地下煤火隐蔽性强且危害大，不仅破坏植被及生态环境，造成严重大气污染，且易诱发地质灾害，威胁周边人们群众的生命财产安全以及煤炭的安全生产，因此开展地下煤火灾害的准确识别与监测具有重要意义。为了解决单一遥感手段难以准确识别地下煤火的问题，基于2017—2019年的多景Landsat-8影像和Sentinel-1 A影像，利用普适性单通道算法和DS–InSAR（Distributed Scatterer Interferometry Synthetic Aperture Radar）技术分别获取了水西沟煤田长时序地表温度与形变分布信息，在此基础上基于阈值分割与时空耦合叠加分析等方法开展了融合地表温度与形变的地下煤火多源遥感识别研究。结果表明：地表长时序温度和形变时空协同分析可以有效克服非煤火高温或形变等复杂异常信号产生的影响，凸显了地下煤火信号在地表温度与形变2个维度中的响应特征。而且，地下煤火区地表温度异常与形变异常空间分布形态存在细微差异，其中形变异常得益于SAR影像分辨率和外界干扰因素较少等条件，其对地下煤火识别的指示作用更强，而基于温度异常正确识别的煤火区域范围则与实勘煤火边界具备更好的空间一致性。另外，地下煤火灾害的温度与形变峰值空间位置存在一定偏移，但在时间维度上2者响应具有明显的一致性，表现为稳定的异常高温与持续沉降。可见，与单一遥感手段相比，融合2者的方法能够更加准确地识别地下煤火区，为地下煤火灾害的广域普查和及时防治提供良好的技术方法支撑。

Underground coal fires have strong concealment and great harm, not only damaging vegetation and ecological environment, causing serious air pollution, but also easily inducing geological disasters, threatening the safety of life and property of surrounding people, as well as the safety of coal production. Therefore, accurate identification and monitoring of underground coal fire disasters is of great significance. To address the issue of difficulty in accurately identifying underground coal fires using a single remote sensing method, multiple Landsat-8 and Sentinel-1 A images from 2017 to 2019 were used. Long term surface temperature and surface deformation of Shuixigou coalfield were obtained using generalized single channel algorithm and DS–InSAR （Distributed Scatterer Inter fabric Synthetic Aperture Radar） technology, respectively. On this basis, research on multi-source remote sensing recognition of underground coal fires by integrating surface temperature and deformation was carried out based on methods such as threshold segmentation and spatiotemporal coupling superposition analysis. The results indicate that the spatiotemporal collaborative analysis of surface long-term temperature and deformation can effectively overcome the impact of complex abnormal signals such as non coal fire high temperature or deformation, and basically accurately restore the response characteristics of underground coal fire signals in the two dimensions of surface temperature and deformation. Moreover, subtle differences were found in the spatial distribution patterns of surface temperature anomalies and deformation anomalies in underground coal fire areas. The deformation anomaly benefits from the resolution of SAR images and fewer external interference factors, which have a stronger indicating effect on underground coal fire identification. However, the range of coal fire areas correctly identified based on temperature anomalies has better spatial consistency with the actual coal fire boundaries. In addition, there is a small deviation between the temperature and deformation peak spatial position of underground coal fire disasters. However, there is a clear consistency in the response between temperature and deformation in the time dimension, which is characterized by stable abnormal high temperatures and continuous subsidence in the coal fire area. It can be seen that compared to a single remote sensing method, the method of integrating temperature and deformation can more accurately identify underground coal fire areas, providing good technical support for the wide area survey and timely prevention and control of underground coal fire disasters.