## 4 Discussion
### Summary of Comparative Analysis with Existing Related Works  
Our LST predictions are consistent with the conclusions drawn in various related studies that utilize the RF model and consider both temporal and spatial variables (refer to [49; 112; 113; 143]). These studies present compelling findings akin to ours. Moreover, our methodology, which emphasizes a wide array of predictors, especially temporal factors, not only corresponds to but also advances upon prior research (refer to [47; 114; 115; 116; 117]). Notably, our analysis identifies the Day of Year (DOY) and minimum daily air temperature (\(T_{\text{min}}\)) as pivotal in predicting daytime and nighttime LST, respectively. However, this finding contrasts with [118], which noted a slight underestimation in LST predictions using MOD1A1 and MYD1A1 products. Thus, our work highlights the nuanced contributions of diverse environmental, land-use, and temporal variables to accurate LST prediction, thereby enhancing model performance and reliability.  
In agreement with [123], our study highlights the significant impact of \(T_{\text{air}}\) heatwaves in regions such as Pathum Thani (suburban) and Bangkok (urban), resulting in these areas being the most susceptible to global warming. We reveal the link between increased heatwave metrics (HWF, HWD, HWM, HWA) and significant El Nino events in 1997, 2013, 2016, and 2019, a finding supported by [133; 134; 135; 136]. Our results also align with the research by [26; 137], providing further evidence of how climatic shifts influence urban heatwave patterns, thereby enriching our understanding with a comprehensive, referenced analysis. Moreover, comparing satellite and in situ air temperature data revealed more intense nighttime heatwaves, consistent with [106], resulting in a pronounced increase in nighttime heatwave frequency and intensity, which relates to the UHI effect. Contrasting with [139] on the use of LST data, our findings reveal a significant correlation between air temperature and MODIS-LST data with robust daytime correlations between Tair and LST in rural (HWN, HWF, HWD, \(r>0.90\)) and peri-urban (HWM, HWA, \(r>0.65\)) regions, challenging traditional perspectives and highlighting the complexity of heatwave analysis.  
Furthermore, our integration approach unveils key insights into daytime heatwave dynamics, with strong correlations in heatwave number and frequency, and moderate connections in duration, magnitude, and amplitude, both supporting and diverging from the findings of [139]. Specifically, rural areas demonstrate pronounced spatial-temporal correlations, highlighting the efficacy of our method in detecting heatwave patterns through LST data--an essential advancement for tackling the challenges of nighttime heatwave analysis, as noted by [38]. By utilizing MODIS-derived LST datasets, our study enhances the understanding of heatwave episodes in less monitored peri-urban and rural areas, significantly improving upon traditional methods. This approach, validated by [142], emphasizes the importance of surface imperviousness and LST in mapping the spatial variability of heat exposure, underlining the intricate interplay between environmental factors and heatwave dynamics.  
Ultimately, this study effectively utilizes the RF model along with temporal and spatial predictors to enhance LST predictions, providing critical insights into the dynamics of daytime and nighttime heatwaves. Our findings highlight the vulnerability of urban and peri-urban areas like Bangkok and Pathum Thani to heatwaves, exacerbated by climatic shifts such as significant El Nino events and global boiling effects. By deepening our understanding of these impacts, our research supports the development of informed climate adaptation strategies, crucial for improving urban and non-urban resilience against escalating heatwave frequencies and intensities.