Research of Light Use Efficiency for Paddy Rice Using Multi-Angle Hyperspectral Observations
Bibliographic record
Abstract
Light use efficiency (LUE) is a critical variable for modeling gross primary production (GPP) of vegetation. The photochemical reflectance index (PRI) is based on reflectance at 531 and 570 nm, which has great potential in predicting the light use efficiency (LUE) and makes LUE related to plant optical characteristics. Despite the great achievement in understanding the correlation of PRI and LUE on forests, the application of PRI in estimating LUE on crops has almost been ignored. The present study reported the ability of multi-angle PRI to track eddy covariance (EC)-based LUE in a rice field in East China. We found that PRI can estimate LUE in sunny days (R2 = 0.4; p < 0.05). The correlation of PRI and LUE was greater than that at OZA of 0° (R2 = 0.4; p < 0.05) after distinguishing different OZAs and scatter directions and was the greatest in the backscatter direction with an OZA of 60° (R2 = 0.7; p < 0.01). This correlation was also corrected by the bidirectional reflectance distribution function (BRDF), where R2 = 0.72 after correction. We noted that the relationship between PRI and LUE on cloudy days was poor, where R2 = 0.26. Photosynthetically active radiation (PAR) had an influence on the LUE and PRI, while vapor pressure deficit (VPD) and air temperature (Ta) had negative influences on the correlation of PRI and LUE. Our research suggests that PRI can efficiently track the LUE of the rice growth period when considering environmental factors and rice canopy structures.
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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 | 0.001 |
| 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".