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 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 | 0.000 | 0.000 |
| 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".