Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigments
Bibliographic record
Abstract
Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigments Shuren Chou1#, Bin Chen2*#, Jing Chen3,4*, Miaomiao Wang2,5, Shaoqiang Wang2,5,6, Holly Croft7, Qin Shi8 1Space Security Center, Space Engineering University, Beijing 101416, China; 2Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 3School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China 4Department of Geography and Planning, University of Toronto, Toronto, Canada 5College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China 6College of Geography and Information Engineering, China University of Geosciences, Wuhan, China 7Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, U.K. 8Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; Abstract: 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 (Nleaf), leaf photosynthetic pigments (i.e. chlorophyll (ChlLeaf), carotenoids (CarLeaf) and xanthophyll (XanLeaf)) and leaf photosynthetic capacity (i.e. the maximum rate at which ribulose bisphosphate (RuBP) is carboxylated (Vcmax25) and regenerated (Jmax25) at 25 oC) at a paddy rice site during the growing season in 2016. We investigated the effectiveness of (Nleaf), leaf photosynthetic pigments, leaf-level photochemical reflectance index at sunny noon (PRILeaf_noon) and their possible combinations for estimating leaf photosynthetic capacities (i.e. Vcmax25 and Jmax25) at a paddy rice site. ChlLeaf was highly correlated to Vcmax25 and Jmax25 (R2 = 0.89 and 0.87, respectively), which were better than Nleaf (R2 = 0.80 and 0.85, respectively). The products of PRILeaf_noon with leaf pigments (i.e. ChlLeaf, CarLeaf and XanLeaf) were also found to be highly correlated with Vcmax25 (R2 = 0.95 to 0.96). Also, the product of leaf chlorophyll a and CarLeaf was a good proxy for Vcmax25 (R2 = 0.93). In sum, this study supported the previously findings that leaf chlorophyll content was better correlated with Vcmax25 than leaf nitrogen content. Also, combining PRILeaf_noon with leaf pigments (i.e. ChlLeaf, CarLeaf and XanLeaf) offered an additional way to estimate leaf photosynthetic capacity (i.e. Vcmax25). 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. Keywords: seasonal variations; leaf nitrogen content; photosynthetic pigments; leaf maximum carboxylation rate
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".