Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data
Bibliographic record
Abstract
Leaf chlorophyll content is a useful parameter for assessing vegetation physiological status and dominates the spectral signal of leaf and canopy reflectance at visible wavelengths. Using hyperspectral instruments, we quantified leaf chlorophyll content and optical properties for 255 overstory and understory leaf samples through the growing season in a mature sugar maple (Acer saccharum) stand. Strong seasonal and canopy-height-related differences were observed in both leaf chlorophyll content and leaf reflectance and transmittance spectra. Seasonal and canopy-height-related variation in leaf spectra were closely related to leaf chlorophyll content. We estimated leaf chlorophyll content using two approaches, namely empirical spectral indices, and a mathematical inversion of the leaf optical model PROSPECT. Both estimates were highly correlated with the measured leaf chlorophyll content; however, the spectral indices resulted in greater accuracy, with the best-performing index (modified simple ratio) showing an accuracy of R2 = 0.88 and RMSE = 3.94 µg/cm2. A leaf thickness factor was introduced in the PROSPECT model to take into account the effects of changes in leaf structure on light absorption. The model inversion was improved after incorporating leaf thickness factors based on observed seasonal and canopy-height-related variation in leaf thickness. The improved model had the best performance, with an accuracy of R2 = 0.93 and RMSE = 3.09 µg/cm2 in retrieved leaf chlorophyll concentration in comparison with laboratory measurements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".