Radiative transfer and viewing geometry considerations for the SIF/GPP relationship
Bibliographic record
Abstract
Solar-Induced chlorophyll Fluorescence (SIF) provides a powerful proxy for determining forest gross primary production (GPP), particularly in evergreen ecosystems where traditional measures of greenness fail. The dynamics of the SIF/GPP relationship, however, are poorly understood under varying viewing directions and light conditions. This is, in large part, due to challenges in measuring SIF at the spatiotemporal scale that is necessary to understand these effects. Therefore, the aim of this work is to utilize high-temporal and spatial resolution SIF measurements to better constrain the response of SIF to ambient canopy illumination and viewing geometry. We use a PhotoSpec instrument and eddy covariance measurements to explore the SIF/GPP relationship under various viewing directions and light conditions during the 2019 and 2020 growing seasons at the Old Black Spruce site in Saskatchewan, Canada. PhotoSpec is a tower-based 2-D scanning spectrometer system capable of taking Fraunhofer-line based SIF retrievals in the red and far-red wavelength ranges with a 0.7 degree field of view at a ~30 second time resolution. Measured SIF and GPP are combined with SCOPE modelling results to provide a mechanistic understanding of the physical and ecophysiological drivers for the SIF/GPP relationship in the Boreal Forest. Our results show that viewing direction and solar zenith/azimuth angles are important for the SIF signal under direct light conditions, but not under diffuse. Furthermore, the SIF/GPP relationship changes under direct and diffuse light conditions at a 30 minute, daily, and monthly resolution. Our ability to use SIF as a proxy for GPP depends on a quantitative understanding of radiative transfer within the canopy and how scanning geometry impacts SIF measurements. These results provide an important insight into these relationships in the Boreal forest, a region where GPP has been traditionally difficult to track using remote sensing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".