Satellite-Based Evapotranspiration by Energy Balance for Western States Water Management
Bibliographic record
Abstract
METRICTM (Mapping Evapotranspiration at high Resolution and with Internalized Calibration) is an image-processing model comprised of multiple submodels for calculating evapotranspiration (ET) as a residual of the surface energy balance. METRIC is a variant of SEBAL, an energy balance process developed in the Netherlands by Bastiaanssen. METRIC was extended for application to mountainous terrain and to provide tighter integration with ground-based reference evapotranspiration. METRIC has been applied with Landsat images in southern Idaho, southern California, and New Mexico to predict monthly and seasonal ET for water rights accounting and for operation of ground water models. ET "maps" (i.e., images) via METRIC provide the means to quantify, in terms of both the amount and spatial distribution, the ET on a field by field basis. The ET images generated by METRIC show a progression of ET during the year as well as distribution in space. Comparisons between ET by METRIC, ET measured by lysimeter and ET predicted using traditional methods have been made on a daily and monthly basis for a variety of crop types and land-uses. The results suggest that METRIC or similar methods hold substantial promise as efficient, accurate, and inexpensive procedures to predict the actual evaporation fluxes from irrigated lands throughout a growing season.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".