Comparison of snowmelt timing estimates from Sentinel-1 SAR and surface observations in British Columbia, Canada
Bibliographic record
Abstract
Snowmelt provides critical water resources that impact ecosystem health and hazard frequency; however, the timing of melt is difficult to infer across large spatial scales. While Synthetic Aperture Radar (SAR) has been used to detect snowmelt onset, the accuracy of different methodological approaches requires evaluation. We use Sentinel-1 SAR observations to estimate snowmelt timing at automated snow water equivalent (SWE) stations ( n = 52) across British Columbia between 2018 and 2021. The timing of backscatter minima are compared to snowmelt onset estimates derived from continuous SWE and surface air temperature records. First, we develop a manual selection method, which requires SWE and air temperature records, to determine the feasibility of SAR for estimating snowmelt onset. Using this approach, we demonstrate that images with identical viewing geometries (i.e., those from the same orbital track) should be utilized for snowmelt onset estimation with SAR, and that including the timing of local minima, a minimum value over an interval, as potential dates of snowmelt initiation increases accuracy compared to the use of absolute minima alone. Accuracy further increases when snowmelt analysis is constrained to specific timeframes informed by region and topography, with error reduced by greater than 50 % in some study years. After demonstrating the potential accuracy of SAR with manually selected estimates (RMSE = 5.3 d), we develop an automated method that can produce SAR estimates of melt onset that are independent of surface observations. While accuracy was reduced from automated approaches, the mean date of absolute backscatter minima in track-separated SAR time series (both cross and co-polarized images) provided the most accurate automated estimates of snowmelt (RMSE = 11 d). We observed no spatially coherent bias in SAR error which suggests that SAR observations can be used to detect snowmelt onset across large regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".