{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:34:20Z","timestamp":1775745260295,"version":"3.50.1"},"reference-count":108,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Global Water Futures (GWF) program"},{"name":"Canada First Research Excellence Fund (CFREF)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Chlorophyll-a concentration (Chl-a) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-a distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2Cor, ACOLITE, ATCOR, C2RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-a concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-a concentrations. The in situ data were categorized based on location, seasonality, and Carlson\u2019s Trophic State Index (TSI). Linear regression Chl-a models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-a models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 \u03bcg\/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites.<\/jats:p>","DOI":"10.3390\/rs16091595","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T08:14:31Z","timestamp":1714464871000},"page":"1595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Comparative Evaluation of Semi-Empirical Approaches to Retrieve Satellite-Derived Chlorophyll-a Concentrations from Nearshore and Offshore Waters of a Large Lake (Lake Ontario)"],"prefix":"10.3390","volume":"16","author":[{"given":"Ali Reza","family":"Shahvaran","sequence":"first","affiliation":[{"name":"Ecohydrology Research Group, Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Remote Sensing of Environmental Change (ReSEC) Research Group, Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada"},{"name":"Water Institute, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2631-0416","authenticated-orcid":false,"given":"Homa","family":"Kheyrollah Pour","sequence":"additional","affiliation":[{"name":"Remote Sensing of Environmental Change (ReSEC) Research Group, Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada"},{"name":"Cold Regions Research Centre, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5476-0820","authenticated-orcid":false,"given":"Philippe","family":"Van Cappellen","sequence":"additional","affiliation":[{"name":"Ecohydrology Research Group, Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Water Institute, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10661-024-12554-w","article-title":"Chlorophyll-a Unveiled: Unlocking Reservoir Insights through Remote Sensing in a Subtropical Reservoir","volume":"196","author":"Mpakairi","year":"2024","journal-title":"Environ. 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