## 1 Introduction
Not surprisingly, these studies show that models to retrieve Chl-_a_ concentration from satellite data tend to perform best for the data range and the region they were trained on. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Study Reference**} & \multirow{3}{*}{ \begin{tabular}{c} **Total Number of** \\ **Chk Retrieval** **Indexes** \\ **Points** \\ \end{tabular}  } & \multicolumn{3}{c}{**Water Turbining**} & \multicolumn{3}{c}{**Imagger Matchups**} & \multicolumn{1}{c}{**Temporal**} & \multirow{2}{*}{ \begin{tabular}{c} **Temporal** \\ **Vortex of** \\ **Chk- Retrieval** \\ **Coll-Data** \\ **Points** \\ \end{tabular}  } & \multicolumn{1}{c}{**Temporal**} & \multirow{2}{*}{\ \begin{tabular}{c} **Convergence of Chk-a** \\ **Concentration** \\ **Availability** \\ \end{tabular}  } & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**Radiometric**} & \multirow{2}{*}{ \begin{tabular}{c} **Comparable \({}^{*}\)** \\ **Sensors** \\ \end{tabular}  } & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**Atmospheric Corrections**} & \multicolumn{1}{c}{**Multiups**} & \multicolumn{1}{c}{**Coverage of**} \\  & & & & & & & & & \\ \hline
[17] & 5 & 68–727 & 0-830 & ✓ & OLI MSI & N/A & ACOUITE, C2 RCR, GS, MECT2, OC-SMART, & \(\pm\)3 and 3 h & N/A \\  & 3 & 34 & & & & & & & \\ \hline
[18] & 3 & 34 & & OLI & 2 & DGS, ATCOR, DSEP, EDN8 & \(\pm\)5 days & 2018–2019 \\  & 5 & 51 & 0-181 & ✓ & MSI & 3 & DGS, ATCOR, DSEP, EDN8 & \(\pm\)5 days & 2018–2019 \\  & 3 & 1099–1668 & N/A & ✓ & MSI & 5-35 & ACOUITE, C2 RCR, KOB, Regn, Polymer, & \(\pm\)3 and 24 h & 2015–2016 \\  & & & & & & & & & \\ \hline
[20] & 6 & 351 & 1-65 & ✗ & OLI & 12 & DGS & \(\pm\)2 and 5 days & 2013–2015 \\  & 9 & 146 & 0-399 & ✓ & MSI & 41 & C2 RCR, CZC, GDN, Polymer & \(\pm\)3 h & 2017–2021 \\  & & & & & & & & & \\ \hline
[22] & 6 & 97 & 0-250 & ✓ & MSI & 3 & ACOUITE, C2 RCR, GS, KOR, SOB, ENSOSO & \(\pm\)1 day & 2018–2019 \\  & & & & & & & & & \\ \hline
[23] & 4 & 139 & 0-15 & ✓ & OLI & 61 & SeqDA, ACOUITE (DSF and EXP), & \(\pm\)1 h & 2019–2021 \\  & & & & & & & & & \\ \hline
[24] & 17 & 120 & 0-13 & ✗ & TM, ETM+, ETM+, ETM+, ETM+  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study Reference**} & \multirow{2}{*}{ \begin{tabular}{c} **Total Number of** \\ **Chl- Retrieval** **Indexes** \\ \end{tabular}  } & \multicolumn{4}{c}{**Water Turbining**} & \multicolumn{2}{c}{**Imager Matchups**} & \multicolumn{1}{c}{**Temporal**} & \multicolumn{1}{c}{**Temporal**} \\ \cline{2-10}  & & **Number of** & **Range of Chl-a** & **Radiometric** & & & & **Window of** & **Coverage of** \\  & & **  
The aim of this study is to build on previous studies mentioned in Table 1 by expanding the ranges of Chl-\(a\) retrieval indexes, atmospheric correction processors, satellites, and in situ matchup data included in our analyses. Specifically, we consider 27 Chl-\(a\) retrieval indexes, 11 atmospheric correction processors, four medium-spatial-resolution (10-30 m resolution) satellites, and 600 in situ matchup data points. Another novelty of our work is the categorization of the in situ Chl-\(a\) data based on seasonality, location, and Carlson's Trophic State Index (TSI). This is followed by the derivation of separate regression-based models for each data category and satellite, which are then compared for their performance in retrieving Chl-\(a\) concentrations for both nearshore and offshore waters of Western Lake Ontario. The results of our work should inform future assessments of the changing spatial-temporal Chl-\(a\) distributions in Lake Ontario and other large lakes, thereby contributing to global efforts to use RS in water quality monitoring and management.