Model development for estimating urban air temperature in China integrating satellite-based LST and auxiliary variables with machine learning

Accurate near-surface air temperature data at high spatiotemporal resolution is crucial for understanding the urban thermal environment, especially given rapid urbanization and global climate change. However, existing research often focuses on daily air temperature metrics, neglecting the importance of sub-daily variations. This study addresses this gap by developing a Random Forest model to estimate sub-daily air temperature in major Chinese cities. Leveraging MODIS-derived land surface temperature (LST) from 2013 to 2023, the model incorporates 18 auxiliary variables encompassing time-related (e.g., atmospheric indices) and space-related (e.g., elevation, land cover) factors. To account for diurnal and seasonal variations, the model was trained and evaluated separately under four distinct conditions: daytime/nighttime and warm/cold. Cross-validation was employed to assess model performance. Results indicate optimal performance during warm nighttime conditions, achieving a low root mean square error. Analysis of variable importance revealed LST as the most influential predictor across all conditions, followed by humidity-related variables. Furthermore, the study found that the relative importance of auxiliary variables shifts with time of day and season. Time-related variables exert greater influence during warm conditions and daytime, while space-related variables become more important in cold seasons and nighttime. This highlights the importance of including diverse auxiliary variables for accurate sub-daily air temperature estimation. Developed using open-source data and cloud computing platforms like Google Earth Engine, the model offers a readily accessible and adaptable tool for urban climate research. This research not only provides valuable high spatiotemporal resolution air temperature data for Chinese cities but also presents a transferable methodology applicable to urban climate studies globally. The resulting data can contribute significantly to a deeper understanding of urban thermal dynamics and the development of effective urban heat island mitigation strategies.