Estimation of Phytoplankton Chlorophyll-a Concentration in the Western Basin of Lake Erie Using Sentinel-2 and Sentinel-3 Data
Why this work is in the frame
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Bibliographic record
Abstract
Worldwide phenomena called algae bloom has been recently a serious matter for inland water bodies. Temporal and spatial variability of the bloom makes it di cult to use in-situ monitoring of the lakes. This study aimed to evaluate the potential of Sentinel-3 Ocean and Land Colour Instrument (OLCI) and Sentinel-2 Multispectral Instrument (MSI) data for monitoring algal blooms in Lake Erie. Chlorophyll-a (Chl-a) related products were tested using NOAA-Great Lakes Chl-a monitoring data over summer 2016 and 2017. Thematic water processor, fluorescence line height/maximum chlorophyll index (MCI) and S2 MCI, plug-in SNAP were assessed for their ability to estimate Chl-a concentration. We processed both Top of the Atmosphere (TOA) reflectance and radiance data. \nResults show that while FLH algorithms are limited to lakes with Chl-a < 8 mg m-3, \nMCI has the potential to be used effectively to monitor Chl-a concentration over eutrophic lakes. Sentinel-3 MCI is suggested for Chl-a > 20 mg m-3 and Sentinel-2 MCI for Chla > 8 mg m-3. The different Chl-a range limitation for the MCI products can be due to the different location of the maximum peak bands, 705 and 709 for MSI and OLCI sensors respectively. TOA radiances showed a signi cantly better correlation with in situ data compared to TOA reflectances which may be related to the poor pixel identi cation during the process of pixel flagging affected by the complexity of Case-2 water. Our fi nding suggests that Sentinel-2 MCI achieves better performance for Chl-a retrieval (R2 = 0.90). However, the FLH algorithms outperformed showing negative reflectance due to the shift of reflectance peak to longer wavelengths along with increasing Chl-a values. Although \nthe algorithms show moderate performance for estimating Chl-a concentration; this study demonstrated that the new satellite sensors, OLCI and MSI, can play a signi ficant role in the monitoring of algae blooms for Lake Erie.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it