Observations of River Ice Breakup Using GNSS-IR, SAR, and Machine Learning
Bibliographic record
Abstract
Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) is an emerging sensor technique that has become well-established for water level monitoring. While GNSS-IR has previously been employed for monitoring properties of lake ice and sea ice, it has not been applied for monitoring river ice. This paper presents results from monitoring river ice breakup at three sites in Canada. GNSS-IR data was compared to co-located time-lapse camera imagery and it was found that GNSS-IR signal was sensitive to periods where there is rough or broken ice in view of the sensor. Using data from Sentinel-1 and the RADARSAT Constellation Mission (RCM), the first ever comparison of GNSS-IR with Synthetic Aperture Radar (SAR) imagery is presented and a negative correlation of -0.8 is found between the GNSS-IR spectral power and SAR backscatter. Three classification algorithms of varying complexity (K-means clustering, neural network and random forest) are explored for detecting river ice using GNSS-IR. Using a shallow neural network with two hidden layers, an optimal accuracy of up to 94% is achieved over all three sites, or 97% when mixed water-ice conditions are excluded from the analysis. In summary, GNSS-IR has strong potential for ice monitoring applications, including monitoring the formation of ice jams.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".