## 1 Introduction  
Urban heat island (UHI) effect, a phenomenon whereby urban areas experience higher air and surface temperatures when compared to the rural areas, has attracted increasing attention due to its adverse impacts on the economy and environment [1; 2; 3]. The increased temperatures in urban areas increase energy use for air conditioning and alter the energy layout [4; 5; 6]. In turn, higher energy consumption raises the anthropogenic waste heat and pollution level [7; 8; 9], which directly warms the environment and enhances the pollution levels in urban areas. The persistent deterioration of the urban environment has a serious impact on the health of urban residents. Heat waves in summer significantly lead to thermal discomfort, heat-related diseases and a higher mortality rate [10; 11; 12; 13]. Understanding the formation and development of the UHI effect helps to draft appropriate mitigation strategies and deal with the negative effects. It is well documented that the drastic change in land use/land cover (LULC) associated with urbanization and human activities is a powerful driving force in UHI effect formation and local climate variation [14; 15]. With the tremendous growth of population in urban areas, the demand for urban roads, commercial, residential, industrial and other construction lands increases correspondingly [16; 17; 18]. A large number of natural landscapes, such as agricultural land, forest land and water body, are increasingly replaced by impervious surfaces such as roads, buildings and parking lots [19; 20; 21]. The modification of surface characteristics and LULC pattern alter the surface radiative, soil water content and thermal properties, and then change the thermal characteristic pattern over urban areas [14; 22; 23; 24; 25]. Extensive studies on the relationship between LULC change and UHI effect have been performed based on Landsat imagines. Classically, land use pattern was mapped based on land use classification and spatially correlated with land surface temperature (LST) distribution [26; 27] to describe the spatial coupling between LULC and LST. The average LST and standard deviation values for each LULC category were counted and compared to identify the impact of LULC change on UHI effect [28; 29; 30]. The findings confirmed that LULC characteristics did have an impact on LST variation, but how it works is not clear. Further studies focused on the influence of LULC composition and configuration on LST variation. The relationship between LST values and some landscape metrics such as area, density, shape index, adjacent index and diversity index were quantitively modeled [31; 32]. In addition, some LULC-related indices such as impervious surface areas (ISA) [33; 34], normalized difference vegetation index (NDVI) [35; 36], normalized difference built-up index (NDBI) [16; 37] and modified normalized difference water index (MNDWI) [26; 38] were used to indicate urban surface LULC characteristics. Their spatial correlations with LST values were quantified to specify the contribution of LULC variation on the UHI effect [14]. Previous studies showed that there exists a high spatial consistency between the LULC pattern and LST distribution [14; 15]. Construction land is always occupied by higher LST values, while water bodies and highly vegetated areas are covered by lower ones [39; 40; 41; 42]. When the construction land expanded, the area defined as UHI increased accordingly [27; 29; 43; 44]. These studies paid more attention to LST comparison among different LULC categories over time and lacked detailed information on how the UHI effect responds to LULC change over space and time. However, the urban environment is characterized by high spatial heterogeneity and temporal variability [27; 45]. UHI effect is considered to be context-sensitive, which varies significantly over space and time [30; 46].