Using MODIS derived fPAR 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; fractionof photosynthetically active radiation absorbed by vegetation(fPAR) from the Moderate Resolution Imaging Spectrora-diometer (MODIS) in combination with tower eddy covari-ance and meteorological measurements to characterise theLight Use Efficiency parameter (ε)variability and the maxi-mumε(εmax)for two contrasting Canadian peatlands. Eight-day MODISfPAR data were acquired for the Mer Bleue(2000 to 2003) and Western Peatland (2004). Flux towereddy covariance and meteorological measurements were in-tegrated to the same eight-day time stamps as the MODISfPAR data. A light use efficiency model: GPP =ε×APAR(where GPP is Gross Primary Productivity and APAR is ab-sorbed photosynthetically active radiation) was used to cal-culateε. Theεmaxvalue for each year (2000 to 2003) at theMer Bleue bog ranged from 0.58 g C MJ−1to 0.78 g C MJ−1and was 0.91 g C MJ−1in 2004, for the Western Peatland.The average growing seasonεfor the Mer Bleue bog forthe four year period was 0.35 g C MJ−1and for the West-ern Peatland in 2004 was 0.57 g C MJ−1. The average snowfree period for the Mer Bleue bog over the four years was 0.27 g C MJ−1and for the Western Peatland in 2004 was0.39 g C MJ−1. Using the light use efficiency method wecalculated theεmaxand the annual variability inεfor twoCanadian peatlands. We determined that temperature was agrowth-limiting factor at both sites Vapour Pressure Deficit(VPD) however was not. MODISfPAR is a useful tool forthe characterization ofεat flux tower sites.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".