Canopy-Level Photochemical Reflectance Index from Hyperspectral Remote Sensing and Leaf-Level Non-Photochemical Quenching as Early Indicators of Water Stress in Maize
Bibliographic record
Abstract
In this study, we evaluated the effectiveness of photochemical reflectance index (PRI) and non-photochemical quenching (NPQ) for assessing water stress in maize for the purpose of developing remote sensing techniques for monitoring water deficits in crops. Leaf-level chlorophyll fluorescence and canopy-level PRI were measured concurrently over a maize field with five different irrigation treatments, ranging from 20% to 90% of the field capacity (FC). Significant correlations were found between leaf-level NPQ (NPQleaf) and the ratio of chlorophyll to carotenoid content (Chl/Car) (R2 = 0.71, p < 0.01) and between NPQleaf and the actual photochemical efficiency of photosystem II (ΔF/Fm′) (R2 = 0.81, p < 0.005). At the early growing stage, both canopy-level PRI and NPQleaf are good indicators of water stress (R2 = 0.65 and p < 0.05; R2 = 0.63 and p < 0.05, respectively). For assessment of extreme water stress on plant growth, a relationship is also established between the quantum yield of photochemistry in PSII (ΦP) and the quantum yield of fluorescence (ΦF) as determined from photochemical quenching (PQ) and non-photochemical quenching (NPQleaf) of excitation energy at different water stress levels. These results would be helpful in monitoring soil water stress on crops at large scales using remote sensing techniques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".