Biophysical Parameters and Actual Evapotranspiration of Bean Culture by Means of Remote Sensing
Bibliographic record
Abstract
Agricultural irrigation is involved in an important chain that involves all sectors of the economy, either directly, by increasing food production, or indirectly, by withdrawing large amounts of fresh water. The relevance of this theme forces the search for alternatives to make water use as rational as possible. Evapotranspiration estimation methods based in remote sensing, such as the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model, become extremely relevant in these scenarios, since it is possible to estimate this parameter in large scales. Therefore, the aim of this research was to apply the SAFER model in the estimation of bean crop actual evapotranspiration using Landsat-8 satellite image data. One of the parameters used as input in the SAFER model is the NDVI (Normalized Difference Vegetation Index), which presented a coefficient of determination (r²) equal to 0.80 when compared to the crop coefficient. The actual evapotranspiration (ETa) estimated by the SAFER model were compared to the FAO 56 model estimates for later correlation between the models. This information is expected to assist the producer in a better management of water resources used in irrigation. The correlation between the two models presented a relevant coefficient of determination (r2 = 0.73), representing the potential of the SAFER model in relation to the FAO model 56.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".