A channel selection methodology for enhancing volcanic SO 2 monitoring using FY-3E/HIRAS-II hyperspectral data Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.5194/amt-18-2333-2025
· OA: W4411004534
The Hyperspectral Infrared Atmospheric Sounder Type II (HIRAS-II) aboard the Fengyun 3E (FY-3E) satellite provides valuable data on the vertical distribution of atmospheric states. However, effectively extracting quantitative atmospheric information from the observations is challenging due to the large number of hyperspectral sensor channels, inter-channel correlations, associated observational errors, and susceptibility of the results to influence by trace gases. This study explores the potential of FY-3E/HIRAS-II in atmospheric loadings of SO2 from volcanic eruptions. A methodology for selecting SO2-sensitive channels from the large number of hyperspectral channels recorded by FY-3E/HIRAS-II is presented. The methodology allows for the selection of SO2-sensitive channels that contain similar information on variations in atmospheric temperature and water vapor for minimizing the influence of atmospheric water vapor and temperature on SO2. A sensitivity study shows that the difference in brightness temperature between the experimentally selected SO2-sensitive channels and the background channels' efficiency removes interference signals from surface temperature, atmospheric temperature, and water vapor during SO2 detection and inversion. A positive difference between near-surface atmospheric temperature and surface temperature enables the infrared band to capture more SO2 information in the lower and middle layers. The efficiency of FY-3E/HIRAS-II SO2-sensitive channels in quantitatively monitoring volcanic SO2 is demonstrated using data from the 29 April 2024 eruption of Mount Ruang in Indonesia. Using FY-3E/HIRAS-II measurements, the spatial distribution and qualitative information of volcanic SO2 is easily observed. The channel selection can significantly enhance the computational efficiency while maintaining the accuracy of SO2 detection and retrieval, despite the large volume of data.
