Polarimetric Synthetic Aperture Radar Measurements of Snow Covered First-Year Sea Ice
Bibliographic record
Abstract
This study examines the utility of fully polarimetric C-and SAR parameters and three-component scattering model to quantify the sensitivity of snow covered first-year sea ice (FYI) to radar incidence angles and surface air temperature (SAT) during the late winter transition. This three-component scattering model is based on surface, double-bounce and volume scattering contributed from various materials and surface properties. RADARSAT-2 C-band fully polarimetric synthetic aperture radar (POLSAR) data is utilized to quantify the sensitivity of thermodynamic effects (-8° C and -0.4° C ) of the polarimetric backscatter signature on mean snow cover thickness ranging from 8 cm to 36 cm with in-situ geophysical data from four different validation sites along with two different radar incidence angles 29° (steep) and 39° (shallow) to discriminate between snow-covered smooth, rough and deformed FYI. The result shows enhanced discrimination at shallower incidence angles compared to steeper ones in both 2-D and 3-D plots. The double-bounce scattering contribution is low for all FYI types at both incidence angles which are attributed to shallower incidence angle as microwave energy being in direct contact with a greater volume of higher dielectric brine coated, enlarged snow grains in the basal layer compared to the steeper incidence angles. The results also show that surface scattering dominates for all three FYI types at both incidence angles and decreases with increasing surface roughness and radar incidence angles whereas opposite results observed for volume scattering mechanisms. The results indicate that higher variation measured for thin snow class ~7 dB for both co- cross-polarization backscatters compared to thick snow class ~1 dB which corroborates with electro-thermodynamic theory.
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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) | 0.003 | 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".