Validation of a Hyperspectral Curve-Fitting Technique for Mapping Crop Water Status
Bibliographic record
Abstract
The estimation of plant water status is an essential component of precision crop management, and is directly related to plant physiological processes and ultimately crop yield. Hyperspectral models developed to estimate plant water content have met with limited success and have not been rigorously validated. A spectrum matching technique was applied to the hyperspectral data to directly calculate the canopy equivalent water thickness (EWT) using a look-up table approach. The objective of this study was to test the validity of this algorithm using crop water status information collected on the ground. Data were acquired over an experimental test site near Indian Head, Saskatchewan using the Probe-1 airborne hyperspectral sensor. Plant biomass samples were collected simultaneously from 96 plots spanning eight fields of various crop types (wheat, canola, and peas). The model was validated against EWT estimated from biomass samples as well as more conventional measures of crop water status. Results indicate that the liquid water retrieval technique can be used to estimate crop water status for broad-leafed crops such as peas and canola, but is not a reliable estimator of wheat this early stage of vegetative growth. This may be related to the low level of water in the crop and the contribution of soil to the reflectance signal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".