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Record W2548357517 · doi:10.1073/pnas.1606162113

A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers

2016· article· en· W2548357517 on OpenAlexafffund
John A. Gamon, K. F. Huemmrich, Christopher Y. S. Wong, Ingo Ensminger, S. R. Garrity, David Y. Hollinger, Asko Noormets, Josep Peñuelas

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersPacific Northwest Research StationDepartment of the Interior Southeast Climate Science CenterOntario Ministry of Research and InnovationU.S. Department of AgricultureNatural Sciences and Engineering Research Council of CanadaU.S. Forest ServiceU.S. Department of EnergyNorthern Research StationAlberta InnovatesNational Institute of Food and AgricultureUniversity of WashingtonOffice of ScienceNational Aeronautics and Space Administration
KeywordsEvergreenPhenologyEvergreen forestVegetation IndexIndex (typography)Remote sensingPhotosynthesisGeographyPhysical geographyEnvironmental scienceEcologyBiologyLeaf area indexNormalized Difference Vegetation IndexBotanyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

In evergreen conifers, where the foliage amount changes little with season, accurate detection of the underlying "photosynthetic phenology" from satellite remote sensing has been difficult, presenting challenges for global models of ecosystem carbon uptake. Here, we report a close correspondence between seasonally changing foliar pigment levels, expressed as chlorophyll/carotenoid ratios, and evergreen photosynthetic activity, leading to a "chlorophyll/carotenoid index" (CCI) that tracks evergreen photosynthesis at multiple spatial scales. When calculated from NASA's Moderate Resolution Imaging Spectroradiometer satellite sensor, the CCI closely follows the seasonal patterns of daily gross primary productivity of evergreen conifer stands measured by eddy covariance. This discovery provides a way of monitoring evergreen photosynthetic activity from optical remote sensing, and indicates an important regulatory role for carotenoid pigments in evergreen photosynthesis. Improved methods of monitoring photosynthesis from space can improve our understanding of the global carbon budget in a warming world of changing vegetation phenology.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations378
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicRemote Sensing in AgricultureFrench-language works237,207