# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 1 Introduction  
With the rapid development of cities, the heat island effect [1; 2] is becoming increasingly significant and endangering people's health [3; 4]. With urban residents' living standards improving, people are demanding a healthy living environment. The heat island effect is therefore an increasingly important issue in the process of urban development. Studying the temporal and spatial distribution characteristics of the urban heat islands can provide guidance for the construction of the urban public environment. This information can be used to inform the future city planning for a sustainable and healthy development. Remote sensing has the advantages of continuous monitoring of heat island effects over both time and large-scale space with finer and finer spatial details. The radiant brightness temperature measured at the thermal infrared wavelength is usually combined with surface-specific emissivity and other parameters to obtain accurate surface temperature products. Currently, Surface Urban Heat Islands (SUHIs) and Canopy Layer Heat Islands (CLHIs) have been widely studied relative to the surface temperature over city areas [5; 6; 7; 8]. The SUHI, which characterizes temperature variations at the surface, exhibits a profound correlation with material types and urban surface orientation relative to the sun. On the other hand, the CLHIpertains to the warming of urban air specifically within the layer extending from the ground to the rooftops of buildings and city trees. When assessing its impact on health, ambient temperature may emerge as a more crucial factor compared to surface temperature [9; 10]. From the two types of data sources of the urban heat island, the CLHI is usually calculated based on the temperature data obtained from weather stations or mobile temperature measurement vehicles, while the SUHI is studied through the surface temperature obtained by inverting remote sensing data. In terms of the correlation between the two types of temperature data, in addition to comparative studies, researchers have also proposed various methods for estimating temperature using satellite remote sensing data [11; 12; 13]. In terms of the formation of CLHIs and SUHIs, surface temperature has an important impact on the air temperature at the height of the shutter. The fundamental difference between air temperature and surface temperature is that surface temperature is the temperature at the surface and the atmosphere, that is, the solid-gas interface, while air temperature is pure gas phase temperature. The calculation of heat island intensity is usually obtained by the difference in temperature. The difference between surface temperature and air temperature leads to the difference between an SUHI and CLHI [14; 15]. The spatial structure of urban heat islands is one of the main research topics in remote sensing applications. Many scholars have analyzed the pattern and form [16; 17], change process [16; 18], and influencing factors [19; 20] of urban heat islands through observations from remote sensing sensors of Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging (MODIS), Enhanced Thematic Mapper Plus (ETM+), and so on. Studies on the spatial changes of urban heat islands have mainly used correlation analysis [21], the section line method [22], the triangle method [23], and the fractal method [24]. [PERSON] [25] fitted the spatial distribution of urban heat islands to a Gaussian surface and found that urban heat island size was negatively correlated with rural temperature, but the spatial extent was independent of both levels, providing new perspectives for exploring the spatial trend and continuity of urban heat islands. In subsequent research, this method was called the Gaussian capacity model. Through the Gaussian capacity model, urban heat island research can be carried out in respect of the strength, center, range, and direction of heat islands, and it can quantitatively analyze and describe the change characteristics of urban heat islands through model parameters.