Modeling geophysical properties of the Algodones Dunes from field and laboratory hyperspectral goniometer measurements using GRIT and comparison with G-LiHT imagery
Bibliographic record
Abstract
We measure and describe the angular dependence of field and laboratory hyperspectral reflectance measurements of sediments from the Algodones Dunes, CA using the Goniometer of the Rochester Institute of Technology (GRIT) and compare with NASA G-LiHT hyperspectral imagery. G-LiHT imagery was acquired concurrently during a joint field experiment in March 2015 conducted by NASA Goddard, South Dakota State University, University of Arizona, University of Lethbridge, and Rochester Institute of Technology (RIT). Radiative transfer models1 and our own observations10 demonstrate that the angular dependence observed in the bidirectional reflectance distribution (BRDF)1,2,3,4,5,6 is strongly influenced by factors such as density, grain size distribution, moisture content, and surface roughness.5,6,7,8,9 Hapke’s model applied to a uniform sediment predicts increasing reflectance as density increases, however, we have observed that multiple scattering and the presence of optically contrasting mineral fractions can lead to the opposite trend.9,10 The degree of multiple scattering is influenced by incident illumination zenith angle, which determines whether the Hapke prediction is observed or the opposite trend.10 To better match observations, modifications of the model are necessary.10 In this paper, we consider some initial work showing the relationship between NASA G-LiHT hyperspectral imagery and GRIT10 field and laboratory BRDF and GRIT-Two (GRIT-T)11 laboratory BRDF. We also discuss preliminary work using this data for retrieval of geophysical properties of the sediment such as density from multi-angular measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".