Automated Selection of Anchor Pixels for Landsat Based Evapotranspiration Estimation
Bibliographic record
Abstract
When managing local and regional water resources, the estimation of evapotranspiration is important and has generally been one of the components of the hydrological cycle that has the greatest uncertainty. With the development of suitable models and algorithms applied to high resolution (30 m) satellite imagery, evapotranspiration may be estimated with greater accuracy, and in a cost effective and time efficient manner. The METRIC image processing procedure calculates net radiation, soil heat flux and sensible heat flux through a number of steps before estimating evapotranspiration as the residual from the energy balance. Sensible heat flux is calibrated using the so-called "cold" and "hot" anchor pixels. These pixels are selected by the user, which may introduce some operator dependency or human errors on the estimation of sensible heat flux and subsequently error in the final map of evapotranspiration. A procedure for automated selection of the anchor pixels is presented. The automated pixel selection procedure will reduce the user dependency of the estimations of sensible heat flux. Additionally, it may allow more novice users to obtain good results when applying METRIC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".