The Airborne Cryospheric SAR System (CryoSAR): Characterizing Cold Season Hydrology Using Ku and L-Band Polarimetric SAR Observations
Bibliographic record
Abstract
The airborne cryospheric synthetic aperture radar (SAR) system, called CryoSAR, has been developed to advance our scientific expertise in estimating snow accumulation on land, lakes and sea ice, characterizing freshwater ice and sea ice properties, and monitoring the freeze-thaw state of soils. Currently, the dual frequency Ku (13.5 GHz) and L-band (1.3 GHz) instrument is focused on making backscatter and phase measurements of snow and ice on land and lakes to estimate total snow accumulation. This paper describes the core system characteristics, the instrument operation, and SAR data processing that is conducted to achieve science ready data. We illustrate the system’s capability to produce science-ready data using examples from a field experiment conducted in Ontario during the winters of 2022-23 and 2023-24. CryoSAR flights were made over a farm field site and a lake site in Ontario with field measurements. Calibrated Ku and L-band polarimetric data are presented and illustrate the sensitivity of the signals to surface and backscatter processes 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 | 0.001 |
| 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".