Preprints
https://doi.org/10.5194/amt-2023-204
https://doi.org/10.5194/amt-2023-204
22 Nov 2023
 | 22 Nov 2023
Status: this preprint is currently under review for the journal AMT.

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

Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen

Abstract. 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.

Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-204', Anonymous Referee #3, 13 Dec 2023
  • RC2: 'Comment on amt-2023-204', Anonymous Referee #1, 25 Dec 2023
Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen
Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen

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Short summary
This study explores the problems of surface reflectance estimation from previous MISR satellite remote sensing images and develops an error correction model to invert and obtain a higher precision AOD product. High-accuracy AOD is important not only for the daily monitoring of air pollution, but also for the study of energy exchange between land and atmosphere. The study will help to further improve the retrieval accuracy of multi-angle AOD on large spatial scales and long time series.