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 ( R 2 = 0.4; p < 0.05). The correlation of PRI and LUE was greater than that at OZA of 0° ( R 2 = 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° ( R 2 = 0.7; p < 0.01). This correlation was also corrected by the bidirectional reflectance distribution function (BRDF), where R 2 = 0.72 after correction. We noted that the relationship between PRI and LUE on cloudy days was poor, where R 2 = 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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".