Comparison of in situ LAI retrieval of two instruments of four mature agricultural crops
Bibliographic record
Abstract
In contribution to the land-surface parameter-validation efforts of the Working Group on Calibration and Validation (WGCV) under the Committee on Earth Observation Satellites (CEOS), the Canada Centre for Remote Sensing - Natural Resources Canada participated in a multinational field exercise led by the CEOS Land Product Validation (LPV) subgroup. The purpose of this exercise was to compare existing and commonly used field instruments and methodologies for determining effective leaf-area index (Le) for the purpose of integrating field Le information to satellite-based Earth Observation (EO) imagery to provide geospatialmaps of Le over a large area. An investigation of integrating these field measurements to satellite imagery will be the focus of a separate report. This study included (but was not limited to) comparisons between using hemispheric upward and downward photography and using the LICOR LAI-2000 and related methodology as tools for determining Le during an agricultural field campaign. Where possible, measurements were obtained subject to both direct (near local solar noon) and diffuse (just after sunrise or just before sunset) illumination. Crop types evaluated included corn, sorghum, soybean, and alfalfa, all mature. All sites were located at the Manfredi Instituto Nacional de Tecnología Agropecuaria (INTA) station in the Cordoba province, Argentina. The field exercise was carried out in early March, 2005.
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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 | 0.001 | 0.001 |
| 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".