Ground‐Based Multiangle Solar‐Induced Chlorophyll Fluorescence Observation and Angular Normalization for Assessing Crop Productivity
Bibliographic record
Abstract
Abstract Solar‐induced chlorophyll fluorescence (SIF) provides remotely sensible signals for monitoring gross primary production (GPP). Ground‐based multiangle observations of both red and far‐red SIF above wheat and maize canopies were conducted to examine angular effects on SIF. With these new measurements, we were able for the first time to refine and apply an algorithm developed for angular normalization of both red and far‐red SIF measurements. The angular normalization improved the correlation of SIF with GPP derived from eddy covariance measurements at the instantaneous scale (1 min), with increases of the diurnal coefficients of determination (of sunlit SIF with GPP) up to 0.21 for far‐red SIF and 0.3 for red SIF based on analysis on 6 sunny days. The improvement was slightly smaller for far‐red SIF than for red SIF, attributing to that the observed angular variation of SIF in the red band was greater than that in the far‐red band due to weaker multiple scattering in the red band in the canopy. In addition, at the hourly time scale, far‐red sunlit SIF shows its advantage to track GPP for heterogonous canopies, while angular normalization of red SIF is effective for homogeneous canopies. In comparison with another angular normalization method based on the escape ratio using datasets over both wheat and maize canopies, the two kinds of method show similar ability to improve the correlation between SIF and GPP, while the results suggest a limitation of SIF in estimating GPP for dense canopies where the fraction of shaded leaves are large.
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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 | 0.002 |
| 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".