Using High Resolution LiDAR Data and a Flux Footprint Parameterization to Scale Evapotranspiration Estimates to Lower Pixel Resolutions
Bibliographic record
Abstract
Over the last several decades the hydrologically sensitive Boreal Plains ecoregion of Western Canada has experienced significant warming and drying. To better predict implications of land cover changes on evapotranspiration (ET) and future water resources in this region, high resolution light detection and ranging and energy balance data are used here to spatially parameterize the Penman-Monteith ET model. Within a 5 km × 5 km area of peatland ecosystems, riparian boundaries, and upland mixedwood forests, the influence of land cover heterogeneity on the accuracy of modeled ET is examined at pixel sizes of 1, 10, 25, 250, 500, and 1,000 m, representing resolutions common to popular satellite products (SPOT, Landsat, and MODIS). Modeled ET was compared with tower-based eddy covariance measurements using a weighted flux footprint model. Errors range from 10% to 36% of measured fluxes and results indicate that sensors with small pixel sizes (1 m) offer significantly better accuracy in large heterogeneous flux footprints, while a wider range of pixel sizes (<25 m) can be suitably applied to smaller homogeneous footprints. Mid (250 m) and coarse (>500 m) pixel sizes offered significantly less accuracy, although changes in pixel size within this range offered comparable results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".