| Urban thermal environment is a complex system of interaction and influence of urban surface temperature and surrounding environment,which is one of the environmental problems arising in the process of urbanization.The urbanization in China is relatively late compared with the global urbanization,but the process is very fast,among which,the North China Plain is one of the most remarkable urbanization areas in China.The urbanization process in this region will continue in the future,and the accompanying urban thermal environment problems will continue to affect and even threaten the residents.Therefore,it is of great significance for the sustainable development of cities to accurately grasp the spatial and temporal characteristics of the urban thermal environment and the influencing factors,and to develop corresponding measures to address the urban heat island effect.Previous studies on urban thermal environment have been limited by the number and spatial distribution of observation stations,which has imposed certain constraints on their research content and conclusions.In recent years,with the development of thermal infrared remote sensing technology,the surface temperature data obtained by remote sensing satellite images have provided scholars with a new perspective for studying the urban thermal environment.Although some studies have been conducted to characterize the spatial distribution of the urban surface thermal environment in some detail,there is still a shortage of multi-city and large-scale systematic studies to analyze its influencing factors and there are relatively few studies on the trends of urban surface heat island intensity during daytime and nighttime as well as between years and months.This paper obtained daily MODIS surface temperature data for 61 cities in the North China Plain from 2019 to 2021 through the big data platform Google Earth Engine,and obtained a total of more than 3500 images including daytime and nighttime data by screening the data quality.Through further processing,temperature data were obtained for various combinations of daytime months,nighttime months,daytime years,and nighttime years.The impervious surface of the cities in the study area was also extracted using the sentinel-2 images and combined with NDVI,NDBI,and NDWI index methods in the platform and gridded to obtain the impervious surface percentage data with the same resolution as the surface temperature data.In addition,MODIS surface albedo data,nighttime lighting data,urban land cover utilization data and built-up area data were also collected in this paper.Based on the above multi-source remote sensing data,this paper employs various analysis and processing methods to conduct a large-scale and comprehensive analysis of the spatial and temporal changes and influencing factors of the surface thermal environment of 61 cities in the North China Plain in 2020,and the main conclusions are as follows:(1)This study analyzes the relationship between urban surface temperature and the proportion of impervious surfaces,and the results show that surface temperature in cities increases with the increase of the proportion of impervious surfaces.On average,for each 1% increase in the percentage of impervious surface,the annual average surface temperature increases by about 0.0213°C and0.0141°C during daytime and nighttime,respectively.In terms of seasonal variation,the rate of change of surface temperature with the percentage of impervious surface is higher in the warm season than in the cold season.In addition,it was found that surface albedo and green space in cities are the most important factors affecting the relationship between urban surface temperature and impervious surface ratio,which proves that improving urban green space condition to mitigate daytime heat island effect and increasing surface albedo to suppress nighttime heat island effect are effective means.(2)This study analyzes the relationship between urban heat island intensity and urban landscape pattern.The results show that heat island intensity in cities is positively correlated with the difference between the built-up area of urban and suburban areas,and negatively correlated with the difference in the average shape index,the difference in the density of patches and the difference in the aggregation index of built-up areas.In addition to built-up areas,the difference in the suburban landscape pattern of green areas also has an impact on urban heat island intensity.It is also found that urban heat island intensity is negatively correlated with the difference of urban landscape diversity index and positively correlated with the difference of urban sprawl index,indicating that increasing the degree of mixing of spatial distribution among different features and the diversity of surface use cover types can mitigate the urban heat island effect.These findings provide a scientific basis for urban planning and urban thermal environment improvement.There are also some shortcomings in this study,such as the influence of urban elevation in the study area was not considered when analyzing the relationship between surface temperature data and impervious surface ratio,and the number of surface cover classifications was too small when analyzing the urban landscape pattern.If the effects of elevation and climate are added and the classification of surface cover is more refined,it is believed that more accurate results will be obtained. |
