Enhanced Detection of Subsurface Combustion: An Improved Index Combined with Time Series Analysis

Subsurface combustion in coal mines poses a significant threat to ecosystem integrity, geological stability, and public safety. Effective risk mitigation requires continuous monitoring and accurate detection of combustion dynamics. In this study, an improved subsurface combustion index (SCI) was developed based on multisource remote sensing indicators, and long-term time series observations (2010–2025) were used to characterize its spatiotemporal evolution. Results show that dREGI achieved the highest anomaly discrimination among all evaluated vegetation indices, with an M-statistic of 1.4186, outperforming NDVI (1.1073) and EVI (0.8226). Adaptive principal component analysis identified dREGI and H as the dominant contributors to SCI construction. Separability analysis further demonstrated that integrating dREGI with LST and H improved the performance of the composite SCI by 16.3%, increasing its M-statistic from 0.959 to 1.115 relative to the dREGI-only baseline. Temporally, subsurface combustion exhibits a multi-stage evolution, with initial anomalies emerging around 2013, followed by a transitional phase during 2014–2018. Activity intensifies during 2019–2023, peaks in 2023, and declines in 2024, indicating residual combustion. Spatially, high-risk areas are concentrated in the eastern region, while moderate and low-risk zones occur in the central and western regions, respectively. These results demonstrate that the proposed indices provide a more robust and sensitive framework for early warning and spatial delineation of subsurface combustion zones.