Correcting MODIS aerosol optical depth products using a ridge regression model

ABSTRACT

Aerosol optical depth (AOD) is an important metric for the concentration of aerosols in the atmosphere. Dark target (DT) algorithm is a widely used physical model to retrieve AOD over land from Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, due to the limitation of surface ‘dark-target’ in some regions and over certain surface types, it does not work very well. In this paper, we propose two hybrid frameworks based on ridge regression (RR) to improve the retrieval accuracy. They are serial and parallel approaches. In both frameworks, the DT algorithm is used as a baseline to derive an initial result, and the bias between the derived AOD and the ground-truth is corrected by the RR model. To validate the effectiveness of the proposed methods, we apply them on 3093 collocated MODIS and Aerosol Robotic Network (AERONET) observations, covering 10 stations at all available time in China. The obtained results demonstrate that the proposed methods can improve retrieval performance compared to the corresponding DT algorithm and the RR model.

Acknowledgments

The authors would like to thank the Atmospheric Sciences Data Centre at NASA Goddard Space Flight Centre for help with the MODIS data, and the AERONET Principal Investigators for providing the ground-based aerosol data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the Natural Science Foundation of China under Grant Numbers: 61532009 and 41501377, and in part by the Natural Science Foundation of Jiangsu Province, China, under Grant 15KJA520001.