Utility of satellite imagery in estimating coastal marine water attributes
Bibliographic record
Abstract
Coastal water resources are essential for sustaining biodiversity and community well-being, yet rapid population growth and climate change increasingly threaten their sustainability. Satellite remote sensing has emerged as a powerful tool for monitoring coastal water quality due to its extensive spatial coverage, cost effectiveness, and rapid data acquisition. The scientific community has seen considerable advances in recent years through these technologies. In view of these developments, this study presents a scoping review of 465 peer-reviewed journal articles published between 2019 and 2024, sourced from Scopus. The analysis identifies commonly used satellite platforms for assessing five critical water quality parameters chlorophyll-a (Chl-a), temperature, colored dissolved organic matter (CDOM), pH, and phosphate across predefined climatic zones and water types. We further examine prevalent algorithmic approaches and validation metrics. Findings indicate that most studies rely on data from Aqua, Sentinel, and Landsat satellites. Results also reveal that Chl-a and temperature are the most widely measured parameters, particularly in temperate and subtropical marine waters, whereas Arctic regions and freshwater systems remain understudied. Recent trends show a growing reliance on empirical and machine learning based algorithms, with root mean square error (RMSE) and coefficient of determination (R 2 ) as the most common validation metrics. These results highlight the need for standardized validation protocols and expanded research efforts in underrepresented regions and parameters to enhance global water quality 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".