Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it