Remote Sensing of Surface Moisture in Near Real Time
Bibliographic record
Abstract
Surface moisture estimates in real time are difficult to accomplish for large areas. This paper investigates the possibility of utilizing satellites' measured radiation to estimate available moisture in real time. Thermal infrared, near infrared, and visible radiation energies are used. These energies are combined to develop a surface moisture index called the Surface Moisture Evapotranspiration (SMET) Index. This index detects available soil and vegetal matter moisture. The parameters of this index are surface temperature (T) and the Normalized Difference Vegetation Index (NDVI). The temperature is estimated from ground thermal radiation. Near infrared and visible radiation energies are converted into NDVI. 1-km NOAA-AVHRR channel 1, 2, and 4 data are used to estimate the ground radiation.The index is computed for East African arid, semi arid, and humid lands, savannahs, forestlands, and the highlands. Variations of SMET temporally and spatially show similar patterns, such as moisture, rainfall, and thei...
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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 | 0.000 |
| 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 | 0.000 | 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".