Leaf reflectance and transmission properties (350–2500 nm): Implications for vegetation indices
Bibliographic record
Abstract
At moderate to high leaf area index (values 3–5), many ratio-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), reach an asymptote where the linear relationship between leaf area index and vegetation index value breaks down. The red and near infrared channels are used to calculate most ratio vegetation indices when using sensors such as Landsat; however, these channels sense very different depths in vegetation canopies due to differences in transmittance, which may explain this breakdown of vegetation indices. In laboratory-simulated canopies composed of four deciduous species, visible wavelengths (∼400–700 nm) were mostly attenuated by the first or second leaf layer, while near infrared wavelengths were substantially transmitted beyond the sixth or seventh leaf layer. Absolute changes in reflectance >1% were seen in some canopies up to four leaf layers thick in the near infrared wavelengths. Therefore, in natural canopies, near infrared wavelengths have a greater probability of penetrating to the soil/litter background than visible wavelengths, which may impact vegetation indices that use both visible and near infrared wavelengths for canopies between two and seven layers thick. While this was a preliminary study that isolated the canopy depth variable, polynomial regression analysis showed that differences in canopy thickness explained most of the observed variability in canopy reflectance. These results will facilitate the development and assessment of spectral vegetation indices that would probe canopies to consistent depths.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".