Computation of Landsat Based Evapotranspiration Maps along the South Platte and North Platte Rivers
Bibliographic record
Abstract
It is important to quantify the consumptive water use by the vegetation when managing regional water resources in irrigated areas. Suitable models and algorithms applied to high resolution (30 m) satellite imagery provide a cost effective and time efficient method to obtain evapotranspiration estimations from bare soil and vegetation. The METRIC image processing model 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 algorithms are calibrated using an operator selected wet and dry pixel. The complete energy balance obtained from the satellite images is calibrated using ground based reference evapotranspiration estimations. The paper describes an application of the METRIC model on parts of the South Platte and North Platte rivers in Colorado and Nebraska for individual days in 1997, 2001 and 2002. Landsat 5 and Landsat 7 shortwave and longwave bands were used. Weather data from selected meteorological stations within the study area was screened and used to estimate reference evapotranspiration. A water balance model was used to estimate evaporation from the soil. During the image processing it was necessary to iterate the selection of the wet and dry pixels after reviewing evapotranspiration behavior for natural vegetation and wet fields at full cover. Uneven distribution of recent precipitation events and operator dependency needed to be addressed. The resulting evapotranspiration maps appear to be congruent with ET from previous studies and will be used by local water management entities.
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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 | 0.001 |
| 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".