Augmenting Satellite Remote Sensing with AERONET-OC for Plume Monitoring in the Chesapeake Bay
Bibliographic record
Abstract
Satellite observations provide broad spatial coverage of complex coastal environments but may lack temporal resolution to capture rapid changes in these dynamic systems. This study explores the potential of the recently installed NASA Aerosol Robotic Network Ocean Color (AERONET-OC) in the Chesapeake Bay, USA, both for comparison with satellite remote sensing and to complement the satellite observations by filling temporal gaps at a fixed site. Using AERONET-OC’s effectiveness as a validation tool through comparisons with multi- and hyperspectral satellites, we find agreement between AERONET-OC and satellite remote sensing reflectance measurements in the Chesapeake Bay. We use AERONET-OC to estimate total suspended matter transport through the upper bay, revealing a 3-day lag of sediment plume transport from riverine discharge to the AERONET-OC site. During the 2023 Canadian wildfire smoke episode, AERONET-OC aerosol optical depth measurements in the Chesapeake Bay agree with satellite products while capturing diurnal variations that are not observable through daily satellite passes. This study demonstrates the potential of continuous in situ monitoring by AERONET-OC to complement satellite observations with higher frequency, important for capturing extreme events that may be missed by daily satellite overpass or are less frequent when cloudy.
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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 | 0.000 | 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) | 0.001 | 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".