Effect of soil surface roughness and scene components on soil surface bidirectional reflectance factor
Bibliographic record
Abstract
Wang, Z., Coburn, C. A., Ren, X. and Teillet, P. M. 2012. Effect of soil surface roughness and scene components on soil surface BRF. Can. J. Soil Sci. 92: 297–313. Bidirectional Reflectance factor (BRF) data of both rough [surface roughness index (SRI) of 51%] and smooth soil surfaces (SRI of 5%) were acquired in the laboratory under 30° illumination zenith angle using a Specim V10E imaging spectrometer and an Ocean Optics non-imaging spectrometer mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5) and version 2.0 (ULGS-2.0), respectively. Under controlled laboratory conditions, the rough soil surface exhibited higher spectral reflectance than the smooth surface for most viewing angles. The BRF of the rough surface varied more than the smooth surface as a function of the viewing zenith angle. The shadowing effect was stronger for the rough surface than for the smooth surface and was stronger in the forward-scattering direction than in the backscattering direction. The pattern of the BRF generated with the non-image based data was similar to that generated with the whole region of interest (ROI) of the image-based data, and that of the whole ROI of the image-based data was similar to that of the illuminated scene component. The BRF of the smooth soil surface was dominated by illuminated scene component, i.e., the sunlit pixels, whereas the shaded scene component, i.e., the shaded pixels, was a larger proportion of the BRF of the rough soil surface. The image-based approach allowed the characterization of the contribution of spatial components in the field of view to soil BRF and improved our understanding of soil reflectance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".