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<p>The Advanced Geosynchronous Radiation Imager (AGRI) onboard China’s Fengyun (FY)-4 satellites, which provides observational data across various wavelengths from visible to infrared (IR), holds great potential for diverse applications. However, the <i>FY-4A</i> AGRI mid-wave IR (MWIR) band (3.75 µm) is often contaminated by stray light in the midnight hours during the 1–2 months before and after the vernal or autumnal equinoxes. In this study, a U-Net-based deep learning model was employed to generate an expedient MWIR band from the <i>FY-4A</i> AGRI long-wave IR band. Validation using normal radiance measurements revealed that MWIR brightness temperatures generated by the deep learning model are very close to those observed by the <i>FY-4A</i> AGRI, with mean absolute error of 1.48 K, root mean square error of 2.39 K, and a correlation coefficient of 0.99. When applying the model to periods of stray light contamination, the brightness temperature anomalies found in the <i>FY-4A</i> AGRI MWIR band are effectively eliminated. The findings of this study could support various scientific applications that necessitate use of the MWIR band during midnight hours, such as identification of fog/low stratus cloud.</p>

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Expedient Mid-Wave Infrared Band Generation for AGRI during Stray Light Contamination Periods Using a Deep Learning Model

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Haixia Xiao,
  • Xiaoyong Zhuge,
  • Fei Tang,
  • Jimin Guo

摘要

The Advanced Geosynchronous Radiation Imager (AGRI) onboard China’s Fengyun (FY)-4 satellites, which provides observational data across various wavelengths from visible to infrared (IR), holds great potential for diverse applications. However, the FY-4A AGRI mid-wave IR (MWIR) band (3.75 µm) is often contaminated by stray light in the midnight hours during the 1–2 months before and after the vernal or autumnal equinoxes. In this study, a U-Net-based deep learning model was employed to generate an expedient MWIR band from the FY-4A AGRI long-wave IR band. Validation using normal radiance measurements revealed that MWIR brightness temperatures generated by the deep learning model are very close to those observed by the FY-4A AGRI, with mean absolute error of 1.48 K, root mean square error of 2.39 K, and a correlation coefficient of 0.99. When applying the model to periods of stray light contamination, the brightness temperature anomalies found in the FY-4A AGRI MWIR band are effectively eliminated. The findings of this study could support various scientific applications that necessitate use of the MWIR band during midnight hours, such as identification of fog/low stratus cloud.