Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate
Bibliographic record
Abstract
Actual crop evapotranspiration (ETa) is measured or estimated using different methods, and its accuracy is critical for water management under precision agriculture. The objective of this study was to compare maize ETa estimated by the two-step approach using a locally developed crop coefficient curve with satellite-retrieved evapotranspiration by six models incorporated in the OpenET to identify the best evapotranspiration estimation alternatives to the two-step approach for water management in northern New Mexico. Maize (Zea mays L.) was planted at the NMSU Agricultural Science Center at Farmington from 2017 to 2022 and uniformly managed across years. Water management in plants was based on maize’s actual evapotranspiration estimated as the product of the reference evapotranspiration and the local crop coefficient, which is described as a third-order polynomial function of the accumulated heat units by maize plants. For the same growing seasons, maize ETa was retrieved from satellite, and was estimated by six models listed within the OpenET from 2017 to 2022. The results show that maize daily ETa was consistently smaller when measured by SIMS and PT-JPL during maize initial and actively growing stages, while ETc(kc), SIMS and eeMETRIC showed similar maize daily ETa during maize full canopy development and mid-season, and which overcome the evapotranspiration estimated by DisALEXI, PT-JPL, geeSEBAL, and SSBop. ETc(kc) drastically dropped and became the lowest value among all ETa estimation models after the first fall snow or the first killing frost. Regarding the seasonal average, all six models included in OpenET showed smaller maize evapotranspiration. Maize seasonal evapotranspiration varied from 589.7 to 683.2 mm. eeMETRIC compares most similarly to the ETc(kc) model, followed by SIMS, with percent errors of 2.58 and 7.74% on a daily basis and 2.43 and 7.88% on a seasonal basis, with the lowest MBE and RMSE values, respectively, and could be used as an alternative for maize actual daily evapotranspiration for water management in northern New Mexico. The results of this study could be used by water managers and crop growers to improve water management in the Four Corners region, using eeMETRIC for crop water use to improve water management and conservation under sustainable agriculture.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".