Mapping leaf area index heterogeneity over Canada using directional reflectance and anisotropy canopy reflectance models using directional reflectance and anisotropy canopy reflectance models.
Bibliographic record
Abstract
Leaf area index (LAI) retrieval from remote sensing is a very active research field. In heterogeneous canopies, simulations with the canopy reflectance model Five-Scale show that nadir view vegetation indices, such as NDVI and Simple Ratio (SR), based on near infrared and red reflectance are more closely related to the gap fraction at the solar zenith angle than the LAI. The gap fraction is important in canopy light interception. At the solar zenith angle, it can be used to estimate the amount of sunlit LAI. But the knowledge of LAI and foliage heterogeneity are both needed to estimate shaded leaves that are also important in the carbon cycle. In our previous studies, we developed a methodology to retrieve the foliage heterogeneity, represented by a clumping index, from remote sensing. The retrieval is accomplished with an anisotropy index using broadband directional reflectance at the hotspot and at the darkspot. The combination of nadir, hotspot, and darkspot views allows the LAI retrieval for a given cover type. However, directional measurements are not usually acquired with the same sun-target-sensor geometry, so the anisotropy kernel-based Four-scale Linear model for AnIsotropy Reflectance (FLAIR) is used to interpolate the directional reflectance from ADEOS-POLDER data to acquire hotspot and darkspot reflectance at a common geometry in the near infrared band. A landcover map at 1-km based on SPOT-VGT data acquired in 1998 and directional POLDER data acquired over Canada in June 1997, are used to map the clumping index. The results show consistent clumping index values compared to in-situ values, and that SR and the anisotropy index are not correlated to each other, indicating that both indices are related to different canopy properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".