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Record W4294682788 · doi:10.1080/07038992.2022.2114439

Temporal Variation in Surface Bidirectional Reflectance of the Railroad Valley Vicarious Calibration Test Site in Nevada

2022· article· en· W4294682788 on OpenAlexaffvenue
Nicole Byford, Craig A. Coburn

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2022
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsBidirectional reflectance distribution functionRemote sensingNadirReflectivityIrradianceEnvironmental scienceCalibrationBackscatter (email)Diurnal temperature variationTest siteRange (aeronautics)OpticsGeologyAtmospheric sciencesSatellitePhysicsMaterials science

Abstract

fetched live from OpenAlex

Spectral reflectance-based vicarious calibration (VicCal) requires accurate characterization of the bidirectional reflectance distribution function (BRDF) of the ground-based target. Railroad Valley (RRV) Playa, Nevada, has been used as a VicCal test site since 1995 as it is large, appears stable over time, and has a reasonably consistent surface. This study presents the results of a diurnal measurement cycle that closely replicated illumination geometries for Earth Observing (EO) satellites over a year. By measuring the rate of change of the BRDF with respect to time, we recorded the range of BRDF effects while holding the surface constant with respect to moisture and surface condition variation. The rate of spectral reflectance change increased rapidly with view angle in the backscatter direction, reaching rates of change that are 2.3 and 10.5 times greater in the backscatter than in the forward scatter direction for view angles of 20° and 40°, respectively. The results show that larger off-nadir viewing angles in the backscatter direction are particularly sensitive to changes in solar/view geometries.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.998

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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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 designSimulation or modeling
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

Citations6
Published2022
Admission routes2
Has abstractyes

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