Estimation of Phytoplankton Chlorophyll-a Concentrations in the Western Basin of Lake Erie Using Sentinel-2 and Sentinel-3 Data
Bibliographic record
Abstract
Algae blooms have been a serious problem in coastal and inland water bodies across Canada. The temporal and spatial variability of algae blooms makes it difficult to use in situ monitoring of the lakes. This study aimed to evaluate the potential of Sentinel-3 Ocean and Land Color Instrument (OLCI) and Sentinel-2 Multispectral Instrument (MSI) data for monitoring algal blooms in Lake Erie. Chlorophyll-a (Chl-a)-related products of these sensors were tested by using the Great Lakes Chl-a NOAA’s monitoring data over summer 2016 and 2017, respectively. Our results show that while fluorescent light height (FLH) algorithm and models are limited to lakes with Chl-a < 8 mg/m3, maximum chlorophyll index (MCI) has the potential to be used effectively to monitor Chl-a concentration over Eutrophic lakes. Sentinel-3’s MCI is suggested for Chl-a > 20 mg/m3 and Sentinel-2 MCI for Chla > 8 mg/m3. Top of atmosphere (TOA) radiances showed a significantly better correlation with in situ data compared to TOA reflectance, which may be related to the poor pixel identification during the process of pixel flagging affected by the complexity of Case-2 water. Sentinel-2 MCI achieves better performance for Chl-a retrieval (R2 = 0.92) than the existing methods. However, the FLH algorithms outperformed negative reflectance due to the shift of reflectance peak to longer wavelengths along with increasing Chl-a values.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".