Lake and river ice classifications with SWOT
Bibliographic record
Abstract
Lake and river ice play an essential role in northern latitudes, contributing to various human activities (such as transportation and fishing), weather regulation, aquatic ecosystems and as an indicator of climate change. Remote sensing data are increasingly used to monitor lake and river ice due to the scarcity of in situ observations, but significant challenges remain due to some limitations of optical and C, L, and X-band radar sensors. More accurate and frequent measurements of ice dynamics are also required. The SWOT satellite, with its Ka-band radar interferometer and high spatial and temporal coverage, offers an unprecedented opportunity to monitor lake and river ice. With SWOT data available since March 2023, we now have two whole winter seasons to study the signal on lake and river ice. As backscatter (sig0) decreases but interferometric coherence remains high (or phase noise standard deviation remains low), ice-covered areas can be detected. However, the signal also varies with the incident angle, requiring the development of an algorithm that takes sig0, coherence, phase noise standard deviation,and incident angle into account. Pixel-by-pixel classification with a random forest allows the monitoring of the spatial variability of ice cover. Pixels are classified into four categories: Water (original SWOT classes 3 and 4), Darkwater (class 5), Low-Coherence (classes 6 and 7) and Ice. The figure below shows an example for Richardson Lake in Canada in spring 2025. The pixel-by-pixel classification is then used in a second random forest to develop a dynamic ice flag for the HR_LakeSP product. The lake-scale classification was developed on lakes in Canada (15 lakes) and Norway (9 lakes) and tested on independent lakes (45 in Canada and 30 in Norway) during the 2024-2025 winter season, achieving an precision of more than 92% when compared to the visual interpretation of Sentinel-2 images, regardless of incident angle, lake size, or region. This classification will be applied to lakes in other regions (e.g. Alaska and Tibetan Plateau) in the next months to validate its global application. A similar classification for rivers is currently being developed at HR_RiverSP node level, again using pixel-by-pixel classification and random forest.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".