## 3 Methodology  
The launch of satellites into orbit in recent decades has enabled the observation and monitoring of the Earth's surface for various scientific objectives, including meteorological conditions. The resolution of satellites is contingent upon their altitude and type, whether that is polar or geostationary. Unlike geostationary satellites, polar satellites have lower temporal resolution; they capture images of the same area at specific intervals but offer higher spatial resolution due to their lower orbital altitude [40]. The data for this analysis are sourced from Landsat 8, a polar satellite that orbits at an altitude of 705 km. The utilization of publicly accessible data from Landsat 8 provides a fine spatial resolution of 100 \(\times\) 100 m\({}^{2}\). Additionally, a well-established calculation methodology is employed to derive the land surface temperature (LST) [41]. Continuous improvements in the satellite's algorithms for processing thermal bands, as provided by its thermal infrared sensor (TIRS), further enhance data reliability, supported by rigorous validation from multiple researchers [8, 42, 43, 44]. Satellites typically pass over the same area twice per month; therefore, it is essential to analyze the images generated to ensure stable climatic conditions and clear skies, in order to avoid interference with the results. This information facilitates the estimation of surface temperatures for various urban features, including roofs, pavements, and types of land use, covering micro-scales such as streets and canyons, as well as medium and macro scales or regions [45, 46, 47, 48]. The wavelengths measured using remote sensors fall within the thermal band, encompassing ultraviolet radiation, visible light, and infrared rays, with a range from 0.1 mm to 100 mm. At this juncture, the Stefan-Boltzmann Law is applied, allowing for the calculation of radiation from a hot body to a cooler environment (Equations (1) and (2)) [49]:  
\[E=\sigma\mathrm{T}^{4} \tag{1}\]  
\[E=\varepsilon\sigma\Big{(}T^{4}-T_{\mathrm{C}}^{4}\Big{)} \tag{2}\]  
where:  
\(\sigma\) = The Stefan-Boltzmann constant 5.67 \(\times\) 10\({}^{-8}\) W/(m\({}^{2}\) K\({}^{4}\));  
\(\varepsilon\) = The emissivity of the surface;  
T = The absolute temperature of the object (\({}^{\circ}\)K);  
Tc = The absolute temperature of the environment (\({}^{\circ}\)K). These remote surveys, which identify temperature peaks, assume that land surface temperature patterns correlate with air temperature patterns. To acquire satellite data, users must access the official website of the United States Geological Survey ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 22 February 2024)). The process can be divided \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{2}{c}{**Highly**} & \multirow{2}{*}{**Moderately**} & \multirow{2}{*}{**Impervious**} & \multirow{2}{*}{**No Data**} \\  & **Impermeable** & & & \\  & **(\%)** & & & **(\%)** \\ \hline
1. Puerto Vallarta, Jalisco (Vall) & 17 & 67 & 13 & 2 \\
2. Manzanillo, Colima (Mzn) & 18 & 70 & 3 & 9 \\
3. Lazaro Cardenas, Michoacán (LC) & 17 & 72 & 6 & 5 \\
4. Tampiro, Tamapiras (Tmp) & 11 & 47 & 28 & 14 \\
5. Veracruz, Veracruz (Vrz) & 46 & 34 & 13 & 7 \\
6. Coatzacooles, Veracruz (Ctz) & 29 & 49 & 18 & 4 \\
7. Ciudad Carmen, Campeche (CC) & 31 & 37 & 17 & 15 \\ \hline \hline \end{tabular}  
\({}^{3}\) Modified and analyzed results from the data of the National Institute of Statistics and Geography [30].  
\end{table} 
Table 3: Percentage of pavement types in the cities analyzed \({}^{3}\).