Indices-based approach for crop chlorophyll content retrieval from hyperspectral data
Bibliographic record
Abstract
This study aims at using forward model simulations and ground-measurements (biophysical and spectral) to estimate chlorophyll concentration from hyperspectral data and imagery. Its specific objectives were: (i) to evaluate various combinations of indices as estimators of chlorophyll content from simulated spectra (PROSPECT and SAILH); (ii) to establish chlorophyll predictive equations using spectral indices determined from field spectra and corresponding chlorophyll concentrations; (iii) to assess the effect of crop type (corn and wheat) on these relationships; and (iv) to validate and compare the indices' prediction capability using hyperspectral images and ground truth measurements. Hence, intensive field campaigns were organized during the growing seasons of 2000, 2004, and 2005 in order to collect ground spectra and corresponding leaf chlorophyll content values as well as crop growth measures. The relationships between leaf chlorophyll content and combined optical indices have shown similar trends for both PROSPECT- SAILH simulated data and ground measured datasets, indicating that both spectral measurements and radiative transfer models hold comparable potential for quantitative retrieval of crop foliar pigments. The dataset used showed that crop type had a clear influence on the establishment of predictive equations as well as on their validation. Moreover, corn and wheat data have led to contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Indices TCARI/TRDVI and TCI/TRDVI seem to be relatively consistent and more stable as estimators of crop chlorophyll content.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".