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Record W4297830516 · doi:10.1080/07038992.2022.2110463

SAR Polarimetric Phase Differences in Wetlands: Information and Mis-Information

2022· article· en· W4297830516 on OpenAlexvenueno aff
F.J. Ahern, Brian Brisco, Michael Battaglia, Laura Bourgeau‐Chavez, D. Atwood, Kevin Murnaghan

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2022
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
FundersU.S. Fish and Wildlife Service
KeywordsPolarimetryRemote sensingBackscatter (email)WetlandEnvironmental scienceSynthetic aperture radarRadarVegetation (pathology)Climate changeGeographyGeologyScatteringOpticsPhysicsComputer science

Abstract

fetched live from OpenAlex

We have previously reported anomalous polarimetric decomposition results from SAR observations of wetlands. This is caused by the abrupt change in the phase difference between the HH and VV backscatter that occurs around the Brewster angle of the emergent vegetation. We have now developed and implemented a model for backscattering from wetlands that features a cylinder emergent from a water plane. The model was used in conjunction with an extensive set of RADARSAT-2 polarimetric observations of wetlands to provide further insights into the backscattering process. We are able to show how the abrupt Brewster transition in HH-VV phase difference varies with cylinder diameter and gravimetric moisture. We find that coherent cross-pol backscatter can result from cylindrical stems being tilted. In swamps with extensive tree mortality but primarily vertical trunks, the CPD can be used to monitor the drying of the trees and thus their fire hazard. These insights may be used to identify drying trees, indicating thawing permafrost, a potentially important climate change application in the near future. We recommend that applications researchers and users choose radar wavelengths that are considerably shorter, or longer, than the diameters of the cylinders producing the dominant double-bounce backscatter to avoid resonance effects.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.201
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

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