Sentinel-1-Based Soil Freeze–Thaw Detection in Agro-Forested Areas: A Case Study in Southern Québec, Canada
Bibliographic record
Abstract
Nearly 50 million km2 of global land experiences seasonal transitions from predominantly frozen to thawed conditions, significantly impacting various ecosystems and hydrologic processes. In this study, we assessed the capability to retrieve surface freeze–thaw (FT) conditions using Sentinel-1 synthetic aperture radar (SAR) data time series at two agro-forested study sites, St-Marthe and St-Maurice, in southern Québec, Canada. In total, 18 plots were instrumented to monitor soil temperature and derive soil freezing probabilities at 2 and 10 cm depths during 2020–21 and 2021–22. Three change detection algorithms were tested: backscatter differences (∆σ) derived from thawed reference (Delta), the freeze–thaw index (FTI), and a newly developed exponential freeze–thaw algorithm (EFTA). Various probabilistic mixed models were compared to identify the model and predictor variables that best predicted soil freezing probability. VH polarization backscatter signals processed with the EFTA and used as predictors in a logistic model led to improved predictions of soil freezing probability at 2 cm (Pseudo-R2 = 0.54) compared to other approaches. The EFTA could effectively address the limitations of the Delta algorithm caused by backscatter fluctuations in the shoulder seasons, resulting in more precise estimates of FT events. Furthermore, the inclusion of crop types as plot-level effects within the probabilistic model also slightly improved the soil freezing probability prediction at each monitored plot, with marginal and conditional R2 values of 0.59 and 0.61, respectively. The model accurately classified observed binary ‘frozen’ or ‘thawed’ states with 85.2% accuracy. Strong cross-level interactions were also observed between crop types and the EFTA derived from VH backscatter, indicating that crop type modulated the backscatter response to soil freezing. This study represents the first application of the EFTA and a probabilistic approach to detect frozen soil conditions in agro-forested areas in southern Quebec, Canada.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".