Spectral mixture analysis of potato crops under different irrigation regimes
Bibliographic record
Abstract
In an agricultural remote sensing image, the digital reflectance value for each pixel is a result of the combined spectral contributions of the various scene components, namely the plant, soil and shadow. Traditional remote sensing image processing methods such as vegetation indices do not separate these components explicitly, yet it is only the plants for which information is sought. The technique of spectral mixture analysis (SMA) is designed to derive the fraction of each component that is contributing to a pixel's reflectance. In this paper, SMA and vegetation indices are compared in a remote sensing experiment to monitor moisture stress in potatoes at a test site near Lethbridge, Alberta Canada. Differential irrigation treatments were implemented at the test site to induce various levels of moisture stress on the potato crop. In 1998, ground-based and airborne remote sensing data were collected in June, July and August. This paper addresses the ground-based August dataset using SMA to quantify the abundance of plant, soil, and shadow at sub-pixel scales towards improved extraction of plant biophysical and structural information. The impact of moisture stress on the crop in August was significant. The strength of the relationship to biophysical parameters was similar for both the SMA fractions and the vegetation indices and was somewhat lower than anticipated. A number of factors are discussed that may have affected the predictive capability of both remote sensing image processing methods.
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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 | 0.000 |
| 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 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".