Mapping Water Clarity in Small Oligotrophic Lakes Using Sentinel-2 Imagery and Machine Learning Methods: A Case Study of Canandaigua Lake in Finger Lakes, New York
Bibliographic record
Abstract
Optical remote sensing of water quality poses challenges in small oligotrophic lakes due to the diminishing contribution of constituents to the water-leaving radiance as water clarity increases. For monitoring water clarity over such lakes, this study utilizes machine learning models and data from citizen science to develop effective models for retrieving Secchi disk depth (SDD) in Canandaigua Lake, USA. Using Sentinel-2 band ratios as input, we trained Random Forest (RF), Adaptive Boosting (AB), Extreme Gradient Boosting (XGB), and Support Vector Regression (SVR) models using spatiotemporally distributed in-situ data within 7 days of Senitnel-2 overpass. Each model was optimized using hyperparameter tuning, and cross-validation was used for accuracy assessment to compare the models' effectiveness in retrieving SDD. The results indicate the superior performance of RF with an R2 of ∼0.74 and an RMSE of ∼0.72 m. A feature importance analysis for RF indicated high relevance of the blue and green bands ratio in estimation of SDD. The RF model was subsequently employed to generate temporal maps for Canandaigua Lake, indicating that water clarity tends to be higher during the early summer months (May and June) but declines during late summer and fall (September and October). This pattern can be closely associated with the increased algal presence in the lake. The spatial variability of the SDD indicated the possibility of greater sediments entering from the southern part of the lake. This study can be expanded to encompass other Finger Lakes, offering a comprehensive understanding of water clarity in these lake systems.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".