Temperature-Dependent Relationship Between Solar-Induced Chlorophyll Fluorescence and Photosynthesis in Evergreen Needleleaf Forests
Bibliographic record
Abstract
Solar-induced chlorophyll fluorescence (SIF) has a high correlation with gross primary production (GPP) at various spatiotemporal scales. However, this relationship varies with the changing environmental conditions, requiring further investigation and interpretation at various scales. In this study, we investigated (i) the temperature sensitivities of the SIF/GPP ratio, (ii) their correlation in 33 evergreen needleleaf forest sites using TROPOMI SIF and flux tower datasets from 2018 to 2021, and (iii) the temperature responses of the ratio of quantum yield of fluorescence to quantum efficiency of photosystem II (ΦF/ΦPSII) using leaf measurements from 3 datasets. At the canopy scale, SIF effectively tracked the GPP during the year, and the SIF/GPP ratio was relatively stable from 8 to 18 °C, while it increased at cold (28°C). Furthermore, the SIF–GPP correlation was strongest at moderate temperatures (~25 °C). At the leaf scale, ΦF/ΦPSII also increased at low temperatures, which demonstrated the direct impact of temperature on the energy partitioning in the light reaction. This study indicated the need to consider the changing temperature and energy partitioning during the light reaction when using fluorescence to track photosynthesis, especially under extreme environments.
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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".