Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes
Bibliographic record
Abstract
Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".