The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to map LCZs. Using Gaofen-6 and Sentinel-1/2 as data sources, this study designed four schemes using convolutional neural network (CNN) and random forest (RF) classifiers, respectively, to demonstrate the potential of high-resolution images in LCZ mapping and evaluate the optimal combination of different data sources and classifiers. The results showed that the combination of GF-6 and CNN (S3) was considered the best LCZ classification scheme for urban areas, with OA and kappa coefficients of 85.9% and 0.842, respectively. The accuracy of urban building categories is above 80%, and the F1 score for each category is the highest, except for LCZ1 and LCZ5, where there is a small amount of confusion. The Sentinel-1/2-based RF classifier (S2) was second only to S3 and superior to the combination of GF-6 and random forest (S1), with OA and kappa coefficients of 64.4% and 0.612, respectively. The Sentinel-1/2 and CNN (S4) combination has the worst classification result, with an OA of only 39.9%. The LCZ classification map based on S3 shows that the urban building categories in Xi’an are mainly distributed within the second ring, while heavy industrial buildings have started to appear in the third ring. The urban periphery is mainly vegetated and bare land. In conclusion, CNN has the best application effect in the LCZ mapping task of high-resolution remote sensing images. In contrast, the random forest algorithm has better robustness in the band-abundant Sentinel data.

As a kind of renewable energy, the development and utilization of solar energy is valued by many countries. Sunshine duration (SD), as an important factor to measure solar energy, has also been widely discussed as relevant in terms of distribution and variation. The spatial patterns and variation trends in SD and related factors (wind speed, precipitation, relative humidity, mean temperature and elevation) over mainland China have been studied based on data from 569 meteorological stations during 1961–2016. The results indicated that annual SD decreased significantly at the rate of −40.7 h/10a over mainland China and the decline trend was the most pronounced in the 1980s. Seasonally, the decline rate in SD was the largest in summer (−16.8 h/10a), followed by winter (−9.9 h/10a), autumn (−9.5 h/10a) and spring (−4.5 h/10a), respectively. Spatially, the decline trend in SD was significantly higher in the eastern region than in the western region during 1961–2016, especially in North China. SD was positively correlated with wind speed (R = 0.76); however, it was negatively correlated with mean temperature (R = −0.60) and precipitation (R = −0.41). Moreover, altitude and population density may affect the values and variations of annual SD over mainland China. This study provides a new perspective for the reduction of SD in mainland of China. The drastic changes in SD, such as abrupt changes and sudden decreases, were closely related to volcanic eruptions. Among them, the mean mutation and sudden decrease of SD in the 1980s were due to the long-time weakening of the aerosol accumulated by multiple volcanic eruptions. After the volcanic eruptions in the early 1990s, volcanic aerosols were gradually dissipating, resulting in a small rebound in SD.

Regional management of the urban thermal environment is essential for sustainable development. However, both the surface urban heat island (SUHI) spatiotemporal patterns and driving mechanisms across humid–arid regions remain uncertain. Therefore, 329 cities from various humid–arid regions were selected to investigate the interannual, seasonal, and diurnal distribution characteristics of SUHIs across regions. By constructing six-dimensional influencing factors and using CatBoost-SHAP and SEM methods, the contributions and action pathways of these factors to SUHIs were analyzed across humid–arid regions. The influence mechanisms, differences in feature importance, and similarities and discrepancies in action pathways were thoroughly examined. The findings are as follows: 1. During the day, higher SUHII values occur in humid and semihumid regions, exceeding those in arid and semiarid regions by 1.521 and 0.921, respectively. At night, arid and semiarid regions exhibit UHI effects (SUHII > 0). The SUHI distribution across humid–arid regions demonstrates seasonal variations. 2. ΔSA and ΔNDVI are stable dominant influencing factors across all regions. The contribution rank varies along the humid–arid region: Pollution factors are more important in arid and semiarid regions, whereas surface features and 2D/3D dominate in humid and semihumid regions at night. 3. SUHI regulation by influencing factors across humid–arid regions follows both similar paths and regional variations. This study reveals the SUHI distribution across humid–arid regions and provides reference data for regional thermal environment management.

The urban lockdown caused by COVID-19 has affected the world economy and human activities, but it appears to have a positive impact on urban environment. In this study, MODIS land surface temperature (LST) and AOD data were selected, combined with Sentinel-5p images and meteorological elements, to analyze the changes and associations of air pollution, LST, and urban heat island (UHI) in three urban agglomerations over China during the COVID-19 lockdown. Results showed that: during the COVID-19 lockdown (February 2020), AOD and the concentration of atmospheric pollutants (PM2.5, NO2, and CO) have decreased significantly. LST in all three urban agglomerations increased by more than 1oC, which was mainly due to climate factors, but city lockdown will hinder the positive LST anomalies. The decrease of nighttime urban heat island intensity (UHIInight) in the three urban agglomerations was greater than daytime, and more than 25%. The reduction of UHIInight is mainly due to reduced human activities and air pollutant emissions. Although COVID-19 lockdown has a positive effect on air pollution and UHI, these changes quickly reverted when the lockdown policy is relaxed. Moreover, small-scale lockdowns do little to improve the environment. Our results have implications for assessing the environmental benefits of city lockdowns.