Improved methods for estimating monthly and growing season ET using METRIC applied to moderate resolution satellite imagery
Bibliographic record
Abstract
Abstract Satellite‐based algorithms based on surface energy balance are now routinely applied to produce evapotranspiration (ET) products on an operational basis for use in water resources management. Landsat satellite imagery, or imagery with similar spatial resolution, is commonly used to produce estimates of ET at field scale because of the presence of an onboard thermal imager and the high spatial resolution of the imagery. The downside of using high‐resolution imagery is less frequent image acquisition. As a result, monthly and ultimately seasonal ET estimates may be based on only one or two satellite image snapshots per month. A potential shortfall in basing integrated ET averages on periodic snapshots from satellites is that local or regional precipitation events antecedent to the satellite images may unduly dominate the ET image, or conversely, effects may be absent from the image, and therefore the image‐based product may not represent evaporation from rainfall averaged over the monthly period. Methods for accounting for precipitation events when interpolating from daily to seasonal or longer periods are presented, including a recently developed soil‐water balance procedure that adjusts the ET derived from the satellite overpass date for background evaporation from soil caused by rainfall over monthly or longer integration periods. The result of the adjustment is an ET image and consequently final ET map that better represents the average evaporative conditions over the period. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".