Research on Air Temperature Inversion Method Based on Land Surface Temperature of Different Land Surface Cover
Bibliographic record
Abstract
This study explores a method for deriving air temperature (AT) from land surface temperature (LST) based on different urban land-use types, aiming to address the accuracy of urban heat island (UHI) effect measurements. Using Wuhan as a case study, the research integrates remote sensing data with ground meteorological observations to develop various models, analyze their accuracy and applicability, and generate LST and AT maps to validate model reliability. The results indicate that when establishing the LST–AT relationship, polynomial regression performs best for water bodies (R2 = 0.905), while random forest yields the highest R2 for built-up areas, cropland, and vegetation at 0.942, 0.953, and 0.924, respectively. Due to the characteristics of the algorithms, it is recommended to prioritize random forest for prediction when the sample range covers the observed data range and to use BP neural networks when it does not. The generated maps reveal that in summer, using LST significantly overestimates UHI intensity in the study area, while differences between UHI intensities in winter are negligible. In resource-constrained scenarios, LST can be directly used to assess the UHI effect.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".