Impact of sea ice on ocean color remote sensing
Bibliographic record
Abstract
We study two types of contamination of Ocean Color data related to the presence of sea ice. The first type, referred to as the adjacency effect, is the contamination of the radiance from the intended target by photons scattered in atmosphere towards the sensor but originating from a bright object such as sea ice nearby the target. The second type results from the presence of sub-pixel sea ice. In the case of the adjacency effect, the contribution of the icy environment to the top-of-atmosphere signal in the visible is not fully removed by the atmospheric correction algorithm, leading to an overestimation of the water-leaving reflectance. This is due to the strong spectral increase of atmospheric scattering with decreasing wavelength. The adjacency effect being more important at short wavelengths, the chlorophyll estimates based on the blue-to-green ratio will tend to decrease as the field of view approaches the ice edge. Conversely, contamination by sub-pixel sea ice results in an underestimation of the water-leaving reflectance, especially in the blue domain, and consequently to an overestimation of the chlorophyll concentration. The magnitude of the errors depends on the type of ice contaminating the pixel. It is more important for ice with high reflectance ratios for the wavebands 765 to 865 nm. Absolute error on the water-leaving reflectance up to 0.7% was observed, which is not acceptable for Ocean Color applications intending inversion of inherent optical properties from the absolute radiance, and for validation and vicarious calibration activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".