Multi-angle aerosol optical depth retrieval method based on improved surface reflectance

• Main Authors: Chen, Lijuan, Wang, Ren, Fei, Ying, Fang, Peng, Zha, Yong, Chen, Haishan
• Format: Text
• Language: English
• Published: 2023
• Subjects: Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/amt-2023-204; https://amt.copernicus.org/preprints/amt-2023-204/

Retrieval of terrestrial aerosol optical depth (AOD) has been a challenge for satellite Earth observations, mainly due to the difficulty of estimating surface reflectance caused by land-atmosphere coupling. Current satellite AOD retrieval products have low spatial resolution under complex surface processes. In this study, based on our previous studies of AOD retrieval, we further improved the estimation method of surface reflectance by establishing an error correction model and then obtained a more accurate AOD. A lookup table is constructed using the Second Simulation of Satellite Signal in the Solar Spectrum (6S) to obtain high-precision retrieval of AOD. The retrieval accuracy of the algorithm is verified by AERONET (Aerosol Robotic Network) observations. The results indicate that the retrieved AOD based on the improved method of this study has advantages in fewer missing AOD pixels and finer spatial resolution, as compared to the MODIS AOD product and our previous estimation method. Among the nine MISR angles, the optimal correlation coefficient (R) of retrieved AOD and observed AOD can reach 0.89. Root mean square error (RMSE) and relative mean bias (RMB) can reach a minimum values of 0.20 and 0.32, respectively. This study will help to further improve the accuracy of retrieving multi-angle AOD at large spatial scales and long time series.