Aerial Remote Sensing of Aquatic Microplastic Pollution: The State of the Science and How to Move It Forward
Bibliographic record
Abstract
Microplastics (MPs) are pervasive environmental contaminants in aquatic systems. Due to their small size, they can be ingested by aquatic biota, and numerous negative effects have been documented. Determining the risks to aquatic organisms is reliant on characterizing the environmental presence and concentrations of MPs, and developing efficient ways to do so over wide scales by means of aerial remote sensing would be beneficial. We conducted a systematic literature review to assess the state of the science of aerial remote sensing of aquatic MPs and propose further research steps to advance the field. Based on 28 key references, we outline three main approaches that currently remain largely experimental rather than operational: remote sensing of aquatic MPs based on (1) their spectral characteristics, (2) their reduction of water surface roughness, and (3) indirect proxies, notably other suspended water constituents. The first two approaches have the most potential for wide-scale monitoring, and the spectral detection of aquatic MPs is seemingly the most direct approach, with the fewest potential confounding factors. Whereas efforts to date have focused on inherently challenging detection in coarse-resolution satellite imagery, we suggest that better progress could be made by experimenting with image acquisition at much lower altitudes and finer spatial and spectral resolutions, which can be conveniently achieved using drones equipped with high-precision hyperspectral sensors. Beyond developing drone-based aquatic MP monitoring capabilities, such experiments could help with upscaling to satellite-based monitoring for global coverage.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".