Generation of Hypothetical Radiances for Missing Green and Red Bands in Geostationary Environment Monitoring Spectrometer

Abstract

True-color imagery is essential for an intuitive comprehension of atmospheric data. However, the Geostationary Environment Monitoring Spectrometer (GEMS) of the geostationary Korea multipurpose satellite (GK) 2B lacks green and red bands, which limits its ability to monitor atmospheric environments. To mitigate this issue, we suggest an innovative method of generating virtual GEMS green and red bands using conditional generative adversarial networks with data observed in the blue-green-red (RGB) bands of the Advanced Meteorological Imager sensor, a payload of the GK-2A satellite. The paired datasets of the AMI blue band and the AMI RGB bands were used to train and test the data-to-data (D2D) translation model. Using the GEMS blue band as input data, the D2D model generated GEMS hypothetical radiance data at the green and red bands. Our results show that the D2D model generated hypothetical GEMS green and red bands with outstanding performance. The averaged values of the correlation coefficient, root-mean-square error, and bias between the observed and D2D-generated GEMS blue band were 0.999, 3.450 W/cm2/cm/sr, and −1.858 W/cm2/cm/sr, respectively. This research is expected to significantly contribute to the monitoring and comprehension of atmospheric environments in Asia and potentially improve the GEMS's global ability to monitor air quality. Additionally, the proposed method has the potential to enhance the capabilities of other satellites with limited spectral bands.

Keywords

• Advanced meteorological imager (AMI)
• data-to -data translation
• geostationary environment monitoring spectrometer (GEMS)
• hypothetical blue-green-red (RGB)
• satellite remote sensing