###### Abstract  
Impervious areas are one of the important indicators for evaluating the urbanization process, while surface temperature is one of the reference factors for evaluating the urban environment. In order to investigate whether the spatial distribution of an impervious surface has any influence on urban surface temperature, Xuzhou City was selected as the study area, and the impervious surface information was extracted based on the maximum likelihood classification method for Xuzhou City for the period of 2013-2022, and surface temperature inversion was performed using Landsat 8 remote sensing imagery and nighttime lighting data. In order to reduce the confusion between bare soil and impervious surfaces, the study area was divided into built-up and non-built-up areas for the selection of impervious and pervious surface samples using nighttime lighting data, and, finally, the maximum likelihood classification method was used to realize the extraction of impervious surfaces. The experimental results show that, by extracting the impervious surface of Xuzhou City, the impervious surface of Xuzhou City continued to increase from 2013 to 2022, in which the growth rate was faster in 2014-2016 and 2019-2021, and slower in 2017-2018 and 2021-2022, after performing surface temperature inversion as well as temperature grading. The results of impervious surface extraction and surface temperature inversion were subjected to overlay analysis and linear regression analysis. It was found that most of the impervious surface area is in high-temperature areas, and the density of the impervious surface is proportional to the surface temperature in the impervious surface and its surrounding area. Therefore, it can be concluded that the expansion of impervious surfaces is one of the reasons for the increase in urban surface temperature. impervious surface; land surface temperature; supervision and classification; spatio-temporal variation +
Footnote †: journal: applied sciences  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], C. Correlation Between Impervious Surface and Surface Temperature Change in Typical Urban Agglomerations--The Case Study of Xuzhou City--The Case Study of Xuzhou City--The Case Study of Xuzhou City--The Case Study of Xuzhou City, China  
[PERSON], [PERSON], [PERSON] The main methods of impervious surface extraction are the exponential method, spectral mixture analysis, and regression method. The exponential modeling approach is used to extract information about features based on differences in reflections of various features at different ranges by constructing a mathematical model. Some of the widely used exponential models are the P-index, impervious surface area index (ISA), Normalized Impervious Surface Index (NDISI), Modified Normalized Difference Impervious Surface Index (MNDISI), and triple-exponential methods, among others [6; 7; 8; 9]. The ISA index can effectively differentiate between vegetated and impervious surfaces, but ignores the effects of soils and water bodies, and has a narrower scope of application, only applying to areas where the cover type is predominantly impervious and vegetated. The NDISI index can more accurately distinguish between different land cover types in urban areas and can better minimize the influence of soil on impervious surface extraction, but the classification accuracy is low [10; 11]. The MNDISI index is based on the NDISI index combined with high-resolution nighttime light data to improve the extraction accuracy, but the nighttime light data are difficult to obtain, and the water body needs to be masked before use, which is a complicated process with many processing steps, and its application is limited [12]. [PERSON] et al. proposed a new Enhanced Impermeable Surface Index (ENDISI), which can effectively eliminate the interference of sandy bare ground in arid areas [13].