and Sentinel-2 (purple) matchups. The green background represents oligotrophic/mesotrophic and eutrophic/hypereutrophic classes based on Carlson's Trophic State Index (TSI). Two outlier concentrations of 137 and 80 \(\upmu\)g/L are excluded for better visual presentation; Figure S2: Heatmaps of R\({}^{2}\) between in situ Chl-\(a\) concentrations and corresponding index values of co-located pixels across various schemes and subcategories. Warmer colours indicate higher R\({}^{2}\) (better performance), while colder ones signify lower R\({}^{2}\). Black cells indicate N/ A values; Table S1: Descriptive statistics of the in situ data categorized based on seasonality, study location, and [PERSON]'s Trophic State Index (TSI); Table S2: Atmospheric correction possessors utilized in this study. Level-2 products of Landsat 5 and 7 (LEDAPS- and LaSRC-corrected) are readily available for download and are therefore excluded from the table below; Table S3: Summary of RS-derived Chl-\(a\) models across satellites and data categories. L5 = Landsat 5, L7 = Landsat 7, L8 = Landsat 8, and S2 = Sentinel-2. References [92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108] are cited in the Supplementary Materials. **Author Contributions:** Conceptualization, A.R.S., H.K.P.