## 4 Discussion
### Heatwave Detection, Its Magnitude, and Characteristics
#### 4.2.2 Satellite-Based Land Surface Temperature Assessment
In the context of satellite-derived LST for heatwave detection, metrics such as detection accuracy, false alarm rate, and spatial-temporal consistency are scrutinized [141]. Nonetheless, it is imperative to consider additional factors, including spatial coverage, resolution, and the spectral properties of the satellite data, to ensure a comprehensive assessment. Our investigation, employing [PERSON]'s correlation analysis, reveals a significant relationship between observed heatwaves based on \(T_{\text{air}}\) and MODIS-derived LST across various areas, particularly in its correlation with heatwave indices as detailed in Table 4. This dual approach is comprehensive, as it covers both the atmospheric temperature (felt by residents) and the surface temperature (which influences the local microclimate). During daytime heatwave conditions, the findings are noteworthy, especially in the context of two specific heatwave characteristics, HWN and HWF, with \(r=0.55\)-\(0.71\) indicating a strong positive correlation. Other aspects, such as HWD, HWM, and HWA, demonstrate a moderate association. Consistent with the findings of [139], the observed match percentages are relatively high, especially when considering the differences in terms of HWM. Ourresults contrast daytime and nighttime heatwave conditions, noting weaker correlations across all heatwave characteristics during the nighttime, with \(r=0.02\)-\(0.45\). In particular, the degree that determines the spatial-temporal pairwise correlation is initially measured as shown in Figure 4. According to the findings of our research, grids located in rural areas have the potential to form the strongest associations compared to the other grids in terms of the HWN, HWF, and HWD, as measured by \(r=0.93\), \(r=0.94\), and \(r=0.80\), respectively. On the other hand, the correlation coefficients for HWM and HWA are found to be at their maximum in a peri-urban area (Pathum Tani), with values of \(r=0.65\) and \(r=0.85\), respectively. Overall, this study represents a significant step forward in our ability to model and understand heatwaves through LST data, especially during daytime heatwaves, and it is crucial, as it highlights the challenges in modeling nighttime heatwaves. This agrees with the findings of [38], which showed that the proposed LST index effectively identified heatwaves in the Mediterranean region during the daytime; however, this correlation was slightly weaker during the nighttime. The utilization of MODIS-retrieved LST datasets for heatwave mapping, especially in peri-urban and rural regions with limited meteorological data, marks a significant advancement over traditional methods. This is particularly beneficial, as it offers the potential for repetitive imaging at frequent sampling times [38]. The strong correlations in rural and peri-urban areas, coupled with the varied correlations in urban settings, underscore the intricate interplay between environmental factors and heatwave dynamics across diverse geographical landscapes. Our approach not only allows for comprehensive mapping of areas prone to heatwaves but also emphasizes the importance of extensive geographic analysis and demographic information in understanding heatwave patterns. Moreover, ref. [142] confirmed that both surface imperviousness (SI) and LST could be used to better understand spatial variation in heat exposures over longer time frames but are less useful for estimating shorter-term, actual temperature exposures, which can be useful for public health preparedness during extreme heat events.