A mutlisensor C-band synthetic aperture radar (SAR) approach to retrieve freeze/thaw cycles: A case study for a low Arctic environment
Bibliographic record
Abstract
Abstract. This study investigates the spatial variability of surface freeze/thaw (F/T) cycles in low arctic tundra retrieved from multisensor SAR backscatter time series. To increase the temporal resolution of SAR observations, we combined measurements from Sentinel-1 and RADARSAT-2. An incidence angle normalization was applied to the backscatter time series to remove the influence of the acquisition angle on backscatter. A seasonal threshold algorithm (STA) was used to detect F/T transitions and applied to HH, HV and HH+HV polarization datasets. The classification threshold was optimized using soil temperature measurements from spatially distributed sites. A detection accuracy of over 93 % was calculated with an optimized classification threshold of 0.62 for the HH+HV time series on those sites. We created surface F/T day of the year (DOY) maps of the study area for the 2018 and 2019 freezing transitions, and for the 2019 thawing transition using the HH+HV time series with the optimized classification threshold. Those maps were combined with a terrestrial ecosystem (ecotype) map to investigate the impact of ecotypes on the F/T transitions. Three generalized least squares (GLS) models were fitted on the coupling of the maps. Differences of about 2–3 days were observed between ecotype classes. Based on these differences, we hypothesize that differences during the freezing transition were probably due to the underlying soil moisture and during the thawing transition, to the influence of vegetation. Our study demonstrates the power of merging two C-band SAR time series to create near-daily F/T maps over arctic environment to allow for better understanding of surface F/T processes happening at small spatial scale in arctic environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".