The estimation of Aerosol Optical Depth in eastern China based on regression analysis

Jing Wang; Runhe Shi; Chaoshun Liu; Cong Zhou

doi:10.1117/12.2187634

4 September 2015 The estimation of Aerosol Optical Depth in eastern China based on regression analysis

Jing Wang, Runhe Shi, Chaoshun Liu, Cong Zhou

Author Affiliations +

Proceedings Volume 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII; 96101B (2015) https://doi.org/10.1117/12.2187634
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

The atmospheric pollution and air quality issues are getting worse in China, the formation mechanism of aerosols and their environment effects attracted more and more attention. Aerosol Optical Depth (AOD) is one of the most important parameters which can indicate the atmospheric turbidity and aerosol load. High-quality AOD data are significant for the study in the atmospheric environment (i.e., air quality). This paper used MODIS/Terra AOD in 2008 to improve the coverage of MODIS/Aqua AOD, which was based on linear regression analysis model. RMSE between estimation value and Aqua_AOD detected through satellite is 0.132. The average value of test data was 0.812. The average of regression result was 0.807. It showed that the regression model between AOD_Terra and AOD_Aqua worked well. Also, we built two sets of estimation models (MODIS AOD and OMI AOD) through stepwise regression analysis model. One is using OMI AOD and meteorological elements to estimate MODIS AOD. The value of RMSE was 0.113, which represents 13.916% of the average(R²=0.782). The other one is using MODIS AOD and meteorological elements to estimate OMI AOD. RMSE of the model is 0.132, which represents 18.182% of the average (R²=0.726).

Citation Download Citation

Jing Wang, Jing Wang, Runhe Shi, Runhe Shi, Chaoshun Liu, Chaoshun Liu, Cong Zhou, Cong Zhou, } "The estimation of Aerosol Optical Depth in eastern China based on regression analysis", Proc. SPIE 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII, 96101B (4 September 2015); doi: 10.1117/12.2187634; https://doi.org/10.1117/12.2187634

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