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Correcting MODIS aerosol optical depth products using a ridge regression model

Author:
Hang, Renlong, Liu, Qingshan, Xia, Guiyu, Song, Huihui
Source:
International journal of remote sensing 2018 v.39 no.10 pp. 3275-3286
ISSN:
1366-5901
Subject:
aerosols, algorithms, governmental programs and projects, models, moderate resolution imaging spectroradiometer, regression analysis, remote sensing, China
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.
Agid:
5957577