Observation Angle Effect of Near-Ground Thermal Infrared Remote Sensing on the Temperature Results of Urban Land Surface
Bibliographic record
Abstract
During the process of urbanization, a large number of impervious land surfaces are replacing the biologically active surface. Land surface temperature is a key factor reflecting the urban thermal environment and a crucial factor affecting city livability and resident comfort. Therefore, the accurate measurement of land surface temperature is of great significance. Thermal infrared remote sensing is widely applied to study the urban thermal environment due to its distinctive advantages of high sensitivity, wide coverage, high resolution, and continuous measurement. Low-altitude remote sensing, performed using thermal infrared sensors carried by unmanned aerial vehicles (UAVs), is a common method of land surface observation. However, thermal infrared sensors may experience varying degrees of sway due to wind, affecting the quality of the data. It is still uncertain as to what degree angle changes affect thermal infrared data in urban environments. To investigate this effect, a near-ground remote sensing experiment was conducted to observe three common urban land surfaces, namely, marble tiles, cement tiles and grasses, at observation angles of 15°, 30°, 45°, and 60° using a thermal infrared imager. This is accompanied by synchronous ground temperature measurements conducted by iButton digital thermometers. Our results suggest that the temperature differences between the remote sensing data of the land surface and the corresponding ground truth data increase as a function of the increasing observation angle of the three land surfaces. Furthermore, the differences are minor when the observation angle changes are not more than 15° and the changes are not the same for different land surfaces. Our findings increase the current understanding of the effects of different angles on thermal infrared remote sensing in urban land surface temperature monitoring.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".