Estimating actual evapotranspiration from remote sensing imagery using R: the package 'TriangleMethod'.
Bibliographic record
Abstract
Actual evaporation (Eta) is an essential variable to assess water availability, drought risk and food security, among others.Measurements of Eta are however limited to a small footprint, hampering a spatially explicit analysis and application and are very often not available at all.To overcome the problem of data scarcity, Eta can be assessed by various remote sensing approaches such as the Triangle Method (Jiang & Islam, 1999).Here, Eta is estimated by using the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST).In this study, the R-package 'TriangleMethod' was compiled to efficiently perform the calculations of NDVI and processing LST to finally derive Eta from the applied data set.The package contains all necessary calculation steps and allows easy processing of a large data base of remote sensing images.By default, the parameterization for the Landsat TM and ETM+ sensors are implemented, however, the algorithms can be easily extended to additional sensors.The auxiliary variables required to estimate Eta with this method, such as elevation, solar radiation and air temperature at the overpassing time, can be processed as gridded information to allow for a better representation of the study area.The package was successfully applied in various studies in Spain, Palestine, Costa Rica and Canada.
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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 | 0.001 | 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 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".