Four-Scale Linear Model for Anisotropic Reflectance (FLAIR) for Plant Canopies. I : Model Description and Partial Validation
Bibliographic record
Abstract
As optical remote sensing techniques provide increasingly detailed canopy reflectance data at a variety of illumination/view geometries, direct quantitative comparisons between data sets require a flexible model of the bidirectional reflectance distribution function (BRDF) suitable for inversion. Typically, such derivations rely on: 1) complex and computationally expensive empirical canopy descriptions, or 2) simplifications for specific canopy types, conditions, or view geometry. More practical would be one general model not requiring significant computing resources, but that provides information on canopy architecture when utilized as an inverse model. The Four-Scale Model, developed by Chen and Leblanc (1997), describes canopy reflectance considering four levels of architecture, distributions of tree crowns, branches, shoots, and leaves. A linear kernel-like model has been developed from this Four-Scale Linear Model for Anisotropic Reflectance (FLAIR). While simplifications are performed, an effort has been made not to limit FLAIR to specific canopy characteristics, while maintaining relationships between modeled coefficients and canopy architecture. Comparisons between Four-Scale and FLAIR, and use of FLAIR in the forward mode on multi-angular data sets obtained during BOREAS 1994, allow examination of the suitability, capabilities, and limitations of this model in describing canopy reflectance. As partial validation, this paper compares FLAIR functions to aspects of the Four-Scale Model from which they are developed. Examination of how this model reacts to inversion of simulated reflectance data sets demonstrates its ability to simulate and reproduce canopy reflectance. leading toward the retrieval of reasonable LAI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".