Using MODIS derived <i>f</i>PAR with ground based flux tower measurements to derive the light use efficiency for two Canadian peatlands
Bibliographic record
Abstract
We used satellite remote sensing data; fraction of photosynthetically active radiation absorbed by vegetation (fPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) in combination with tower eddy covariance and meteorological measurements to characterise the Light Use Efficiency parameter (ε) variability and the maximum ε (εmax) for two contrasting Canadian peatlands. Eight-day MODIS fPAR data were acquired for the Mer Bleue (2000 to 2003) and Western Peatland (2004). Flux tower eddy covariance and meteorological measurements were integrated to the same eight-day time stamps as the MODIS fPAR data. A light use efficiency model: GPP = ε×APAR (where GPP is Gross Primary Productivity and APAR is absorbed photosynthetically active radiation) was used to calculate ε. The εmax value for each year (2000 to 2003) at the Mer Bleue bog ranged from 0.58 g C MJ−1 to 0.78 g C MJ−1 and was 0.91 g C MJ−1 in 2004, for the Western Peatland. The average growing season ε for the Mer Bleue bog for the four year period was 0.35 g C MJ−1 and for the Western Peatland in 2004 was 0.57 g C MJ−1. The average snow free period for the Mer Bleue bog over the four years was 0.27 g C MJ−1 and for the Western Peatland in 2004 was 0.39 g C MJ−1. Using the light use efficiency method we calculated the εmax and the annual variability in ε for two Canadian peatlands. We determined that temperature was a growth-limiting factor at both sites Vapour Pressure Deficit (VPD) however was not. MODIS fPAR is a useful tool for the characterization of ε at flux tower sites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".