Evaluation of Different Methods for Estimating the Fraction of Sunlit Leaves and Its Contribution for Photochemical Reflectance Index Utilization in a Coniferous Forest
Bibliographic record
Abstract
Proper determinations of light use efficiency (LUE) and absorbed photosynthetically active radiation (APAR) are essential for LUE models to simulate gross primary productivity (GPP). This study intended to apply the photochemical reflectance index (PRI) to track LUE or APAR variations in a subtropical coniferous forest using tower-based PRI and GPP measurements. To improve the ability of using PRI to track LUE or APAR, a two-leaf approach differentiating sunlit and shaded leaves was used to process the remote sensing and flux data. However, penumbra region, the ‘grey region’ between sunlit and shaded leaves, increases the difficulty for quantifying the fractions of sunlit and shaded leaves. Firstly, three methods with different ways on treating the penumbra region were investigated for estimating the fraction of sunlit leaves (PT). After evaluating the correlations between observed PRI (PRIobs) and inversely retrieved PRI (PRIinv) from estimated PT using the three methods, we found that treating a substantial portion of penumbra region as sunlit leaves was reasonable and using the ratio of canopy reflectance to leaf reflectance as PT was accurate and efficient. Based on this, we used the two-leaf approach to estimate the canopy-level PRI, aiming to evaluate the ability of using PRI as a proxy for LUE or APAR. Results showed that PRI was able to capture half-hourly and daily changes in LUE and APAR, and the two-leaf approach could enhance the correlations between PRI and both LUE and APAR at both half-hourly and daily time steps. Strong diurnal correlations (averaged R = 0.82 from 173 days) between two-leaf PRI and APAR were found on more than 80% days and the relationship between them over the whole study period was also very significant (R2 > 0.5, p <0.0001) regardless of different climate conditions, suggesting that the two-leaf PRI was probably a better proxy for APAR than for LUE at short-term scale as PRI mainly represented the absorbed energy allocated to photoprotection at short time scale and was a direct outcome driven by APAR. However, the scattered relationships of PRI with LUE and APAR indicated there were still many limitations in usage of PRI to accurately estimate physiological parameters affected by changing weather conditions, pigment pool size, etc., which needed further exploration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".