<p>Coal fires are pervasive global issues that affect the environment continuously, particularly in coal-rich regions; these fires can cause significant environmental damage, safety hazards, and economic losses. While numerous studies have investigated coal fires using remote sensing techniques, research integrating multisource remote sensing data for comprehensive coal fire zone detection and monitoring remains relatively limited. Current methods often analyze different data sources independently, limiting our understanding of the complex relationships between various surface manifestations of coal fires. This study presents a novel comprehensive analysis method employing multisource remote sensing technology to identify and monitor coal fires. Using 29 Landsat-8 images from Sulabulak fire area, we derived fractional vegetation cover (FVC) and land surface temperature (LST) parameters to identify vegetation loss patterns and thermal anomalies. In addition, 135 dual-polarized Sentinel-1A images were analyzed using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) and persistent scatter interferometric synthetic aperture radar (PS-InSAR) techniques to obtain surface deformation data. The integration of these datasets, validated by field survey data, revealed a significant correlation between the identified coal fire zones and subsidence areas. Our results revealed an increase in sparse vegetation areas of 2.77 km², an expansion of high-temperature anomalies of 0.75 km², and a cumulative surface subsidence of −123.9&#xa0;mm in the study area. These findings indicate ongoing and intensified coal fire combustion as well as an expansion of coal fire zones. The effectiveness of this method in identifying coal fire areas highlights its potential for enhancing coal fire monitoring and management strategies.</p>