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Proceedings Paper

The estimation of Aerosol Optical Depth in eastern China based on regression analysis
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Paper 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 AquaAOD 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 AODTerra and AODAqua 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).

Paper Details

Date Published: 4 September 2015
PDF: 9 pages
Proc. SPIE 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII, 96101B (4 September 2015); doi: 10.1117/12.2187634
Show Author Affiliations
Jing Wang, East China Normal Univ. (China)
Institute of Remote Sensing and Digital Earth (China)
Runhe Shi, East China Normal Univ. (China)
Institute of Remote Sensing and Digital Earth (China)
Chaoshun Liu, East China Normal Univ. (China)
Institute of Remote Sensing and Digital Earth (China)
Cong Zhou, East China Normal Univ. (China)
Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 9610:
Remote Sensing and Modeling of Ecosystems for Sustainability XII
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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