Retrieving chlorophyll content in conifer needles from hyperspectral measurements
Bibliographic record
Abstract
Spectrally continuous hyperspectral data can be used to detect subtle features in the leaf optical spectra, which correlate especially well with major leaf pigments such as the leaf chlorophyll content. Extensive field and laboratory measurements were carried out at 10 sites in black spruce (Picea mariana (Mill.)) forests near Sudbury, Canada, to collect leaf optical spectra, leaf pigment contents, and leaf biophysical parameters. It was found that black spruce needles sampled from different sites, age classes, and branch orientations demonstrated variability in both optical properties and chlorophyll contents. The variability in needle optical spectra showed a good correlation (R2 = 0.63) between the average visible absorptance and needle chlorophyll content. The leaf optical model PROSPECT was modified to incorporate the edge effects of needles on light transfer through them. Two leaf biophysical parameters, namely needle width and thickness, were introduced into the model to take into account the effects of leaf morphology on chlorophyll content retrieval. With the modifications to PROSPECT, the model can capture the variability of needle optical properties and chlorophyll content from the measurements. The retrieval of needle chlorophyll contents was improved with an accuracy of R2 = 0.59 and root mean squared error of RMSE = 6.32 µg/cm2 compared with the original PROSPECT model with an accuracy of R2 = 0.31 and RMSE = 9.51 µg/cm2.
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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 | 0.000 |
| 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 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".