GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

Abstract

Abstract. The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleighcorrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τ G is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τ G has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τ A from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τ G − τ A ) is within −0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τ G are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products

“…Several studies have estimated aerosol properties by evaluating ocean surface reflectance using the Cox and Munk method [8,23,24,41,44]. In this study, ocean surface reflectance is calculated using A Linearized pseudo-spherical Vector Discrete Ordinate Radiative Transfer (VLIDORT) [45] ver.…”

“…However, over turbid water areas, aerosol properties are retrieved using the reflectance of band 1 and band 4, which are relatively less affected by aerosols [24].…”

“…In this study, two methods are used to retrieve surface reflectance: the MRM, which is used in the MODIS DB C5 algorithm [10] and GOCI-YAER [23,24], and the Estimated Surface Reflectance from SWIR (ESR), which estimates the surface reflectance in the visible range from TOA reflectance at SWIR, as in the MODIS DT [5,6] and MODIS DB C6 [11] algorithms.…”

“…Land surface reflectance was estimated from SWIR in addition to existing Minimum Reflectance Method (MRM), and ocean reflectance was from Fresnel equations with the consideration of chlorophyll-a concentration, as discussed in the next section. Here, AOPs were retrieved for February, May, August, and November to represent each season in 2016 using the YAER algorithm [22][23][24], and AOP results were compared with those calculated from AERONET sun-photometer data. The YAER algorithm should be preceded by cloud masking and surface reflectance estimation.…”

“…2018, 10, 699 9 of 25 as in Table 2, the remaining pixels were collected on a 6 km × 6 km grid (at sub-satellite point), where upper 40% and lower 20% of the 36 pixels were removed. The high and low-reflectance filtering was performed to obtain more reliable data by removing brighter and darker pixels affected by sub pixel cloud and cloud shadows [5,6,23,24]. were finished as in Table 2, the remaining pixels were collected on a 6 km × 6 km grid (at sub-satellite point), where upper 40% and lower 20% of the 36 pixels were removed.…”