Inferring Inherent Optical Properties of Sea Ice Using 360-Degree Camera Radiance Measurements
Bibliographic record
Abstract
In this work, we demonstrate the utilization of a compact, consumer-grade 360-degree camera for measuring the in-ice spectral angular radiance distribution. This novel technique allows for the instantaneous acquisition of all radiometric quantities at a given depth with a non-intrusive probe. This gives the opportunity to monitor the light field structure (mean cosines) from the atmosphere to the underlying ocean beneath ice. In this study, we report vertical profiles of the light field geometric distribution measured at two sites representative of distinct ice types: High Arctic multi-year ice and Chaleur Bay (Quebec, Canada) landfast first-year ice. We also propose a technique to empirically retrieve the depth-resolved inherent optical properties by matching simulated profiles of spectral irradiances calculated with the HydroLight radiative transfer model to the observed ones. As reported in other studies, the derived reduced scattering coefficients were high (641.57 m-1, 72.85 m-1) in the top (2 cm, 5 cm) for both sites (High Arctic, Chaleur Bay) and lower in the interior part of the ice (0.48 to 4.10 m-1, 0.021 to 7.79 m-1). Due to the inherent underdetermined nature of the inversion problem, we emphasize the importance of using the similarity parameter that considers both the absorption and the reduced scattering coefficients. Finally, we believe that this radiometric device, combined with the proposed inversion technique, will allow to scale up the measurements of the inherent optical properties of different kinds of sea ice enabling to take better account of terrain variability in radiative transfer models.
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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 | 0.000 |
| 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) | 0.001 | 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".