Hyperspectral chlorophyll indices sensitivity analysis to soil backgrounds in agrirultural aplications using field, Probe-1 and Hyperion data
Bibliographic record
Abstract
This paper focuses on the evaluation and comparison of the sensitivity of several chlorophyll indices to bare soils optical property variations. To achieve our goal, field spectroradiometric measurements were used as well as hyperspectral data acquired with the Probe-1 airborne and Hyperion EO-1 satellite sensors. The field-based reflectance measurements were acquired above 90 bare soil plots with various optical properties and selected from different agricultural lands. Probe-1 and Hyperion data were spectrally and radiometrically calibrated as well as atmospherically corrected. After these pre-processing steps, sixty spectra of different bare soils with various optical properties were extracted from each dataset for use in the analysis. The obtained results show an excellent agreement between the accuracies estimated from field, airborne and satellite data. Independently from the data source and from the bare soil background, CARI, MCARI and TCARI indices are basically not sensitive to changes in soil optical properties with an RMSE less than 1% and will permit a better estimation of chlorophyll content in sparse crop cover environment.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".