*4.2. Relation between Park LST and Its Impact Factor*

First, we analyzed the relation between park LST and the spectral indices inside the park. The results showed that the mean park LST was significantly related to the FVC, the NDISI, and the NDWI (Figure 3). The cooling effect of the park is directly proportional to the park's vegetation percentage (R2 = 0.489), indicating that more vegetation cover makes parks cooler (Figure 3c). For example, Xiongerhe park's FVC has one of the highest values (0.408), while the mean temperature is the lowest (28.06 ◦C). Moreover, the average PCI of Xiongerhe shows it is much colder (2.18 ◦C) than its surrounding area. The results showed that the NDWI plays a negative role in park LST (Figure 3b), indicating that the NDWI value strengthens the cooling effect of the park. On the contrary, the NDISI has a relatively positive effect on park LST. From the regression model between LST and NDISI, the coefficient of determination (R2) reached 0.926 (Figure 3c), revealing that the impervious surfaces have a significant impact on park temperature. The impervious surface is the main contributor to warm conditions of parks. We can conclude that water and vegetation have a positive impact on park cooling roles in Zhengzhou while the impervious surface increases the park warmth.

**Figure 3.** Regression analysis among mean park LST and, (**a**) FVC, (**b**) NDWI, (**c**) NDISI.

Secondly, we analyzed the relation between park LST and park characteristics (patch metrics). The result of the analysis shows that patch metrics have relations to park LST. From Figure 4, park size is negatively correlated with the mean park LST (Figure 4a, R<sup>2</sup> = 0.308), which means the park size is one of the main factors of LST. We can see from the Figure 4a, if the park size was larger than 40 ha, the average LST was below 31 °C, and the average LST appeared in a wide temperature range among the parks with size below 20 ha. Fractal dimension (Frac\_Dim) and perimeter area ratio (Paratio) show a positive correlation with the park LST, and the coefficient of determination R2 is 0.191, 0.280. This indicates that these two factors also have an impact on LST. The shape index has no significant correlation with park LST (Figure 4d). For example, the park with a maximum shape index (2.13) has the same LST (28.80 ◦C) as parks with the lowest shape index (1.21) (Figure 4e). From the results, we can conclude that the park size and perimeter-area ratio play a more critical role than other patch metrics in the sample parks of Zhengzhou city.

**Figure 4.** Regression analysis among mean park LST and park characteristics: (**a**) Size; (**b**) Frac\_Dim; (**c**) Paratio; (**d**) Shape\_Idx; (**e**) Three selected park examples.
