Validation of Permafrost Active Layer Estimates from Airborne SAR Observations
Bibliographic record
Abstract
In permafrost regions, active layer thickness (ALT) observations measure the effects of climate change and predict hydrologic and elemental cycling. Often, ALT is measured through direct ground-based measurements. Recently, synthetic aperture radar (SAR) measurements from airborne platforms have emerged as a method for observing seasonal thaw subsidence, soil moisture, and ALT in permafrost regions. This study validates airborne SAR-derived ALT estimates in three regions of Alaska, USA using calibrated ground penetrating radar (GPR) geophysical data. The remotely sensed ALT estimates matched the field observations within uncertainty for 79% of locations. The average uncertainty for the GPR-derived ALT validation dataset was 0.14 m while the average uncertainty for the SAR-derived ALT in pixels coincident with GPR data was 0.19 m. In the region near Utqiaġvik, the remotely sensed ALT appeared slightly larger than field observations while in the Yukon-Kuskokwim Delta region, the remotely sensed ALT appeared slightly smaller than field observations. In the northern foothills of the Brooks Range, near Toolik Lake, there was minimal bias between the field data and remotely sensed estimates. These findings suggest that airborne SAR-derived ALT estimates compare well with in situ probing and GPR, making SAR an effective tool to monitor permafrost measurements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".