Satellite‐based ET estimation in agriculture using SEBAL and METRIC
Bibliographic record
Abstract
Abstract Surface Energy Balance Algorithms for Land (SEBAL) and Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) are satellite‐based image‐processing models that calculate evapotranspiration (ET) as a residual of a surface energy balance. Both models are calibrated using inverse modelling at extreme conditions approach to develop image‐specific estimations of the sensible heat flux (H) component of the surface energy balance and to effectively remove systematic biases in net radiation, soil heat flux, radiometric temperature and aerodynamic estimates. SEBAL and METRIC express the near‐surface temperature gradient as an indexed function of radiometric surface temperature, eliminating the need for absolutely accurate surface temperature and the need for air temperature measurements. Slope and aspect functions and temperature lapsing are used in METRIC applications in mountainous terrains. SEBAL and METRIC algorithms are designed for relatively routine application by trained professionals familiar with energy balance, aerodynamics and basic radiation physics. The primary inputs for the models are short‐wave and long‐wave (thermal) images from satellite (e.g. Landsat and MODIS), a digital elevation model and ground‐based weather data measured within or near the area of interest. ET ‘maps’ (i.e. images) developed using Landsat images provide means to quantify ET on a field basis in terms of both rate and spatial distribution. METRIC takes advantage of calibration using weather‐based reference ET so that both calibration and extrapolation of instantaneous ET to 24‐h and longer periods compensate for regional advection effects where ET can exceed daily net radiation. SEBAL and METRIC have advantages over conventional methods of estimating ET using crop coefficient curves or vegetation indices in that specific crop or vegetation type does not need to be known and the energy balance can detect reduced ET caused by water shortage, salinity or frost as well as evaporation from bare soil. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".