Geo-Kompsat-2A (GK-2A) is the third new-generation geostationary meteorological satellite that orbits Asia and monitors China and its surrounding areas, following the Himawari-8 and Fengyun-4A satellites. The nadir point positioning and satellite channel parameters of the GK-2A are better than those of the Himawari-8 and FY-4A, which are more conducive to fire monitoring in China. In this study, a new fire detection algorithm is proposed based on GK-2A satellite data. That is, considering the large solar zenith angle correction for reflectance and the proportion information of background pixels in the existing spatial threshold method, fires under the different underlying surface types and solar radiation states can be automatically identified. Moreover, the accuracy of the Himawari-8 fire monitoring algorithm and the present algorithm of GK-2A is compared and analyzed through the ground truth fire spot data. The results show that compared with the original fire monitoring algorithm with fixed parameter thresholds, the brightness temperature difference of this algorithm is reduced by 0.55 K, and the correction coefficient is reduced by 0.6 times, the fire can be found earlier, and the monitoring sensitivity is improved. According to the practical fire case, the present fire monitoring algorithm of GK-2A has better monitoring accuracy than the fire monitoring algorithm of Himawari-8. The present fire monitoring algorithm of GK-2A can meet the fire monitoring requirements under different sun angles, different cloud cover ratios and vegetation ratios with good versatility.

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In this study, we compare the data of the advanced geostationary radiation imager (AGRI) on board the FY-4B and the advanced meteorological imager (AMI) on board the GK-2A, in terms of overall data, different reflectivity/brightness temperature intervals, different regions, and different underlying surfaces. The results show that the AGRI and AMI data are generally consistent; the mean biases for reflectivity channels show a range of 0.50% to 1.69%, with channel VIR004 being exceptionally good, while brightness temperature (TB) differences in the IR channels ranging from 0.11 to 0.57 K, with channel IR120 being the most accurate. The reflectivity of the AGRI is higher than that of the AMI in terms of mean bias. The dispersion of the reflectivity difference between the AGRI and AMI is smaller at the short-wavelength channels than that at the longer-wavelength channels. The TB data observed by the AGRI are higher than those of AMI at conditions above 310 K. In the case of observing the same target, the difference in infrared brightness temperature due to the random noise signal is small. The differences between the two sensors can be considerably reduced by revising mean biases. In the following studies of quantitative product algorithms, the characteristics of sensor data need to be further analyzed in detail.