Coal mining provides energy and economic benefits but also causes environmental damage, including land degradation, pollution, and surface temperature anomalies. Underground coal fires can severely impact the environment, leading to abnormal heat, ground deformation, and ecological harm. Using Landsat-9 imagery and meteorological data, we developed a new threshold-based method to detect large-scale land surface temperature anomalies (LSTAs). By analyzing multiple images from November to February, we improved the accuracy of this method. The LSTA data were integrated with topographic indexes and different coal seam depths to filter irrelevant points. A Wilcoxon test, correlation analysis, and linear regression were performed with the LSTA multi-data matrix to quantify the relationships between the topographical and temperature indexes. The results revealed significant differences in elevation (relative elevation), slope, and TWI across different coal seam depths (p < 0.001). LST distribution in November, December, and February was significantly different among the three different seam depth units (p < 0.001). Relative elevation strongly correlated with temperature. The relationship between relative elevation and temperature may change seasonally due to seasonal climatic fluctuations and heterogeneous underlying surface characteristics.