Estimation of Crop Cover and Chlorophyll from Hyperspectral Remote Sensing
Bibliographic record
Abstract
Over the last two decades, there has been extensive development in hyperspectral remote sensing. Interest is rapidly growing in the application of hyperspectral data to precision farming. This paper investigates the potential of hyperspectral remote sensing data for providing crop information for use in precision farming. Ground measurements and airborne hyperspectral Probe-1 data were simultaneously acquired in July 1999 near Clinton, Ontario, Canada. Specifically, percent ground cover and chlorophyll estimations derived from the Probe-1 data are being validated. Constrained linear unmixing was conducted on the airborne hyperspectral surface reflectance and at-sensor radiance data to determine crop endmember fractions. Chlorophyll maps were generated from Probe-1 reflectance data using three different methods. Correlations between ground data and Probe-1 derived image products were significant and produced encouraging results. Although based on a limited range of chlorophyll values available in this study, Probe-1 derived chlorophyll index values were sensitive to differences in SPAD-502 measurements taken in the field. Crop ground cover was significantly correlated with spectral fractions derived from the radiance or the reflectance data.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".