Modelling SAR Backscatter from Lake Ice under Wet Conditions using the Snow Microwave Radiative Transfer (SMRT) model
Bibliographic record
Abstract
Lake ice plays a critical role in local energy balances and provides crucial socio-economic services such as travel between communities and transportation of goods during winter months. However, over the past 40 years, the number of in situ observations has declined. Additionally, increasing temperatures lead to an increasing number of melt events throughout the season, resulting in the formation of more snow ice and slush layers. The increase in wet ice conditions poses a challenge in monitoring lake ice using active microwave technologies (e.g., synthetic aperture radar) and can be a risk to those who use ice cover as an essential travel route. This study focuses on Lake Oulujärvi in Finland during the 2020-2021 ice season. Using the snow microwave radiative transfer (SMRT) model, backscatter was modelled using observations of dry and wet conditions from the field. Snow density, snow depth, microstructure data, and ice thickness data collected during the field campaign helped parameterize the Snow Microwave Radiative Transfer (SMRT) model. Simulations under dry conditions showed that increasing roughness at the ice-water interface had the largest increase in backscatter. However, when the water content of the overlying snow layers increased, the roughness of the interface with the highest moisture content became the dominant interface impacting backscatter. Melt-freeze events throughout the ice season had a prolonged impact on backscatter resulting in increases of >3.69 dB. Larger increases in backscatter due to higher moisture were a result of larger dielectric contrasts created between overlying dry snow on slush layers. Improved understanding of the impact of wet conditions on backscatter can help to improve the monitoring of hazardous lake ice conditions and aid in the further development of inversion models for lake ice properties.
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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 | 0.001 |
| 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 | 0.001 | 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".