First-year sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2
Bibliographic record
Abstract
Abstract. Sea ice melt pond fraction (fp), linked with lower sea ice surface albedo and increased light transmittance to the ocean, is inadequately parameterised in sea ice models due to a lack of observations. In this paper, results from a multi-scale remote-sensing program dedicated to the retrieval of level first-year sea ice (FYI) fp from dual co- and cross-polarisation C-band synthetic aperture radar (SAR) backscatter are detailed. Models which utilise the dominant effect of free-water melt ponds on the VV / HH (vertical transmit and vertical receive / horizontal transmit and horizontal receive) polarisation ratio at high incidence angles are tested for their ability to provide estimates of the subscale fp. Retrieved fp from noise-corrected Radarsat-2 quad-polarisation scenes are in good agreement with observations from coincident aerial survey data, with root mean square errors (RMSEs) of 0.05–0.07 obtained during intermediate and late stages of ponding. Weak model performance is attributed to the presence of wet snow and slush during initial ponding, and a synoptically driven freezing event causing ice lids to form on ponds. The HV / HH (horizontal transmit and vertical receive / horizontal transmit and horizontal receive) ratio explains a greater portion of variability in fp, compared to VV / HH, when ice lids are present. Generally low HV channel intensity suggests limited applications using dual cross-polarisation data, except with systems that have exceptionally low noise floors. Results demonstrate the overall potential of dual-polarisation SAR for standalone or complementary observations of fp for process-scale studies and improvements to model parameterisations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".