Modeling and Measurement of C-Band Radar Backscatter From Snow-Covered First-Year Sea Ice
Bibliographic record
Abstract
In this paper, we present model and measurement results for C-band HH and VV normalized radar cross-sections (NRCS) from winter snow-covered first-year sea ice with average snow thicknesses of 16, 4, and 3 cm. The brine content in snow pack was low in all three case studies, which is typical for cold winter conditions. We used the first-order approximation of the small perturbation theory accounting for surface scattering from the air-snow and snow-ice rough interfaces and continuously layered snow and sea ice. The experimental data were collected during the Circumpolar Flaw Lead system study in the winter of 2008 in the southern Beaufort Sea from the research icebreaker Amundsen. Good agreement between the model and experimental data were observed for all three case studies. The model results revealed that the scattering at the snow-ice rough interface is usually stronger than that at the air-snow interface. Furthermore, both model and experimental NRCS values (at VV and HH polarizations) were considerably higher for thin-snow cover compared with the thick-snow-cover case. We associate this effect with the lower attenuation of the propagated wave within the thin-snow pack in comparison to the thick-snow pack. We also demonstrated that different brine volume contents in snow with close thicknesses of 4 and 3 cm did not affect the backscattering coefficients at certain incidence angles and polarization. Our findings provide the physical basis for winter snow thickness retrieval and suggest that such retrievals may be possible from radar observations under particular scattering conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".