NDLI: Correlation between PM concentrations and Aerosol Optical Depth in eastern China based on BP neural networks

Content Provider	IEEE Xplore Digital Library
Author	Yerong Wu Jianping Guo Xin Zhang Xiaowen Li
Copyright Year	2011
Description	Author affiliation: Chinese Academy of Meteorological Sciences, Beijing 100081, China (Jianping Guo) \|\| State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications of Chinese Academy of Sciences, 100875, China (Yerong Wu; Xin Zhang; Xiaowen Li)
Abstract	Two years (2007–2008) of observational particulate matter (PM) data from ten ground-stations over eastern China, as well as collocated Aerosol Optical Depth (hereafter called AOD) derived from Moderate Resolution Imaging Spectroradiometer (MODIS), in conjunction with meteorological parameters, such as planetary boundary layer height (PBLH), relative humid (RH), temperature (TEMP), wind speed (WS), wind direction (WD), were applied to investigate the correlation between PM and AOD with the back propagation (BP) neural networks algorithm, notably the PM-AOD correlation change with seasons. The PM2.5 concentrations retrieved from AOD/MODIS data based on BP neural networks presents significant change with seasons. Albeit the sensitivity to seasons of PM2.5 retrieval from satellite, results indicated that the algorithm of BP network could be applied in retrieving long term PM concentrations from satellite.
Starting Page	3308
Ending Page	3311
File Size	369047
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049927
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Aerosols Artificial neural networks Correlation Satellites MODIS Atmospheric modeling Atmospheric measurements PBLH PM BP Neural Networks
Content Type	Text
Resource Type	Article

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