Effects of the new Priestly-Taylor equation on determining the boundary of LST/FVC space for soil moisture monitoring
Bibliographic record
Abstract
Land Surface Temperature and Fractional Vegetation Coverage (LST/FVC) space is a classical model in remote sensing of soil moisture (SM). Its most vital issue is the determination of the boundary i.e. dry and wet edges. Visual interpretation and automatic fitting methods are very demanding for the research area. In contrast, theoretical calculation of the boundary has great application potential, where the traditional Priestley-Taylor (PT) equation has been introduced to derive a Sun2016 method. Recently, a new Priestly-Taylor equation was suggested. In order to furtherly improve the optical & thermal remote sensing of SM, we evaluated the new PT equation for boundary determination through deriving a Sun 2021 Sun H, Liu H, Ma Y, Xia Q. 2021. Optical remote sensing indexes of soil moisture: evaluation and improvement based on aircraft experiment observations. Remote Sens. 13(22):4638.[Crossref] , [Google Scholar] method. The evaluation was conducted using data from three aircraft experiments for SM observation i.e. SMAPVEX12, SMAPVEX16 in Iowa, and SMAPVEX16 in Manitoba. Simulated data with the Simsphere model was also used. Results demonstrated that the effects of the new PT equation are related to air temperature (Ta). For a certain range of Ta such as from 290 K to 310 K, the Sun 2021 Sun H, Liu H, Ma Y, Xia Q. 2021. Optical remote sensing indexes of soil moisture: evaluation and improvement based on aircraft experiment observations. Remote Sens. 13(22):4638.[Crossref] , [Google Scholar] method is close to the Sun2016 method, which implies limited influence of the new PT equation within that range. For Ta out of that range, the Sun 2021 Sun H, Liu H, Ma Y, Xia Q. 2021. Optical remote sensing indexes of soil moisture: evaluation and improvement based on aircraft experiment observations. Remote Sens. 13(22):4638.[Crossref] , [Google Scholar] method presented better performance than the Sun2016, which implies stronger suitability of the new PT equation. Sensitivity analysis indicated that the new PT equation increases the sensitivity of calculated wet edge to Ta while decreases its sensitivity to the other input variables. For future extensive application, we also explored convenient ways for determining some essential parameters in the Sun 2021 Sun H, Liu H, Ma Y, Xia Q. 2021. Optical remote sensing indexes of soil moisture: evaluation and improvement based on aircraft experiment observations. Remote Sens. 13(22):4638.[Crossref] , [Google Scholar] method. The new PT equation has potential to promote the optical & thermal remote sensing of SM, evapotranspiration, drought, etc.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".