Microwave scattering in the Antarctic megadunes region: reconciling radar and radiometry
Bibliographic record
Abstract
Icy surfaces across the solar system display unusual microwave radar and radiometry properties, including very high backscattering cross-sections and polarization ratios. At low temperature, snow and ice are very transparent to microwaves, leading to long path lengths and multiple scattering. Yet despite the large volume of available passive and active microwave satellite observations over the Earth cryosphere, physical interpretation of the co-variability of the multi-frequency observations is still challenging, especially when trying to reconcile radiometry and radar observations. To shed light on microwave scattering in icy regoliths, we focus on the Antarctic megadunes region, the coldest and driest area on Earth, which we propose as a new analog for icy satellites due to its very low precipitation (net zero snow accumulation) and temperature (averaging -50°C), combined with the highest microwave backscatter in Antarctica. We assemble a dataset consisting of 5.2 GHz ASCAT and 13.4 GHz QuikSCAT and OSCAT scatterometry, as well as AMSR2 radiometry at 6.9 to 89 GHz. Using the Snow Microwave Radiative Transfer (SMRT) model with a simplified snowpack with constant temperature and continuously increasing grain size and density with depth, we simulate simultaneously radar and radiometry. For the first time, we show that scatterometry and 6.9 to 37 GHz radiometry at V polarization can be successfully simulated with a unique simple snowpack model, indicating that incoherent volume scattering on subsurface heterogeneities dominates both the active and passive signal. The success of our approach encourages further work to analyze and simulate jointly active and passive microwave observations, both in the Earth cryosphere and on icy moons.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".