Estimating and validating basin-scale actual evapotranspiration using MODIS images and hydrologic models
Bibliographic record
Abstract
An algorithm for estimating daily spatial actual evapotranspiration (ET) from remotely sensed MODIS data is presented. It is based on the surface energy balance scheme and the modified Priestley–Taylor equation, and has been applied to the MODIS data acquired during growing seasons over the Laohahe River basin, northeastern China. Spatial distributed mapping of daily ET for 22 clear sky days in the year of 2000 from MODIS images over the study area were obtained. In order to validate ET values estimated from MODIS data, regional daily ET values were calculated using the lumped modified Xinanjiang hydrologic model and distributed SWAT model based on the water balance scheme, respectively. The relationship between actual daily ET estimated from MODIS images and basin-scale ET calculated from the hydrologic model were in good agreement with acceptable correlation coefficient. The results suggested that the algorithm is applicable and operational for estimating and mapping basin-scale distributed daily actual ET over the study area. In order to use the algorithm proposed by this paper for water resource management and agricultural decision making, the algorithm should be validated using more data and be tested under different environment and different land use scenario conditions in future work.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".