Synergetic Use of MODIS Cloud Parameters for Distinguishing High Aerosol Loadings From Clouds Over the North China Plain

作者:Shang, HZ (Shang, Huazhe)[ 1,2 ] ; Chen, LF (Chen, Liangfu)[ 1 ] ; Tao, JH (Tao, Jinhua)[ 1 ] ; Su, L (Su, Lin)[ 1 ] ; Jia, SL (Jia, Songlin)[ 1,2 ]

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

卷: 7  期: 12  页: 4879-4886  特刊: SI

DOI: 10.1109/JSTARS.2014.2332427

出版年: DEC 2014

摘要

The Moderate Resolution Imaging Spectroradiometer (MODIS) standard cloud product is prone to misidentifying areas that are heavily polluted with aerosols as cloudy regions over the North China Plain (NCP) and to retrieving aerosol characteristics as cloud parameters. Based on the differences in physical and optical properties between aerosols and clouds, we propose a new approach to distinguish aerosol-laden areas from cloudy regions using MODIS level 2 cloud properties (e.g., cloud fraction, cloud phase, and cloud top pressure products). The approach was applied to 22 haze-fog cases that occurred in the 2011 and 2012 winters over the NCP. The aerosol identification results were then compared with MODIS-flagged aerosol areas, which were inferred from the noncloud obstruction flag and the suspended dust flag in the MODIS cloud mask product. The results indicated that approximately 60% of the MODIS-flagged aerosol areas were correctly identified using our approach. Among the analyzed cases, two cases exhibited substantial differences; the aerosol areas detected using the newly proposed method were approximately 2.5 times larger than that of the MODIS-flagged area. Further comparisons with aerosol distributions along the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) orbit for the two cases demonstrated that approximately 60%-80% of the CALIOP observed aerosols were identified using our method, while less than 10% of the CALIOP observed aerosols were consistent with the MODIS flagging.

通讯作者地址: Chen, LF (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China