*4.3. Relation between PCI and Its Impact Factor*

First, we analyzed the relation between PCI and the spectral indices inside the park. The results of correlation with PCI are shown in Figure 5. We found that FVC has a positive effect on PCI: the more FVC we have, the higher PCI appears. However, the coefficient of determination R<sup>2</sup> is only 0.237. This means that PCI only partly depends on vegetation cover. Figure 5b indicates that higher NDWI contributes to higher PCI, this quadratic regression analysis coefficient of determination (R2) is 0.433. Among the three factors, the NDISI has the strongest relationship with PCI (Figure 5c), the coefficient (R2) is 0.618, which means the impervious surface has a significant influence on PCI. So, from the park spectral indices results, we can recognize the park vegetation and water percentage play a decisive role in PCI, while the high impervious surface reduces the cooling effect of parks.

**Figure 5.** Regression analysis of park PCI and (**a**) mean FVC; (**b**) mean NDWI; (**c**) mean NDISI.

Secondly, we analyzed the relation between PCI and park characteristics (patch metrics). PCI has a complex correlation with park patch metrics (Figure 6). Among the four analysis results, the size, fractal dimension (Frac\_Dim), and perimeter area ratio (Paratio) regression coefficient of determination R2 is 0.321, 0.355, 0.439, respectively (Figure 6a–c) which means those three factors contribute to the PCI in general. While the shape index showed no significant correlation, as its linear model R2 is 0.089 (Figure 6d). Park shape index does not contribute to PCI among the selected sample parks of Zhengzhou.

**Figure 6.** Regression analysis among PCI and mean park characteristics: (**a**) Size; (**b**) Frac\_Dim; (**c**) Paratio; (**d**) Shape\_Indix.

Thirdly, we investigated the relationship between PCI and the impact factor of the park surrounding area. In order to analyze the impact of PCI and the type of land cover around the parks, we selected 43 parks with similar mean LST (within the range of 29.0 ◦C–30.0 ◦C) from 123 samples (Figure 7). For external land cover types, we use spectral indicators: NDVI, NDWI, NDISI to measure vegetation, water coverage and impervious surfaces of the surroundings.

**Figure 7.** Regression analysis between the PCI and the impact factor of park surrounding area (500 m buffer). (**a**) surrounding FVC; (**b**) surrounding NDWI; (**c**) surrounding NDISI, park type: LST range (29–30 ◦C) based selection of 43 parks: 1-urban park; 2-theme park; 3-street park; 4-linear park.

The linear regression analysis was used to analyze the PCI relationship with the three factors outside the parks. The results show (Figure 7) that in the case of parks within the LST range of 29–30 ◦C the type of land cover around the park has a significant impact on PCI. PCI has a negative correlation with surrounding vegetation and water bodies, and a positive correlation with impervious surfaces in cases we analyzed from elements within the same LST range (29–30 ◦C). This shows that PCI is not only affected by the internal factors of the park but also related to the surrounding environment.

In addition, we analyzed the location factor on PCI based on the city rings. Zhengzhou city has three rings defined by the urban ring road (Figure 2b), the first ring is the urban center area, which is denser than the other two. The parks in the first ring have the highest average PCI (Figure 8a), as the land cover types in the urban center are mostly commercial areas and built-up areas with tall buildings and impervious surfaces, which are warmer than other areas of the city. The third ring is the low-density urban area and is covered with more green spaces and mostly low-rise buildings. We recognized that PCI is also influenced by the location factor, which is partly in relation to the different land cover types of park surroundings.

**Figure 8.** Analysis between PCI and location factor and park types (all 123 samples). (**a**) PCI and park location in three city rings; (**b**) PCI and five park types: 1-urban park; 2-theme park; 3-street park; 4-linear park; 5-urban square.

The park type can be defined based on different surrounding types, for example, linear park is mostly located, and surrounded by road or river, the urban square is usually located in the high-density area. Due to this reason, the linear park and urban square show low PCI (Figure 8b). These results are mainly attributed to the different surrounding environments and land cover of different park types. So, we can conclude that PCI is also related to the surrounding land cover types.
