How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?
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Bibliographic record
Abstract
Optical sensors are increasingly sought to estimate the amount of chlorophyll a (chl_a) in freshwater bodies. Most, whether empirical or semi-empirical, are data-oriented. Two main limitations are often encountered in the development of such models. The availability of data needed for model calibration, validation, and testing and the locality of the model developed—the majority need a re-parameterization from lake to lake. An Unmanned aerial vehicle (UAV) data-based model for chl_a estimation is developed in this work and tested on Sentinel-2 imagery without any re-parametrization. The Ensemble-based system (EBS) algorithm was used to train the model. The leave-one-out cross validation technique was applied to evaluate the EBS, at a local scale, where results were satisfactory (R2 = Nash = 0.94 and RMSE = 5.6 µg chl_a L−1). A blind database (collected over 89 lakes) was used to challenge the EBS’ Sentine-2-derived chl_a estimates at a regional scale. Results were relatively less good, yet satisfactory (R2 = 0.85, RMSE= 2.4 µg chl_a L−1, and Nash = 0.79). However, the EBS has shown some failure to correctly retrieve chl_a concentration in highly turbid waterbodies. This particularity nonetheless does not affect EBS performance, since turbid waters can easily be pre-recognized and masked before the chl_a modeling.
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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