Spaceborne GNSS-R Sea Ice Detection Using Delay-Doppler Maps: First Results From the U.K. TechDemoSat-1 Mission
Bibliographic record
Abstract
In this paper, a scheme is presented for detecting sea ice from Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDM). Less spreading along delay and Doppler axes were observed in the DDMs of sea ice relative to those of seawater. This enables us to distinguish sea ice from seawater through studying the values of various DDM observables, which describe the extent of DDM spreading. The area associated with a DDM that results in an observable below or above a threshold value will be classified as covered by sea ice and seawater, respectively. In particular, this study applies an adaptive incoherent summation to each DDM with efforts to increase signal-to-noise ratio and avoid the averaging between DDMs collected over surfaces of different types. Accordingly, an adaptive threshold is employed for the derived observable based on the incoherent summation interval for its corresponding DDM. The proposed sea ice detection method is tested with five different DDM observables. Through comparing DDM observable-based detection results with ground-truth sea ice data, the feasibility of this method is validated with an accuracy of up to 99.73% based on the pixel number observable.
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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 | 0.001 | 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) | 0.000 | 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".