Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigments
Bibliographic record
Abstract
<p><strong>Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigments</strong></p><p>Shuren Chou<sup>1#</sup>, Bin Chen<sup>2</sup>*<sup>#</sup>, Jing Chen<sup>3</sup><sup>,4</sup>*, Miaomiao Wang<sup>2,5</sup>, Shaoqiang Wang<sup>2,5,</sup><sup>6</sup>, Holly Croft<sup>7</sup>, Qin Shi<sup>8</sup></p><p><sup>1</sup>Space Security Center, Space Engineering University, Beijing 101416, China;</p><p><sup>2</sup>Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;</p><p><sup>3</sup>School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China</p><p><sup>4</sup>Department of Geography and Planning, University of Toronto, Toronto, Canada</p><p><sup>5</sup>College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China</p><p><sup>6</sup>College of Geography and Information Engineering, China University of Geosciences, Wuhan, China</p><p><sup>7</sup>Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, U.K.</p><p><sup>8</sup>Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China;</p><p><strong>Abstract: </strong>Leaf chlorophyll content has recently been found to be a better proxy than leaf nitrogen content for leaf photosynthetic capacity in a mixed deciduous broadleaf forest. A key concept underlying the relationship between leaf photosynthetic capacity and leaf chlorophyll content was the coordinate regulation of photosynthetic components (i.e. light harvesting, photochemical, and biochemical components). In order to test this hypothesis, we measured seasonal variations in leaf nitrogen content (N<sub>leaf</sub>), leaf photosynthetic pigments (i.e. chlorophyll (Chl<sub>Leaf</sub>), carotenoids (Car<sub>Leaf</sub>) and xanthophyll (Xan<sub>Leaf</sub>)) and leaf photosynthetic capacity (i.e. the maximum rate at which ribulose bisphosphate (RuBP) is carboxylated (V<sub>cmax25</sub>) and regenerated (J<sub>max25</sub>) at 25 <sup>o</sup>C) at a paddy rice site during the growing season in 2016. We investigated the effectiveness of (N<sub>leaf</sub>), leaf photosynthetic pigments, leaf-level photochemical reflectance index at sunny noon (PRI<sub>Leaf_noon</sub>) and their possible combinations for estimating leaf photosynthetic capacities (i.e. V<sub>cmax25</sub> and J<sub>max25</sub>) at a paddy rice site. Chl<sub>Leaf</sub> was highly correlated to V<sub>cmax25</sub> and J<sub>max25</sub> (R<sup>2 </sup>= 0.89 and 0.87, respectively), which were better than N<sub>leaf</sub> (R<sup>2 </sup>= 0.80 and 0.85, respectively). The products of PRI<sub>Leaf_noon</sub> with leaf pigments (i.e. Chl<sub>Leaf</sub>, Car<sub>Leaf</sub> and Xan<sub>Leaf</sub>) were also found to be highly correlated with V<sub>cmax25</sub> (R<sup>2 </sup>= 0.95 to 0.96). Also, the product of leaf chlorophyll a and Car<sub>Leaf</sub> was a good proxy for V<sub>cmax25</sub> (R<sup>2</sup> = 0.93). In sum, this study supported the previously findings that leaf chlorophyll content was better correlated with V<sub>cmax25</sub> than leaf nitrogen content. Also, combining PRI<sub>Leaf_noon</sub> with leaf pigments (i.e. Chl<sub>Leaf</sub>, Car<sub>Leaf</sub> and Xan<sub>Leaf</sub>) offered an additional way to estimate leaf photosynthetic capacity (i.e. V<sub>cmax25</sub>). These findings supported the hypothesis of coordinate regulation of photosynthetic components and they would be helpful to estimation of leaf photosynthetic capacity using remote sensing data.</p><p><strong>Keywords: </strong>seasonal variations; leaf nitrogen content; photosynthetic pigments; leaf maximum carboxylation rate</p><p> </p>
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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) | 0.000 | 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) | 0.001 | 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".