Progress in retrieving canopy structural parameters and chlorophyll content using the refined hyperspectral and multi-angle measurement concept and CASI data
Bibliographic record
Abstract
We attempt to test the refined concept of combining multi-angle and hyperspectral remote sensing proposed by [1] using airborne data. The concept proposes a system that acquires hyperspectral signals only in the nadir direction and measures in two additional directions in two spectral bands, red and NIR. It has been successfully demonstrated that the off-nadir hyperspectral simulations could be closely reconstructed based on the nadir hyperspectral reflectance and off-nadir multi-spectral reflectance in red and NIR bands. This is shown using the Compact Airborne Spectrographic Imager (CASI) data acquired over a forested area in the Sudbury region (Ontario, Canada). Through intensive validation using field data, it is demonstrated that the combination of the hotspot and darkspot reflectances has strong response to changes in vegetation clumping. Furthermore, the model inversion using a LUT approach is employed to retrieve chlorophyll content per unit leaf area. In order to explore the impact of clumping on the chlorophyll content retrieval, we compared the results of the inversion based on leaf area index (LAI) and based on effective LAI (Le). In the comparison with the field-measured data, the determination coefficient increases and RMSE decreases when LAI is considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".