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Record W2909054580 · doi:10.1111/gcb.14565

Solar‐induced chlorophyll fluorescence exhibits a universal relationship with gross primary productivity across a wide variety of biomes

2019· letter· en· W2909054580 on OpenAlexaff
Jingfeng Xiao, Xing Li, Binbin He, M. Altaf Arain, Jason Beringer, Ankur R. Desai, Carmen Emmel, David Y. Hollinger, Alisa Krasnova, Ivan Mammarella, Steffen M. Noe, Penélope Serrano-Ortíz, Camilo Rey‐Sánchez, Adrian V. Rocha, Andrej Varlagin

Bibliographic record

VenueGlobal Change Biology · 2019
Typeletter
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBiomeEddy covariancePrimary productionProductivityAtmospheric sciencesPhotosynthesisChlorophyll fluorescenceEnvironmental sciencePrimary productivityCarbon cycleFlux (metallurgy)MonsoonEcosystemPhysicsEcologyBotanyBiologyMeteorologyChemistry

Abstract

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In our recent study in Global Change Biology (Li et al., 2018), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally. In our recent study in Global Change Biology (Li et al., 2018), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally. In a letter to the Editor, Zhang, Zhang, Joiner, and Migliavacca (2018) argued that different viewing zenith angles (VZA) of the OCO-2 instrument could impact the SIF-GPP relationship revealed by our recent study. We need to clarify four over- or misinterpretations. In the letter, Zhang et al. (2018) first explained the measurement modes of OCO-2 (i.e., nadir, glint, and target) and called for attention to the effects of VZA (Frankenberg et al., 2014; He, Chen, Liu, Mo, & Joiner, 2017) on SIF magnitude. We recognized the effects of viewing geometries and also demonstrated that with all observations from 64 sites grouped together, there was no significant difference in the mean SIF between the nadir mode and the combined modes (glint/target) (Li et al., 2018). We combined SIF data from all observation modes to examine the universality of the SIF-GPP relationship among eight biomes (Li et al., 2018). In the Letter to the Editor, Zhang et al. (2018) argued that our nearly universal relationship may be complicated by the fact that the three observation modes have different viewing geometries. At an individual site, the observation modes could lead to slightly different slopes. Here we re-examined how the SIF-GPP relationship varies among biomes using the same data as used in our study (Li et al., 2018) but with the SIF observations acquired in the nadir mode only. Our new analysis shows that a nearly universal SIF-GPP relationship among the eight biomes still exists (Figure 1). Although there are view angle effects that require further investigation and may be site specific, our grouped global results appear to be robust regardless of the inclusion or exclusion of observations acquired in the target and/or glint mode. Our study showed that with all data from 64 sites grouped together, there is no significant difference in the slope of the SIF-GPP relationship between nadir and the combined modes (glint/target) (Li et al., 2018). In the letter, Zhang et al. (2018) lumped observations from a number of sites together and found a significant difference between target and nadir (or glint) but not between nadir and glint. The SIF-GPP relationship solely based on observations with high VZA indeed could be different from that based on observations with low VZA as revealed in our earlier study (Li, Xiao, & He, 2018). Observations by the target mode only accounted for a small fraction of the OCO-2 observations and therefore we combined glint and target observations and compared the slopes between nadir and the combined modes (Li et al., 2018). Finally, Zhang et al. (2018) suggested that the slopes could vary significantly among individual sites if a linear fit was forced through the origin following Sun et al. (2017). There is no evidence that the true SIF-GPP relationship at the ecosystem scale should pass through the origin although fitting a model without an intercept is mathematically feasible. Nevertheless, here we revisited the SIF-GPP relationship for each of the eight biomes (Li et al., 2018) using a linear fit without an intercept (Figure 2). Our new analysis clearly shows that there is a universal SIF-GPP relationship across the eight biomes. It is important to recognize that the SIF-GPP relationship at the biome level could be different from that at the site level, particularly given the representativeness of an individual site and a very limited number of observations available at each site. Viewing geometries of the OCO-2 instrument could influence the SIF-GPP relationship at individual sites but do not alter the near universality of the relationship between SIF and GPP across a wide variety of biomes found in our study (Li et al., 2018). This analysis contributes to projects funded by the National Aeronautics and Space Administration (NASA) (Grant No. NNX16AG61G and NNX14AJ18G).

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.024
GPT teacher head0.233
Teacher spread0.210 · 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

Citations50
Published2019
Admission routes1
Has abstractyes

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