Estimation of Directional Surface Reflectance and Atmospheric Aerosols Over East Asia Using a Multi-Channel Geostationary Satellite

• Main Authors: Lee, Kwon-Ho, Yoo, Jung-Moon, Wong, Man-Sing
• Format: Conference Proceeding
• Language: English
• Subjects: Aerosol; Aerosols; AHI; Land surface; MODIS; radiative transfer; Reflection; Reflectivity; Remote sensing; satellite; Sea surface
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS39084.2020.9323592

Earth-observing data from the recent geostationary orbit satellites can be useful to retrieve the surface reflectance as well as aerosol properties including aerosol optical thickness (AOT). Implementing the algorithm to determine the geometry dependent surface reflectance based on the atmospheric correction with pre-defined atmospheric transmission function is an effective way to determine AOT. This study presents framework to integrate surface reflectance and aerosol retrieval processes by combining with the atmospheric correction, minimum reflection composite, and look-up table (LUT) application for the Advanced Himawari Imager (AHI) onboard Himawari-8 data. We discuss the algorithm's performance by applying test period datasets including a clear sky as well as anomalously high AOT values during the air pollution events. We also described results from a comparison with the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) operational products. Continuous observation data were used to derive surface reflectance at a given time and this geometry dependent surface reflectance were successfully applied to aerosol retrieval process. There were good agreements between retrieved AOT with sunphotometer-derived and MODIS AOTs, with a linear correlation coefficient (r) of 0.78∼0.83 and mean bias of 0.07. These results suggest that the proposed method applied to the AHI data can accurately estimate continuous surface and aerosol properties. Moreover, aerosol retrieval results from the geostationary satellite are promising for monitoring air quality with its enhanced spatial and temporal resolution.