At present, remote sensing forest fire monitoring mainly focuses on the accuracy of fire point detection by polar-orbiting satellites. At the same time, there is less research on remote sensing monitoring and identification of fire points, smoke characteristics and other comprehensive fire information based on multi-source remote sensing images. The forest fire of May 9, 2020, in Anning City, Yunnan Province, was studied based on the Gaofen-6 wide-field (GF-6 WFV) data and the FY-3D polar-orbiting meteorological satellite medium-resolution spectrometer (FY-3D MERSI) data for smoke, burned areas extraction and fire point identification. Firstly, Based on GF-6 WFV data, six spectral feature indices were selected to identify fire smoke and fire trails by maximum likelihood, support vector machine, and random forest classification methods, and evaluated for accuracy. Then, Based on the 1 km mid-infrared channel data of FY-3D MERSI, the potential fire point identification algorithm is improved, and the basic principles of FY-3C VIRR and MODIS fire point detection are combined with dynamic threshold and context detection method to identify fire points. Then the identification results are optimized by combining the far-infrared channel with 250 m resolution. Finally, the information on smoke, fire points and fire trails extracted from the two kinds of data were combined to explore and analyze the monitoring capability of GF-6 WFV and FY-3D MERSI for forest fires. The results show that the smoke and burned areas can be effectively identified by five feature indices and eight bands of GF-6 WVF data, and the random forest classification is the most effective among the three classification methods, with an overall classification accuracy and Kappa coefficient of 97.20% and 0.955. The improved fire point recognition algorithm for FY-3D MERSI data can effectively improve the recognition accuracy of fire points. Combining the mid-infrared -and far-infrared channels to detect fires can improve the fire detection accuracy from kilometer to 100 meter level. The combined GF-6 and FY-3D MERSI data can effectively extract smoke, burned areas and fire point information from the fire site, and the use of multi-source data can carry out forest fire monitoring and early warning in multiple directions, which is of great significance to improve the capacity of satellite remote sensing forest fire monitoring.