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Record W2332354772 · doi:10.1109/tgrs.2015.2502600

An Integrated Surface Parameter Inversion Scheme Over Agricultural Fields at Early Growing Stages by Means of C-Band Polarimetric RADARSAT-2 Imagery

2016· article· en· W2332354772 on OpenAlexaff
Xiaodong Huang, Jinfei Wang, Jiali Shang

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsAgriculture and Agri-Food CanadaWestern University
Fundersnot available
KeywordsRemote sensingSurface roughnessInversion (geology)Environmental sciencePolarimetryGround truthSoil scienceSoil waterWater contentScatteringGeologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

Many research studies have investigated surface parameter inversion for bare soils. This paper attempts to take into account the agricultural fields with crop residues and fields under low vegetation cover in addition to bare soil fields. An integrated surface parameter inversion scheme (ISPIS) is proposed to invert surface parameters in these agricultural fields based on the analysis of H-α parameters at the early crop growing stages, in which the calibrated integral equation model (CIEM) is adopted to invert surface parameters for bare soils, and an adaptive two-component decomposition combined with the CIEM and a simplified adaptive volume scattering model is developed for fields with crop residues and under low vegetation cover. Fully polarimetric RADARSAT-2 data with ground truth collected on April 29 and May 9 in 2013 and from May to June in 2014 are used for validation. Compared with other methods, the derived volumetric soil moisture (MV) and surface roughness (KS) of all agricultural fields are consistent with verifiable observations with the lowest overall root mean square error: 6.12 [vol.%] and 0.48, respectively, when all sample sites are considered.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.996

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.211
Teacher spread0.204 · 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

Citations46
Published2016
Admission routes1
Has abstractyes

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