Abstract: Single remote sensing techniques cannot simultaneously capture both surface temperature and surface subsidence characteristics, and are susceptible to interference from seasonal variations and human activities, which may lead to misidentification of coal fire areas. To address these limitations, this study proposed a multi-source remote sensing method for detecting shallow-buried coal fire areas by integrating thermal infrared remote sensing and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. Multi-season, long-term thermal infrared data were first acquired using thermal infrared remote sensing. Surface temperature was then retrieved using the split-window algorithm, and temperature anomaly areas were extracted using the temperature threshold segmentation method. Interferometric phase information was subsequently obtained based on SBAS-InSAR, and surface subsidence rates were calculated using the singular value decomposition method to delineate subsidence anomaly areas. Finally, spatial overlay analysis was performed between the temperature anomaly areas and the subsidence anomaly areas, and regions exhibiting both temperature and subsidence anomalies were identified as coal fire areas. The results showed that: ① surface temperature variations in coal fire areas were strongly correlated with seasonal changes, and temperature anomalies were more pronounced during non-summer periods. In contrast, surface subsidence in coal fire areas exhibited stable and continuous development. The temperature anomalies extracted by thermal infrared remote sensing were generally consistent with the subsidence anomalies identified by SBAS-InSAR in the temporal dimension, whereas certain discrepancies existed in the spatial dimension. ② Thermal infrared remote sensing was susceptible to seasonal variation, surface environmental disturbances, and the loss of high-temperature anomalous pixels, resulting in an underestimation of coal fire areas. SBAS-InSAR, with its high spatial resolution, produced detection results that more closely matched the actual distribution of coal fire areas, but it could not independently delineate coal fire areas because of the influence of surface backfilling and natural subsidence. ③ The integration of thermal infrared remote sensing and SBAS-InSAR effectively reduces the influence of seasonal temperature differences and human activities on coal fire detection results and improves the detection accuracy of coal fire areas.