Monitoring of a Terrestrial Snowfall Event on Grassland Using C-Band and L-Band Polarimetric Microwave Radars
Bibliographic record
Abstract
Terrestrial snow plays a crucial role in the Earth's system as it significantly governs the energy balance of the cryosphere. Among the diverse methods for measuring snow, remote sensing permits continuous surveillance across various space and time scales. The focus of our study is to evaluate the efficacy of co-located C-band and L-band microwave scatterometers for monitoring snowfall on the ground. In pursuit of this objective, co-polarized and cross-polarized signals were acquired both before and after the occurrence of a snowfall event. The Normalized Radar Cross Sections (NRCS) of C-band and L-band scatterometers were examined throughout the entire study duration. Our findings reveal that both L-band and C-band scatterometers promptly detected wet snow accumulation on the ground. Specifically, C-band at a lower elevation incidence angle (30°) and L-band at a higher incidence angle (55°) exhibited the greatest sensitivity to the accumulation of wet snow on the ground.
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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 | 0.000 | 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 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".