Temporal hyperspectral monitoring of chlorophyll, LAI, and water content of barley during a growing season
Bibliographic record
Abstract
We describe a study using the ASIA-Eagle hyperspectral sensor to measure the spectral response of spring barley over an entire climate-controlled growing season and correlate those results with the results of 25 biophysical and biochemical parameters. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VIs) that have been recorded in the literature. Furthermore, all combinations of the 252 spectral bands were tested to calculate a range of difference vegetation indices (VIs(xy)) and reflectance value indices (R(x)) at the central wavelength (x nm) of each band (R(x)). For all three index types we examined the relationship with the vegetation variables measured on the ground by using a cross-validation procedure. The relationship between the estimated and the measured canopy chlorophyll content (CCC) was (CV, covariance of variation). An was obtained when modelling leaf area index (LAI), chlorophyll content (Chl-SPAD) as well as leaf gravimetric water content (GWC). The prediction of Chl-SPAD with reflectance VIs leads to greater prediction accuracy compared with published VIs as well as difference VIs. Based on the literature, we used the DI1 vegetation index for extracting vegetation variables such as LAI and GWC. However, because of overlap effects, an explicit assignment of the spectral response to a particular vegetation parameter was not possible. The ascertained subtraction VIy = (565–779) nm also shows very good prediction accuracy compared with LAI. The investigated overlap effects for the published VIs did not result in an explicit responsiveness of the spectral response to the measured vegetation parameters. No index shows an explicit spectral signal for a single vegetation parameter. The optimisation tests show that when compared with univariate techniques, multivariate regressions improved the prediction accuracy of LAI, Chl, and CCC.
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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".