Assessing Polarimetric SAR Interferometry coherence region parameters over a permafrost landscape
Bibliographic record
Abstract
Rising temperatures in the Arctic are leading to recent changes in permafrost regions. The monitoring of permafrost state and dynamics benefits from the large-scale capabilities of airborne and spaceborne remote sensing as well as the opportunity to image regions that are difficult to access. In this regard, the German Aerospace Center (DLR) conducted an experimental airborne SAR campaign aiming at characterizing SAR responses over permafrost landscapes and assessing to which extent parameters of interest, such as soil moisture and soil layer properties, can be estimated in such regions [1]. The campaign comprises two data sets acquired in the Canadian Arctic in August 2018, when the upper layer of the soil is thawed, and in March 2019, when the ground is entirely frozen. Smaller (X- and C-band) and larger (L-band) wavelengths were used. This study focuses on a particular test site encompassing the Trail Valley Creek catchment (Northwest Territories), which is a well-studied area of continuous permafrost covered by tundra vegetation [2]. Previous polarimetric analysis shows an overall decrease in backscattering power of several dB from summer to winter and a relative increase of the surface contribution (entropy/alpha analysis) [3]. Simultaneously, landscape features remain clearly recognizable at all wavelengths in winter. As permafrost parameters (vegetation, soil moisture, soil type, winter snow depth) are suspected to be related with another [2], unambiguous interpretation of the signal is challenging. Adding another dimension (interferometry) related to phase center height and therefore signal penetration depth is a step towards better interpretation of the signal. As several baselines were flown over Trail Valley Creek test site and fully polarimetric data was acquired, polarimetric SAR interferometry observables can be retrieved. A particular parameter of interest is the coherence region, namely the set of coherences that can be obtained from all possible pairs of polarimetric acquisitions at a given baseline. Pol-InSAR coherence regions are characterized by their shape and extent in the complex plane. In particular, their extent in amplitude and phase in the complex plane are of interest, as they can be related to the vertical distribution of scatterers within a given resolution cell. In summer, overall small vegetation (below 1m) covers the soil, and little penetration into the ground is expected as the upper layer of the soil is thawed. In winter, the soil is entirely frozen, covered by snow, and the vegetation is partially frozen. Larger penetration into the frozen soil is expected at larger wavelengths. These effects could influence the coherence region features. An analysis and a comparison of the Pol-InSAR coherence region parameters for the winter and summer datasets over the Trail Valley Creek catchment will be presented. Several frequencies will be considered and the influence of landcover will be assessed by discriminating between different vegetation types. [1] I. Hajnsek, H. Joerg, R. Horn, M. Keller, D. Gesswein, M. Jaeger, R. Scheiber, P. Bernhard, S. Zwieback, “DLR Airborne SAR Campaign on Permafrost Soils and Boreal Forests in the Canadian Northwest Territories, Yukon and Saskatchewan: PermASAR”, POLINSAR 2019; 9th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, 2019 [2] I. Grünberg, E.J. Wilcox, S. Zwieback, P. Marsh, and J. Boike, “Linking tundra vegetation, snow, soil temperature, and permafrost”, Biogeosciences, 17(16), 4261-4279, 2020 [3] P. Saporta, A. Alonso González, I. Hajnsek, “A temporal assessment of fully polarimetric multifrequency SAR observations over the Canadian permafrost”, EUSAR 2022; 14th European Conference on Synthetic Aperture Radar, VDE, 2022
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".