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Record W4415256928 · doi:10.1109/tgrs.2025.3620306

Temperature-Dependent Relationship Between Solar-Induced Chlorophyll Fluorescence and Photosynthesis in Evergreen Needleleaf Forests

2025· article· W4415256928 on OpenAlexaff
Ruonan Chen, Liangyun Liu, Xinjie Liu, Christopher Y. S. Wong, Ingo Ensminger

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2025
Typearticle
Language
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersNational Natural Science Foundation of China
KeywordsChlorophyll fluorescencePhotosynthesisPhotosystem IIEvergreenCanopyFluorescenceQuantum yield

Abstract

fetched live from OpenAlex

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 (<5°C) and hot extremes (>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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.238
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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