High Angular Resolution Measurements of the Anisotropy of Reflectance of Sea Ice and Snow
Bibliographic record
Abstract
We introduce a new method to determine the anisotropy of reflectance of sea ice and snow at spatial scales from 1 m 2 to 80 m 2 using a multispectral circular fish‐eye radiance camera (CE600). The CE600 allows measuring radiance simultaneously in all directions of a hemisphere at a 1° angular resolution. The spectral characteristics of the reflectance and its dependency on illumination conditions obtained from the camera are compared to those obtained with a hyperspectral field spectroradiometer manufactured by Analytical Spectral Device, Inc. (ASD). Results confirm the potential of the CE600, with the suggested measurement setup and data processing, to measure commensurable sea ice and snow hemispherical‐directional reflectance factor, HDRF, values. Compared to the ASD, the reflectance anisotropy measured with the CE600 provides much higher resolution in terms of directional reflectance ( N = 16,020). The hyperangular resolution allows detecting features that were overlooked using the ASD due to its limited number of measurement angles ( N = 25). This data set of HDRF further documents variations in the anisotropy of the reflectance of snow and ice with the geometry of observation and illumination conditions and its spectral and spatial scale dependency. Finally, in order to reproduce the hyperangular CE600 reflectance measurements over the entire 400–900 nm spectral range, a regression‐based method is proposed to combine the ASD and CE600 measurements. Results confirm that both instruments may be used in synergy to construct a hyperangular and hyperspectral snow and ice reflectance anisotropy data set.
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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 | 0.000 |
| 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".