Sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 1: In situ observations
Bibliographic record
Abstract
Abstract. An understanding of the evolution of melt ponds on Arctic sea ice is important for climate model parameterizations, weather forecast models, and process studies involving mass, energy and biogeochemical exchanges across the ocean-sea ice–atmosphere interface. A field campaign was conducted on landfast first-year sea ice in the Canadian Arctic Archipelago during the summer of 2012, to examine the potential for estimating melt pond fraction from C-band synthetic aperture radar (SAR). In this study, in situ dual-polarisation radar scatterometer observations of pond covered ice are combined with surface physical measurements to analyse the effects of radar and surface parameters on backscatter. LiDAR measurements of ice surface roughness and ultrasonic wind-wave height profiles of melt ponds are used to quantify the sea ice surface rms-height. Variables contributing to the roughness of wind-generated melt pond surface waves within the fetch-limited pond environment are evaluated, and we show that pond roughness and backscatter cannot be explained by wind speed alone. The utility of the VV / HH polarisation ratio (PR) for retrieving melt pond properties including pond fraction, due to the dielectric contrast between free surface water and sea ice, is demonstrated and explained using Bragg scattering theory. Finally, the PR approach is discussed in the context of retrievals from satellite C-, L-, and P-band dual-polarisation SAR.
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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".