Retrieving freeze/thaw-cycles using Sentinel 1 data in Eastern Nunavik (Québec, Canada)
Bibliographic record
Abstract
In the terrestrial cryosphere, freeze/thaw (FT) state transitions play an important and measurable role for climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. Satellite active and passive microwave remote sensing has shown its principal capacity to provide effective monitoring of landscape FT dynamics. Sentinel-1 continues to deliver high-resolution microwave remote sensing than ever before and has therefore a large potential of usage for monitoring. In light of this, the capability and responses of its radar backscatter to landscape FT processes in different surface soil depths should be examined to provide a thorough grounding for a robust application of the F/T retrieval algorithm. This study presents a seasonal threshold approach, which examines the time series progression of remote sensing measurements relative to signatures acquired during seasonal reference frozen and thawed states. It is developed to estimate the FT-state from the Sentinel 1 database and applied and evaluated for the region of Eastern Nunavik (Québec, Canada). In this course, the FT state transitions derived from Sentinel 1 data are compared to temporally overlapping situ measurements of soil moisture from different depths within the top 20cm soil. This work allows to explore differences in the sensitivity of the Sentinel 1 at different surface soil depths in higher detail; this information is used to examine the penetration performance of the Sentinel 1 under different conditions of permafrost and permafrost-dominated landscapes. This work is dedicated to providing more accurate data to capture the spatio-temporal heterogeneity of freeze/thaw transitions. As Sentinel-1 continues to deliver high-quality information, the provided threshold algorithm delivers an extended time series to analyze FT-states and improve our understanding of related processes in permafrost landscapes.
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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 | 0.001 | 0.000 |
| 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".