Sensor-generic adjacency-effect correction for remote sensing of coastal and inland waters
Bibliographic record
Abstract
The adjacency effect distorts the top-of-atmosphere (TOA) spectral signals of coastal and inland waters and is a major challenge for optical remote sensing of nearshore aquatic environments. We introduce a closed-form expression that corrects for the adjacency effect prior to atmospheric correction. The method is included in an open-source Python tool, which ingests level-1 imagery and calculates the point-spread function of the atmosphere to convolve the input imagery. For each band, the difference between the observed and convolved reflectances is used to quantify and correct for the adjacency effect, i.e. , pixels are corrected to the TOA reflectance they would have if surrounded by pixels of identical reflectance. Validation was conducted for Sentinel-2 MSI and Landsat 8 OLI imagery against a global dataset of coincident in situ radiometric measurements. Results showed improved accuracy of water-leaving reflectance derived by atmospheric correction processors, including ACOLITE, POLYMER, and l2gen, when these were applied following adjacency-effect correction. For matchups within 200 m of shorelines ( n = 212), adjacency-effect correction resulted in an average 16.7 % reduction in root mean square error, a 32.4 % reduction in symmetric signed percentage bias, and a 36.8 % reduction in median symmetric accuracy for the three processors. The improvements were more significant in the near-infrared (NIR) range for ACOLITE, visible wavelengths for l2gen, and evenly distributed across the visible-NIR spectrum for POLYMER. We anticipate that this physics-based approach to adjacency-effect correction will lead to improved satellite-derived aquatic products for coastal and inland waters under diverse atmospheric and aquatic conditions. • We present an adjacency-effect correction tool for aquatic applications. • The code is open-source and physics-based. • The output is adjacency-free TOA reflectance, input to any AC processor. • We show substantially improved reflectance retrieved by three AC processors.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".