Anisotropic reflectance effects on spectral indices for estimating ecophysiological parameters using a portable goniometer system
Bibliographic record
Abstract
Remote sensing studies are affected by the inherently anisotropic nature of reflectance from natural surfaces. The objective of this study was to investigate the effect of anisotropic reflectance on the 970 nm water band index (WBI) for Pleurozium schreberi moss from the Fluxnet Canada Western Peatland site in northern Alberta. A series of hyperspectral bidirectional reflectance measurements from the University of Lethbridge Goniometer System (ULGS-I) were assessed for their effect on a variety of WBI and NDVI spectral indices. These indices are often used to estimate ecophysiological parameters such as plant water content, pigment content, and leaf area and subsequently have the potential to contribute to estimates of ecosystem CO2 flux across large regions. Estimates of the bidirectional reflectance distribution function (BRDF) from laboratory ULGS-I measurements of P. schreberi moss were made under controlled illumination conditions from which WBI and NDVI were computed to evaluate the variation in index magnitude by view direction. As view angle increased from nadir, WBI declined dramatically from 1.40 to 1.24, and NDVI values changed from 0.87 to 0.96. This finding increases our understanding of the effect of anisotropic reflectance on vegetation indices and enhances our ability to derive improved information from remote sensing data when these angular effects are prevalent for different surface targets.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".