Retrieval of Forest Canopy Parameters by Inversion of the PROFLAIR Leaf-Canopy Reflectance Model Using the LUT Approach
Bibliographic record
Abstract
The potential of simulating broad leaf forest canopy spectral reflectance using a canopy-leaf PROFLAIR (PROSPECT + FLAIR) model was investigated in this study. The model was inverted with hyperspectral Hyperion data using a look up table (LUT) approach to retrieve canopy leaf area index (LAI), leaf chlorophyll content <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$({\rm C}_{{\rm a}+{\rm b}})$</tex></formula> and canopy integrated chlorophyll content <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$({\rm LAI}\times{\rm C}_{{\rm a}+{\rm b}})$</tex></formula> . The LUT was populated by simulating the model in forward mode using a space of realization generated based on the specific distribution of the input parameters and based on a priori information from the field. The estimated variables were then compared to ground measurements collected in the field. The results showed the ability of the PROFLAIR model to realistically simulate canopy spectral reflectance. When compared to ground measurements, the model showed a reasonable performance to retrieve canopy LAI with an RMSE of 0.47 and leaf chlorophyll content with an RMSE of 4.46 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu$</tex></formula> g/cm <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$^{2}$</tex></formula> .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".