Quantification and Mapping of Water Clarity for Freshwater Lakes Using Sentinel-2 Data and Random Forest Regression Model: Application on Finger Lakes, New York
Bibliographic record
Abstract
The growing effects of climate change and urbanization necessitates continuous monitoring of the freshwater resources in terms of water quality. Although remote sensing techniques have been successful in estimating water quality, its applicability over small oligotrophic lakes still remains a challenge due to the lower contribution of constituents to the water-leaving radiance. As such, this study leverages the availability of citizen science data and the synergistic use of Sentinel-2 imagery with Random Forest (RF) regression to estimate Secchi Disk Depth (SDD) over Canandaigua Lake. The results indicate an R2of 0.74, RMSE of about 0.72 m, MAE and Bias of 1.11 and 0.98, respectively. The feature importance for RF was also calculated, and the results indicate high value for visible bands. The model can be replicated for similar study areas and the findings can be used for efficient freshwater monitoring.
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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 | 0.000 | 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) | 0.001 | 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".