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Atmospheric water vapor plays a vital role in global climate change. It not only affects local weather and climate but also influences the global water cycle and energy balance. Therefore, an accurate understanding of the atmospheric water vapor content and its changes are of great importance for forecasting climate, understanding global climate change, and studying the greenhouse effect. In this paper, the inversion of atmospheric water vapor content was conducted by FY3B/MERSI data, and verification was made by comparing with the sounding data, ground water vapor pressure data, and AQUA/MODIS water vapor content data. The results show that FY3B/MERSI atmospheric water vapor content has a significant positive correlation with the sounding water vapor content, ground water vapor pressure data, and AQUA/MODIS water vapor content data. Among them, the correlation with the sounding water vapor content is the most significant. An inversion correction model of MERSI atmospheric water vapor content can be established to improve the inversion accuracy.

Almost all forest fires in Liaoning were caused by the act of men. Between 15:00-19:00, which is when the frequencies of human activities and the occurrence of fire peak, Liaoning is out of the path of the polar-orbiting satellites, making these 4 hours the blind spot domain in fire-incidence monitoring. At the same time, the trend of fire accidents changed quickly under the influence of local meteorological conditions, leading to the demand for a higher temporal resolution. Therefore, there is an urgent need to utilize the Himawari satellite in the forest fire intensity monitoring so as to compensate for the drawbacks of the polar-orbiting satellites. By improving the forest fire identification model of the Himawari-8 satellite, a forest fire monitoring done every ten minutes was realized , thereby enhancing the effectiveness of fire monitoring and rescue within a given period of time.