Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery
Bibliographic record
Abstract
Algal blooms are a major environmental issue in many freshwater environments. While traditional in-situ measurements remain indispensable to monitor algal dynamics, they offer only limited spatiotemporal coverage, especially when dealing with large water bodies. Satellite remote sensing can help overcome this limitation. Here, a semi-empirical model for retrieving surface water Chlorophyll- a (Chl- a ) concentrations, a proxy of phytoplankton biomass, was developed for the western basin of Lake Ontario, one of the Laurentian Great Lakes. ACOLITE-corrected Landsat 8 and 9 imagery between 2013 and 2023 was calibrated and verified with local in-situ Chl- a measurements. The nearshore areas of Western Lake Ontario, including the semi-enclosed Hamilton Harbour, are prone to algal blooms, while oligotrophic conditions prevail in the offshore areas. Three bloom indicators—intensity, extent, and severity—were used to characterize the variability and seasonality of algal blooms in different areas of the lake. Time-series analyses revealed contrasting temporal trends in Chl- a concentrations of the nearshore and offshore waters over the eleven-year period of observation. Analysis of external factors impacting algal blooms in Western Lake Ontario and Hamilton Harbour revealed temperature, wind speed, and cloud cover as the most influential, with around 80 % of blooms occurring under moderate conditions (temperature 4–26 °C and wind speed 2.5–5. m s −1 ). Overall, our research underlines the great potential for cost-effective monitoring of algal dynamics in large lakes, utilizing publicly available satellite imagery, in order to support eutrophication management. • Landsat 8/9 imagery captures 2013–2023 algal dynamics in Western Lake Ontario • Matchup-validated semi-empirical Chl- a retrieval model outperforms pre-trained MDN • Nearshore meso-eutrophic waters contrast with offshore oligotrophic waters • Peak algal growth conditions are ≤24 °C, ∼3 m s −1 wind speed, and clear skies
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".