Sentinel 2 analysis of turbidity retrieval models in inland water bodies: The case study of Jordanian dams
Bibliographic record
Abstract
Abstract This study investigates the potential of using Sentinel 2 multispectral satellite images to develop turbidity retrieval models and further estimate the turbidity values of inland water bodies in Jordan. Traditionally, laboratory analysis has been used to assess surface water quality, which is expensive, time‐consuming, and requires accessing the field physically. In contrast, remote sensing technologies can detect the water contaminant level at a consistent spatial and temporal coverage. Turbidity is an essential indicator of inland water quality as it directly reflects under‐water light penetration. This study was held in three Jordanian dams, King Talal Dam, Mujib Dam, and Wadi Al Arab Dam, which vary in their water quality level. Twenty water samples were collected from each dam. Forty samples were used to calibrate the models, and the rest samples were used to validate the predictive models. The results show that Sentinel 2 near‐infrared band to green band (B8/B3) achieved high fitting accuracies with R 2 = 0.832 and root mean square error (RMSE) = 1.123 NTU. Overall, this study has demonstrated the ability of Sentinel 2 data to estimate the turbidity in different ranges of inland water bodies’ quality and indicates that remote sensing can be used as an efficient tool for monitoring inland water quality. This study presents empirical data that could act as a platform to extend future work to cover more sites and contexts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".