Abstract
The production of true color images requires observational data in the red, green, and blue (RGB) bands. The Advanced Geostationary Radiation Imager (AGRI) onboard China’s Fengyun-4 (FY-4) series of geostationary satellites only has blue and red bands, and we therefore have to synthesize a green band to produce RGB true color images. We used random forest regression and conditional generative adversarial networks to train the green band model using Himawari-8 Advanced Himawari Imager data. The model was then used to simulate the green channel reflectance of the FY-4 AGRI. A single-scattering radiative transfer model was used to eliminate the contribution of Rayleigh scattering from the atmosphere and a logarithmic enhancement was applied to process the true color image. The conditional generative adversarial network model was better than random forest regression for the green band model in terms of statistical significance (e.g., a higher determination coefficient, peak signal-to-noise ratio, and structural similarity index). The sharpness of the images was significantly improved after applying a correction for Rayleigh scattering, and the images were able to show natural phenomena more vividly. The AGRI true color images could be used to monitor dust storms, forest fires, typhoons, volcanic eruptions, and other natural events.
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Supported by the National Key Research and Development Program of China (2018YFC150650) and National Satellite Meteorological Center Mountain Flood Geological Disaster Prevention Meteorological Guarantee Project 2020 Construction Project (IN_JS_202004).
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Xie, Y., Han, X. & Zhu, S. Synthesis of True Color Images from the Fengyun Advanced Geostationary Radiation Imager. J Meteorol Res 35, 1136–1147 (2021). https://doi.org/10.1007/s13351-021-1138-3
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DOI: https://doi.org/10.1007/s13351-021-1138-3