Snow Water Equivalent Estimation for Flooding Warning Utilizing InSAR Technique on RCM Data
Bibliographic record
Abstract
Accurate estimation of snow water equivalent (SWE) is crucial for effective flood forecasting and disaster management. The paper develops a technology that can provide a reliable estimation and monitoring of snow water equivalent (SWE) for potential snowmelt flood events utilizing RADARSAT Constellation Mission compact polarimetric data. Leveraging 4-day repeat-pass interferometry, interferometric SAR (InSAR) technology was employed to estimate snow depth by analyzing SAR signal propagation delays caused by snowpack. Snow depth estimations closely aligned with ground truth measurements from nearby weather stations, demonstrating the capability of InSAR in detecting dynamic snow depth changes. Snow density was derived using a backscatter-based inversion model, enabling dynamic SWE estimation as the product of snow depth and density. The resulting SWE maps and time-series analyses showed strong correlations with weather station data, validating the accuracy and reliability of the developed methodology.
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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) | 0.000 | 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) | 0.001 | 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".