Comparison of physically based and empirical models to estimate corn (<i>Zea mays</i>L) LAI from multispectral data in eastern Canada
Bibliographic record
Abstract
Leaf area index (LAI) is a key input for many ecological models such as crop and carbon and nitrogen models. The LAI patterns measured in situ are time consuming and expensive and could be substituted by remote sensing technology. The objective of this study was to evaluate the possibility to map LAI of corn fields in eastern Canada using airborne multispectral imagery. Four models were locally optimized, tested, and compared. The first two methodologies relied on simple empirical relationships between LAI and the normalized difference vegetation index. The third methodology was a physically based model assuming that spectral vegetation indices are proportional to the fraction of photosynthetically active radiation absorbed by the green vegetation canopy. The last methodology relied on neural network (NN) models using different channels of the multispectral images as input. No studies combining airborne remote sensed data and NN for corn LAI simulation at regional or field scales are known by the authors. Ground measurements of LAI in five experimental sites at two dates in 2011 and 2012 were used to optimize and evaluate the models. Even though performances of the standard methods were improved by local optimisation, an NN model built with the near-infrared and red channels provided more accurate LAI simulations. All models performed better at LAI values below 3, but scattering at high LAI values was less pronounced with the NN model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".