Use of hyperspectral remote sensing to estimate the gross photosynthesis of agricultural fields
Bibliographic record
Abstract
Optimization of crop growth and yield is achieved through the use of effective management practices. However, transient weather conditions will modify crop growth and yield. To assess crop development it is therefore essential to understand the current crop ecophysiological status. Such information can be monitored continuously using micrometeorological instrumented towers over agricultural surfaces. The spatial coverage of this approach is limited to the upwind area contributing to the flux. Remote sensing becomes key in deriving carbon exchanges and crop vigour over larger spatial areas. Derived from ground-based hyperspectral reflectance measurements from five growing seasons, a relationship between the eddy covariance estimates of gross photosynthesis and the product of the standardized photochemical reflectance index and the integrated modified triangular index was expanded to the field scale through the use of Compact Airborne Spectrographic Imager (CASI) data for corn and wheat over two consecutive seasons in the same field. Imagery-derived maps of gross photosynthesis successfully identified areas of potential stress that were known to be correlated with lower yield. Results were further verified using an independent flux dataset. This approach, modified from previous attempts in natural ecosystems, offers additional promise for managed systems.
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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 | 0.001 |
| 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".