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Record W2057152129 · doi:10.1109/jstars.2015.2414714

Polarimetric Decompositions of Temperate Wetlands at C-Band

2015· article· en· W2057152129 on OpenAlexafffundabout
Brian Brisco, F.J. Ahern, Sang‐Hoon Hong, Shimon Wdowinski, Kevin Murnaghan, Lori White, Donald K. Atwood

Bibliographic record

VenueIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2015
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsNatural Resources Canada
FundersCanadian Space AgencyNational Science Foundation
KeywordsWetlandMarshPolarimetryRemote sensingBackscatter (email)Vegetation (pathology)Environmental scienceSwampL bandSynthetic aperture radarGeologyScatteringComputer sciencePhysicsEcology

Abstract

fetched live from OpenAlex

C-band SAR is well established as a useful sensor for water resources applications. It is commonly accepted that the backscatter from wetlands that consist of many emergent stems over open water (swamps and marshes) is dominated by a double-bounce scattering mechanism. However, recent observations with fully polarimetric data from Radarsat-2 over the extensive wetlands of the Everglades and numerous small wetlands in Ontario appear to be inconsistent with this interpretation of the backscatter physics. In this paper, we use several forms of polarimetric analysis and decomposition. All of these indicate that the backscatter from small marshes and swamps in Ontario is dominated by polarimetric characteristics normally attributed to the odd-bounce mechanism. This anomalous result might be explained as a consequence of changes in the double-bounce reflectance properties of vegetation as a function of the incidence angle. However, detailed electromagnetic backscatter modeling will be needed to provide a more complete and reliable understanding of the details of backscattering from wetlands with emergent vegetation. Additional observational and theoretical work will be required to document and understand the unusual results we report here. If these results are substantiated, the SAR community must re-interpret the generally accepted meanings of the popular decomposition variables, and introduce new terminology to describe them. This would lead to an improved understanding of the backscatter physics and better use of polarimetric SAR for wetland management applications.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.228
Teacher spread0.206 · 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 designBench or experimental
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

Citations22
Published2015
Admission routes3
Has abstractyes

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Same venueIEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingSame topicSynthetic Aperture Radar (SAR) Applications and TechniquesFrench-language works237,207