## 2 Materials and Methods
The surface temperature is determined by correcting the emitted radiance of the ground (B) in Kelvin (K) using the mono-window algorithm proposed by [PERSON] et al. [28]. NDVI (Normalized Difference Vegetation Index) is calculated using Equation (9), where \(\uprho\)NIR and \(\uprho\)RED represent the values of the near-infrared and red bands, respectively. Calculating NDVI is important as it closely relates to the vegetation cover fraction (Pv). Pvrepresents the percentage of vegetation in the pixel and is calculated based on [PERSON] and Ripley [29] using Equation (9):  
\[\text{Pv}=((\text{NDVI}-\text{NDVIs})/(\text{NDVIv}-\text{NDVIs}))^{2} \tag{9}\]  
Pv is also related to the emissivity (E), which is calculated using Equation (10):  
\[\text{E}=\varepsilon\text{v}\text{Pv}+\varepsilon\text{g}\,(1-\text{P})+4< \text{d}\varepsilon>\text{Pv}\,(1-\text{Pv}) \tag{10}\]  
In Equation (10), \(\varepsilon\) represents the land emissivity, \(\varepsilon\)v represents the emissivity of pure vegetation (set to 0.985), \(\varepsilon\)g represents the emissivity of bare soil (set to 0.960), and \(<\)d\(\varepsilon>\) is a parameter adjusted to an average of 0.01. Finally, the surface temperature in Kelvin is converted to Celsius using Equation (11):  
\[\text{Surface temperature}=(\text{TB}/(1+(\lambda\times\text{TB}/\text{C2}) \times\text{ln}(\varepsilon)))-273.15 \tag{11}\]  
In Equation (11), TB represents the sensor brightness temperature in Kelvin (K), \(\rho=\text{hc}/\sigma\), where \(\sigma\) is the Boltzmann constant (1.38 \(\times\) 10\({}^{-23}\) J/K), h is the Planck constant (6.626 \(\times\) 10\({}^{-34}\) J/s), \(\varepsilon\) is the speed of light (2.998 \(\times\) 10\({}^{8}\) m/s), and \(\varepsilon\) is the emissivity. Overall, the temperature differences between MODIS satellite data and MMD data are lower compared to the differences between Landsat satellite data and MMD data. For example, MODIS satellite data have the largest temperature difference of +1.2 \({}^{\circ}\)C compared to that of the MMD data, while Landsat data has a difference of \(-\)1.7 \({}^{\circ}\)C (Figure 3). The 0.5 \({}^{\circ}\)C difference between the two RS datasets is due to the characteristics of the data. MODIS represents the monthly average temperature, while Landsat represents the daily average temperature. Daily average temperature data has higher variability due to daily weather fluctuations compared to monthly average temperature, which represents the overall average of daily temperatures [4]. Figure 3: Difference in daily temperature values with LST (land surface temperature) from the Landsat satellite. The selection of RS data depends on the research objectives of the investigators. Previous studies have reported that MODIS data are highly suitable for studies involving regional and global scales due to their 5600 m resolution. Additionally, researchers can gain a detailed understanding of land surface temperature changes because MODIS data are obtained from two satellites, Aqua and Terra, which provide daily data both during the day and at night. MODIS data can be obtained in various time scales, including daily, 8-day average, weekly average, monthly average, and annual average temperatures. The continuous and freely available data provided to researchers worldwide have made MODIS data the second most important dataset for studying weather and climate changes, especially temperature data. However, MODIS data were launched in 1999, and the data were usable from 2000 onwards. This means that researchers lost useful data before 2000. This is why the Landsat satellite is the preferred choice for studying temperature, as the Landsat series, including Landsat 4/5 TM, 7 ETM, 8 OLI TIRS, and 9 OLI TIRS 2, has been providing data from 1989 to 2021. This has allowed researchers to extensively study the impact of development changes on temperature [5; 6]. Furthermore, Landsat data offer the highest thermal data resolution for temperature studies, currently at 100 m, compared to the 5600 m resolution of MODIS.