Radiative transfer and viewing geometry considerations for remote sensing as a proxy for carbon uptake in boreal ecosystems
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".