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Record W4233576601 · doi:10.4095/219791

The effect of soil and crop residue characteristics on polarimetric radar response

2002· report· en· W4233576601 on OpenAlexaff
H McNairn, C Duguay, B Brisco, T J Pultz

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPolarimetryRemote sensingScatteringRadarPedestalLinear polarizationPolarization (electrochemistry)Environmental scienceSynthetic aperture radarOpticsPhysicsGeologyGeographyChemistryComputer science

Abstract

fetched live from OpenAlex

Although the interaction between linear polarized microwaves and agricultural targets has been studied extensively, far less is understood about the added information provided from polarimetric Synthetic Aperture Radars (SARs). Using 1994 Spaceborne Imaging Radar-C (SIR-C) data, this study examines the sensitivity of linear polarizations and polarimetric parameters to conditions present on agricultural fields during the period of preplanting and postharvest. The polarimetric parameters investigated include circular polarized backscatter, pedestal height, and co-polarized phase differences (PPD). The co-polarizations signature plots are also discussed. Results indicate that the dominant scattering mechanism from these fields varies depending on the type and amount of residue cover, and whether the crop had been harvested. Radar parameters most sensitive to volume and multiple scattering perform best at characterizing these surface conditions. These parameters are the pedestal height, as well as the linear cross-polarization (HV) and the circular co-polarizations (RR). The co-polarizations signature plots and the standard deviation associated with the PPD are also useful in categorizing these cover types. However, the field average PPD provides little information on residue and soil characteristics.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 designNot applicable
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

Citations9
Published2002
Admission routes1
Has abstractyes

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