EGU24-13808, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Aerosol Type Classification and Surface Reflectance Optimization for GOCI-II Aerosol Retrieval.

Jeewoo Lee1, Jhoon Kim1, and Seoyoung Lee2,3
Jeewoo Lee et al.
  • 1Yonsei University, Atmospheric Sciences, Seoul, Seodaemun-gu, Korea, Republic of (
  • 2University of Maryland, Baltimore County, Baltimore, MD, USA
  • 3NASA Goddard Space Flight Center, Greenbelt, MD, USA

Since its launch in 2020, the GOCI-II (Geostationary Ocean Color Imager-II) onboard the GEO-KOMPSAT-2B (GK-2B) satellite has provided aerosol products using the Yonsei aerosol retrieval (YAER) algorithm (Lee et al., 2023). The GOCI-II YAER algorithm retrieves aerosol optical depth (AOD) at 550 nm using an inversion algorithm with a precalculated look-up table (LUT) over UV to near-IR wavelengths. The surface reflectance database is collected using the Cox and Munk method (Cox and Munk, 1954) and the minimum reflectance technique (Hsu et al., 2004) over ocean and land, respectively. The minimum value of Lambertian Equivalent Reflectance (LER) of each wavelength is designated as the surface reflectance at each pixel. The 550 nm AOD is calculated by averaging the weighted AOD of two aerosol types that minimize the standard deviation among the six pre-assumed types.

In this study, we improved the performance of the GOCI-II YAER algorithm by renewing the surface reflectance database and the aerosol type selection phase. First, we validated the spectral AOD of the YAER algorithm to that of the AErosol RObotic NETwork (AERONET) to test the accuracy fluctuations between each wavelength. The wavelength with its AOD showing the highest consistency with that of AERONET was selected as the standard of the minimum reflectance composition. Second, aerosol type selection was modified to consider more information on the aerosol optical properties. As a result, the improved product showed better validation statistics when compared to AERONET AOD in terms of % within expected error (EE), the correlation coefficient, and the root mean squared error (RMSE). The improved GOCI-II aerosol products can help the air quality policymakers and broaden our knowledge of distribution of aerosols over Northeast Asia.

How to cite: Lee, J., Kim, J., and Lee, S.: Aerosol Type Classification and Surface Reflectance Optimization for GOCI-II Aerosol Retrieval., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13808,, 2024.