## 4 Discussion  
### Performance Evaluation of Land Surface Temperature Predictive Modeling  
Given the inherent limitations of MODIS-LST, which often contains gaps due to cloud cover and other atmospheric conditions [47; 69], filling these missing data points is particularly critical in heatwave assessment studies that require daily data inputs rather than instantaneous or averaged values. Consequently, a crucial step in heatwave assessment involves the optimization of the dataset during preprocessing, which includes a systematic treatment of missing values essential for accurate heatwave detection. Selecting appropriate input variables is crucial before training machine learning models [112]. Our results align with the findings of [113], confirming that LST patterns are not constant and exhibit seasonal variations. By selecting a more varied set of predictors than those used in previous studies, our study highlights the critical importance of temporal factors in LST analysis. It significantly enhances heatwave analysis by exploring the implications of identified correlations between different variables, particularly in understanding the dynamics of heatwaves and their impacts. Our selected methodology, RF, aligns with the findings of [114], which underscore the importance of incorporating a diverse range of environmental and land-use factors in predicting LST. By employing both temporal and spatial variables as significant predictors for LST modeling, our approach not only advances our understanding of LST but also highlights the pronounced impact of temporal variables. This emphasis suggests that these factors may have a more substantial effect on LST than previous studies. Moreover, our work builds upon the findings of [47; 115; 116], which linked LST with factors such as land cover types, terrain, vegetation, moisture conditions, solar radiation, elevation, and Julian day. Interestingly, our research has distinctively identified the DOY as the most significant feature for predicting daytime LST (MOD11A1; \(R^{2}\) = 0.28 and MYD11A1; \(R^{2}\) = 0.36) as shown in Figure 3, owing to its critical role in influencing weather patterns through its capture of seasonal changes. Similarly, the daily \(T_{\text{min}}\) is crucial for nighttime LST prediction (MOD11A1; \(R^{2}\) = 0.33 and MYD11A1; \(R^{2}\) = 0.36), confirmed by the study of [117], which stated that the average of nighttime LST was closest to \(T_{\text{min}}\), addressing diurnal temperature variations in both urban and non-urban settings. The outstanding performance of the MOD11A1 night model is supported by its RMSE of 2.09 \({}^{\circ}\)C and high \(R^{2}\) value of 0.64. These results align with theories suggesting that certain algorithms possess superior capabilities in processing and analyzing complex environmental data, particularly during stable atmospheric conditions at night. Our results are consistent with those of [62], who demonstrated the high spatial-temporal continuity and relative accuracy of reconstructed nighttime MODIS-LST products across China. However, temporal feature extraction, which involves incorporating time-related variables such as day of the year to account for seasonal effects on temperature, is significant. This process is reflected in the challenges faced by the MYD11A1 day model, which showed data overestimation issues with the highest RMSE (5.02 \({}^{\circ}\)C) and the lowest \(R^{2}\) (0.29). In contrast with the study of [118], it was found that MOD11A1 and MYD11A1 products slightly underestimated daytime LST with an overall absolute bias < 0.9 \({}^{\circ}\)C and RMSE < 2.9 \({}^{\circ}\)C. The effective use of the MOD11A1 night model and the challenges associated with the MYD11A1 day model are consistent with theories in environmental data analysis. This demonstrates a nuanced understanding of how algorithms perform under various atmospheric conditions. However, when utilizing LST for heatwave studies, the requirement for daily data is critical and should be a primary consideration in future research.