## 3 Experimental Results and Analysis
### Effect of Impermeable Surfaces on Surface Temperature  
The Landsat@ OLI/TIRS C2L2 dataset is a multispectral atmospherically corrected surface reflectance image and surface temperature data, which is a multispectral band surface reflectance and thermal infrared band surface temperature product, and, using the 10 th band in the imagery, surface temperatures can be obtained by simple calculations based only on this. The formula is as follows, where \(DN\) is the image element value and \(ST\) is the surface temperature. \[ST=DN\times 0.00341802+149-273\] \begin{table}
\begin{tabular}{c c c} \hline
**Year** & **Method Accuracy** & **Kappa** \\ \hline
2013 & 97.80\% & 0.8562 \\
2014 & 97.79\% & 0.8660 \\
2015 & 97.81\% & 0.8851 \\
2016 & 97.75\% & 0.8951 \\
2017 & 97.76\% & 0.8991 \\
2018 & 97.77\% & 0.9025 \\
2019 & 94.74\% & 0.8460 \\
2020 & 92.08\% & 0.8867 \\
2021 & 93.23\% & 0.9109 \\
2022 & 95.28\% & 0.9127 \\ \hline \end{tabular}
\end{table} 
Table 1: Impervious surface extraction accuracy for 2013–2022. Figure 4: Changes in impervious surface area in Xuzhou City, 2013–2022. After obtaining the surface temperature using the mean standard deviation method, the surface was divided by temperature into low-, sub-low-, medium-, sub-high-, and high-temperature zones, respectively, and the temperature classes are shown in Table 2. Un is the surface temperature, Um is the mean surface temperature in the study area, and Us is the standard deviation [27]. To quantitatively analyze the impact of impervious surfaces on surface temperature, the results of temperature classification and impervious surface extraction were overlaid for analysis, and the temperature grading of impervious surface area was obtained, as shown in Table 3, which shows that more than 50% of impervious surfaces in the years 2013-2022 are high-temperature or sub-high-temperature zones. Also, less than 20% of the impervious surface area was in either the cryogenic or sub cryogenic zone from 2013-2022. In contrast, about 50 percent of the permeable surface is distributed in the medium-temperature zone, and about 30 percent is distributed in the low-temperature or sub-low-temperature zone. The temperature in the impermeable surface region is significantly higher than in the permeable surface region. To visualize the effect of impervious surfaces on the surface temperature, the surface is divided into aggregated impervious surface area, other impervious surface area, and permeable surface area, and the average temperature of each area is calculated; details are shown in Table 4. Aggregated impervious surface areas had the highest average temperatures, followed by other areas of impervious surfaces, and permeable surfaces had the lowest average temperatures. The largest temperature difference was in 2016 when the average temperature in areas with clustered impervious surfaces was 5.49 \({}^{\circ}\)C higher than the average temperature on the previous surfaces. This further indicates that the more aggregated the impervious surfaces, the higher the surface temperature. Figure 5 shows the distribution of impervious surfaces in selected areas in 2013 overlaid with the high-temperature areas from the surface temperature inversion results. From the figure, it can be seen that most of the impervious surface areas are high-temperature zones, especially in the areas where impervious surfaces are aggregated.