**1. Introduction**

The Urban Heat Island (UHI) problem has been studied for more than 200 years since it was discovered in 1818 [1]. It refers to the phenomenon that the temperature of urban areas is higher than the temperature of its surrounding area [2–4]. The main reason for this phenomenon is the process of urbanization, as the vegetation is replaced with the built-up area during urban development [5]. This process leads to changes in the physical properties of the surface structure which modifies the thermal environment of urban areas. At present, urbanization is a major driving force within developing countries and their rapid urban growth towards becoming a developed nation. Taking China as an example, according to the National Bureau of Statistics of China, the urbanization rate in China was 59.58% in 2018. This number is 12.58% higher than in 2008. This high-speed urbanization had led to widespread UHI problems in China [6]. With this rapid increase in urbanization has come an increase in energy consumption, with individuals attempting to reduce these adverse temperature effects (e.g., air conditioning, private vehicle usage). As a consequence, higher energy consumption based on coal power plants may result in air pollution, water pollution, and additional climate changes. More specifically, UHI compromised the health and life quality of citizens [7].

Generally speaking, UHI can be measured by two methods: one of them is Surface UHI (SUHI); the other one is atmospheric UHI. Atmospheric UHI is defined into two different types: canopy layer UHI and boundary layer UHI [8,9]. From studies on the UHI effect, SUHI is widely used to characterize the UHI effect in the case of regional or urban studies. Due to the development of remote sensing technology, a high number of studies used satellite imagery to derive land surface temperature (LST) [6,10–12]. Concerning the investigation of UHI characteristics and changes in large-scale, these studies mainly focus on: (1) The spatial distribution of UHI; (2) the methods of satellite image inversion; (3) the relationship between land use land cover (LULC) and LST [13]. Furthermore, landscape pattern analysis in a regional scale was also proved to be a proper method in UHI research [14].

However, in small-scale UHI studies, the UHI is mainly characterized by the actual measured air temperature [15–17]. These kinds of studies mainly focused on the temperature difference between the green space and other land types, and the method of characterizing UHI intensity. In addition, some studies add microclimate factors and use the Local Climate Zone (LCZ) factors [3,18] to investigate UHI. These microclimate conditions such as wind speed, wind direction, humidity, solar light intensity, surface reflectance and other localized effects on temperature [19–21]. Those studies showed that the green space cools the air due to the transpiration of the plants, which contributes to low UHI. In addition, local wind speed and wind direction also modify air temperature. The higher the surface albedo, the lower the temperature is found to be [22]. Besides the complexity of local climate and related environmental conditions the measurement of air temperature is limited by the monitoring system, including pieces of equipment, experts, method and such factors. The accuracy of data collection is always a key factor and it is almost impossible to conduct ideal UHI research on a wide range of space-time scales. These data sources are however very useful in understanding the generalized sources of data studied.

In the studies of Urban Cold Island (UCI) effects and UHI mitigation [23,24], two methods were used to quantify the cooling effect of green space and park areas. These are called Green space Cooling Intensity (GCI) and Park Cooling Intensity (PCI) [25,26]. GCI is defined as the temperature difference between green space and the average temperature of the whole study area. While the PCI usually determined as the temperature difference between the inside park area and its outside within a 500 m buffer area [27,28]. These two methods are used to describe the cooling effect of green spaces and parks. Studies also show that vegetation coverage has a significant effect on the reduction of UHI [10,29,30]. On top of this, the impervious surface area is positively correlated with LST and contributes the most to UHI [10,31,32]. Some studies use landscape factors to analyze the UCI and GCI on mitigating the UHI effect [33–35]. The parameters include shape index (Shape\_ldx), fractal dimension (Frac\_Dim) and landscape connectivity. One research study focused on the role of green space in reducing the UHI effect. This study looked at the distance changes of the green space cooling effect in relation to the characteristics of green space bodies with regards to size, perimeter, shape index, fractal demission, and UHI. The results of these studies showed that the cooling effect of green space is complex [25–27]. PCI related studies mostly used remote sensing imagery as base data. According to a study based partly on surrounding vegetation, water body and impervious surface [26], the cooling effect of parks is also depending on types of outside the park spaces. The air temperature is also employed to examine the cooling effect of parks on UHI [36,37]. It is also noted that the patch and pattern of a park has a relationship to the cooling effects that it has on the UHI it forms part of. Some researches employ the

thermal conduct theory, a physical science methodology, to investigate the heat balance (between park and its surrounding area) when studying the urban thermal environment [38–40], this method of study can assist in providing a methodical approach to understanding the cooling effect of park.

At present, cities have grown exponentially. The actual scale and speed of China's overall urbanization process has never seen before in modern urban development. Super-large cities and megacities have become commonplace in China's inland areas. These big cities result in urban environment problems like UHI, For example, In Zhengzhou city, Studies showed that from 1996 to 2014, the average LST increased by 2.94 ◦C in Zhengzhou city [41], and the UHI change was positively correlated with land cover changes over this period. It has also been proven that the increase of the built-up urban areas [42] showed a negative correlation with the vegetation cover rate in Zhengzhou. UHI studies in the city of Zhengzhou analyzed many factors, but these neglected to focus on green space categories types while also not using large enough sample sizes.

In this paper, we selected the latest cloud-free satellite image acquired on 07 July 2019 as base data to focus on the UHI effect characteristics in the megacity of Zhengzhou. Choosing 123 parks as samples identified though high-resolution Google Earth images, we investigated the cooling effect characteristics of the chosen parks. The cooling intensity and park buffer sizes were studied, and the correlation between the park patch metrics and the cooling intensity was explored. We aimed to: (1) Analyze the PCI differences among five park types; (2) analyze the park LST and its relation to vegetation, water surface area and impervious surface factors; (3) analyze park LST and its relation to park patch indices; (4) analyze PCI and its relation to park patch indices and impact factors of park surrounding areas. As a whole, this research was conducted to analyze the relationship between park cooling effect and its related impact factors, to understand UHI characteristics in Zhengzhou. The intention of this analyses understanding is to give guidance for stakeholders, as well as to the developers of urban planning strategies to address UHI.
