Underground coal fire is widely distributed and repeatedly treated, causing waste of resources and ecological damage. China is the country with the most serious coal spontaneous combustion disaster in the world, 80% of coal seams have the tendency to spontaneous combustion. Rapid, comprehensive, timely and accurate detection of hidden fire sources in coalfields is the basis and prerequisite for fire prevention, extinguishing and ecological management. Multi-source remote sensing has a great potential for the applications, but it needs to penetrate the surface and go deep underground, and there are many bottlenecks to be solved. Firstly, the problem of multi-source remote sensing detection of hidden fires in coalfields is abstracted into the key nodes of same source (same underground spontaneous combustion source), multi-phenomenon (various abnormal phenomena formed on the surface), multi-image (photographed by multi-source remote sensing, including a variety of surface image of abnormal information). Meanwhile, the research chain of multiple phenomena is analyzed, which includes the same source, the phenomenon to image mapping, the transmission from source to phenomenon, and the multiple image recognition source. On these basis, the technical bottleneck of multi-source remote sensing detection of concealed fire sources in coalfields is discussed. Secondly, based on the research examples of concealed fire detection in coal fire areas of Fukang, Miquan and Bao'an in the Xinjiang Uygur Autonomous Regions, China, we give the research progress and effects of polarized time-series interferometric synthetic aperture radar (InSAR) fire area deformation detection, spatio-temporal temperature threshold method fire area delineation, multi-source satellite remote sensing fire area identification, and unmanned aerial vehicle fire area monitoring experiment. Finally, the development direction of integrating multi-source satellite remote sensing images and space-sky-ground-mine cooperative perception cognitions is prospected.