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Record W4200385222 · doi:10.1002/essoar.10509278.1

Radiative transfer and viewing geometry considerations for remote sensing as a proxy for carbon uptake in boreal ecosystems

2021· preprint· en· W4200385222 on OpenAlexaff
Zoe Pierrat, Alexander Norton, Lea Baskin Monk, Nicholas C. Parazoo, Andrew J. Maguire, Katja Großmann, Troy S. Magney, Alan Barr, Bruce Johnson, J. Stutz

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsJet propulsionElectronic mailWorld Wide WebPhysicsComputer science

Abstract

fetched live from OpenAlex

The boreal forest plays an important role in the global carbon cycle but has remained a significant source of uncertainty. Remote sensing can help us better understand the boreal forest’s role in the global carbon cycle. A faint light signal emitted by plant’s photosynthetic machinery, known as solar-induced chlorophyll fluorescence (SIF), is a promising remotely sensed proxy for carbon uptake, also known as gross primary productivity (GPP), due to its connection to photosynthesis and its strong relationship with GPP when observed by satellite. However, SIF and GPP are fundamentally different quantities that describe distinct, but related, physiological processes. The relationship between SIF and GPP is therefore complicated by both physical and ecophysiological controls. In particular, the dynamics of the SIF/GPP relationship are poorly understood under varying viewing directions and light conditions. This is further complicated in evergreen systems where canopy clumping and the presence of needles create a unique radiative environment. We use a combination of tower-based SIF and GPP measurements from a boreal forest field site compared with a coupled biochemical-radiative transfer model to understand illumination effects on the SIF/GPP relationship. We find that GPP is amplified under cloudy sky conditions in both measurements and model results. SIF on the other hand, shows no significant difference between sunny or cloudy sky conditions in modeled results, but does show a difference in measurements. We suggest that these differences may be due to viewing geometry effects that are important for SIF under sunny sky conditions or the presence of clumping. Accounting for the differences in the SIF/GPP relationship therefore is critical for the utility of SIF as a proxy for GPP. In summation, our results provide insight into how we can use remote sensing as a tool to understand photosynthesis in the boreal forest.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.237
Teacher spread0.220 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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