On the sensitivity of snow bidirectional reflectance to variations in grain characteristics
Bibliographic record
Abstract
Snow is a ubiquitous natural material that plays an important role in Earth’s climatological system and energy resource budget. Its insular and reflective properties are key factors contributing to the radiation budget of the cryosphere. Due to its prevalence at extreme latitudes, the monitoring of snowpack quantities is often performed via remote observation. These data are acquired using either satellite readings or by fixing instruments to the underside of aircraft. When acquiring data remotely, it is important to account for the angular configuration of the source illumination and the location of the instrument relative to the surface since reflection is affected by the geometry of the observation. In other words, the bidirectional reflectance distribution function (BRDF) depends on the angle of incidence of the solar illumination and the angle of observation in addition to the wavelength of the incident light. It has been recognized that the granular properties of a snowpack markedly influence its BRDF. Unfortunately, works examining the effects of snow grain characteristics, such as size and facetness, on BRDF outputs are still scarce. Moreover, measured BRDF values from field studies presented in the literature are limited to specific target samples. This further hinders a more comprehensive understanding of the effects of changes in snow characterization parameters on the bidirectional reflectance of snowpack. The measured datasets often do not provide a detailed characterization of the target samples either, which also reduces their usefulness for elucidating these effects. To address these limitations and enhance the current understanding about the sensitivity of snow BRDF to variations in grain characteristics, we have conducted controlled experiments employing a first-principles in silico experimental framework supported by measured data. Our findings unveil the qualitative effects that snow granular properties have on bidirectional reflectance of snowpack, and highlight the importance of accounting for snow granular properties in remote sensing applications. In addition, our in silico experiments provide a high-fidelity assessment of snow BRDF with respect to key wavelengths particularly relevant for remote sensing applications. More broadly, our investigation demonstrates how remote observations of snow-covered terrains can be significantly improved by the correct incorporation of snow grain characteristics into the bidirectional reflectance models used to assess snowpack properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".