Nighttime satellite land surface temperature for urban applications: achievements, challenges, and future prospects
Bibliographic record
Abstract
Satellite-derived nighttime land surface temperature (LST) provides unique insights into urban thermal dynamics, such as nocturnal urban heat island effects, differentiated from daytime LST. Recent advances in satellite technology and upcoming missions promise high spatiotemporal resolution nighttime LST, unlocking new opportunities for urban studies. This study presents a comprehensive review of 420 peer-reviewed papers published between 2016 and 2024 to identify the trends in how nighttime LST has been used in the urban application and summarize the progress, key achievements, and current constraints in six main application topics: urban heat island analysis, heat and health impacts, associations with greenspace and air temperature, synergetic usage with numerical models, and other interdisciplinary applications. Based on our review, we suggested five main future directions for nighttime LST studies, including improving nighttime LST data resolution and quality, advancing modeling techniques, expanding geographic and climatic coverage, exploring emerging topics such as anthropogenic heat and nighttime heatwaves, and integrating nighttime LST with multidimensional urban data. Further research using nighttime LST is expected to understand better nocturnal thermal dynamics and their impacts on public health, energy use, and environmental sustainability.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".