Simulation and Evaluation of Mid-Infrared Sea Surface Sun Glint Directional Radiation
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1.National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;2.University of Chinese Academy of Sciences, Beijing 100049, China

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Supported by the integration and demonstration technology of Transparent Earth-Observing (2023YFB3907705)

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    Abstract:

    The energy received in the mid-infrared (MIR) band at the sensor's aperture includes both reflected solar energy and the emitted energy from Earth's surface. Typically, the reflected solar energy in this band is weak; however, under certain conditions, such as in sun glint regions over the sea surface, the reflected solar energy detected by the MIR channel can be substantial. Currently, the application of sun glints physical models in the MIR band is not well-understood. This study investigates the accuracy of applying different visible light and shortwave infrared sun glint models to the MIR band. The paper selects three models: Breon-Henriot, Ebuchi-Kizu, and Wu, and evaluates the sensitivity of each sun glint model. Subsequently, using four selected MODIS sun glint images as data sources, and combining them with ERA5 reanalysis data matched to satellite data for atmospheric parameter calculations, the solar radiation intensity reflected by the sea surface is computed using the three models. The accuracy of each model is then further validated with an MIR radiation transfer model. The results show that the Breon-Henriot model generally performs best in terms of correlation coefficient and root-mean-square error compared to MODIS measurements. These findings not only extend the application range of sun glint models in the MIR band but also enhance the MIR forward modeling system, providing new theoretical support for MIR radiation transfer and improving the effectiveness and accuracy of MIR remote sensing products in climate change monitoring and sea surface temperature dynamic analysis.

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History
  • Received:September 05,2024
  • Revised:December 09,2024
  • Adopted:September 30,2024
  • Online: December 05,2024
  • Published:
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