In the context of global warming, more and more cities are experiencing extreme Urban Heat Island (UHI) effects and extreme weather phenomena, but urban green spaces are proven to mitigate UHI. Most of UHI’s research focuses on the large scale and uses remote sensing methods, which do not reflect the dynamic characteristics in detail and do not detect internal influencing factors of the green space cooling effect. Therefore, this study focused on Small Green Spaces (SGS), carrying out the measurement of the meteorological parameters (temperature, relative humidity, wind direction, wind speed, photosynthetic radiation) of the 16 sites in four types of coverage (Impervious surface; Shrub-grass; Tree-grass; Tree-shrub-grass) in a university campus. At the same time, the coverage characteristic parameters, such as Canopy Density (CD), Leaf Area Index (LAI), Photosynthetically Active Radiation (PAR), Mean Leaf Angle (MLA), of each plot were analyzed and compared. The results showed that there were significant differences in temperature among different coverage types in SGS. The biggest difference was concentrated in the noon period when solar radiation is strongest during the day. The difference between the four types of coverage with vegetation at night was small. The maximum air temperature difference among the four types could reach 8.9 ℃ and the maximum relative humidity difference was 28.5%. The cooling effect of the multi-layer vegetation-covered (Tree-shrub-grass) area was the largest compared to the impervious surface, indicating that tree cover was the core factor affecting the temperature. Temperature and relative humidity had a close correlation with surface coverage types and some plant community characteristics (such as CD and LAI). The cooling and humidifying effects of plants were also related to PAR and leaf angle. The results provide suggestions for green space management and landscape design.

Urban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R² = 0.99) and 0.9 ℃ (R² = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R² = 0.6, daytime) and 2.2 ℃ (R² = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R² = 0.34, daytime) and 1.2 ℃ (R² = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers.

Predictive models for urban air temperature (T_air) were developed by using urban land surface temperature (LST) retrieved from Landsat-8 and MODIS data, NDVI retrieved from Landsat-8 data and T_air measured by 24 climatological stations in Szeged. The investigation focused on summer period (June−September) during 2016−2019 in Szeged. The relationship between T_air and LST was analyzed by calculating Pearson correlation coefficient, root-mean-square error and mean-absolute error using the data of 2017−2019, then unary (LST) and binary (LST and NDVI) linear regression models were developed for estimating T_air. The data in 2016 were used to validate the accuracy of the models. Correlation analysis indicated that there were strong correlations during the nighttime and relatively weaker ones during the daytime. The errors between T_air and LST_MODIS-Night was the smallest, followed by LST_MODIS-Day and LST_Landsat-8 respectively. The validation results showed that all models could perform well, especially during nighttime with an error of less than 1.5^o. However, the addition of NDVI into the linear regression models did not significantly improve the accuracy of the models, and even had a negative effect. Finally, the influencing factors and temporal and spatial variability of the correlation between T_air and LST were analyzed. LST_Landsat-8 had a larger original error with T_air, but the regression model based on Landsat-8 had a stronger ability to reduce errors.

Near-surface air temperature (Tair) is critical for addressing urban challenges in China, particularly in the context of rapid urbanization and climate change. While many studies estimate Tair at a national scale, they typically provide only daily data (e.g., maximum and minimum Tair), with few focusing on sub-daily urban Tair at high spatial resolution. In this study, we integrated MODIS-based land surface temperature (LST) data with 18 auxiliary data from 2013 to 2023 to develop a Tair estimation model for major Chinese cities, using random forest algorithms across four diurnal and seasonal conditions: warm daytime, warm nighttime, cold daytime, and cold nighttime. Four model schemes were constructed and compared by combining different auxiliary data (time-related and space-related) with LST. Cross-validation results were found to show that space-related and time-related variables significantly affected the model performance. When all auxiliary data were used, the model performed best, with an average RMSE of 1.6 °C (R² = 0.96). The best performance was observed on warm nights with an RMSE of 1.47 °C (R² = 0.97). The importance assessment indicated that LST was the most important variable across all conditions, followed by specific humidity, and convective available potential energy. Space-related variables were more important under cold conditions (or nighttime) compared with warm conditions (or daytime), while time-related variables exhibited the opposite trend and were key to improving model accuracy in summer. Finally, two samples of Tair patterns in Beijing and the Pearl River Delta region were effectively estimated. Our study offered a novel method for estimating sub-daily Tair patterns using open-source data and revealed the impacts of predictive variables on Tair estimation, which has important implications for urban thermal environment research.

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.

Urban air temperature (Tair) is an essential variable for a variety of urban issues, and analyzing the spatial patterns of Tair is of great importance for urban planning and management. However, it is difficult to obtain Tair data with a high spatial resolution because of the absence of weather stations within a heterogeneous city. In this research, Tair predictive models were developed in Szeged by the help of an urban climate monitoring system which is a part of COST FAIRNESS project. Our investigation focused on the whole year instead of just the summer which has been the most studied season for urban heat issues. Two statistical mthods, multiple linear regression and random forest regression, were used for developing Tair estimation models. Tair data obtained from in situ meteorological stations from 2014 to 2017 were used as the dependent variable for models training. Land surface temperature data from numerous MODIS satellite images and 7 auxiliary variables were used as the independent variables. The auxiliary data are open source, including local climate zone (LCZ) lassification data containing a wide range of urban surface information and atmospheric parameters in the urban boundary layer associated with near-surface urban climate. The atmospheric parameters calculated from the ERA5 reanalysis data. We calculated RMSE based on 10-fold cross-validation to valadite the models. The results indicated that the random forest models performed better than multiple linear models with lower RMSE in four seasons. According to the importance analysis in random forest, both LCZ classification and atmospheric parameters are effective in reducing model errors. LCZ parameters affect the models significantly during the day, while atmospheric parameters affects the models significantly at night. Both effects of these two auxiliary variables showed their maximum in summer. In the end of this research, we used the final models to estimate Tair and mapped the seasonal average Tair patterns from 2018 to 2019. Our overall aim is to develop a generalized methodology using globally available land surface temperature and auxiliary data capable to estimate the urban air temperature.