干旱区生态环境知识资源中心(Arid): Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method

Arid

DOI	10.3390/rs11242931
	Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method
	Yao, Zhigang 1,2; Li, Jun 3; Zhao, Zengliang 1; Zhu, Lin 4; Qi, Jin 4; Che, Huizheng 5
通讯作者	Qi, Jin
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:24
英文摘要	Two back-propagation artificial neural network retrieval models have been developed for obtaining the dust aerosol optical depth (AOD) and dust-top height (DTH), respectively, from Atmospheric InfraRed Sounder (AIRS) brightness temperature (BT) measurements over Taklimakan Desert area. China Aerosol Remote Sensing Network (CARSNET) measurements at Tazhong station were used for dust AOD validation. Results show that the correlation coefficient of dust AODs between AIRS and CARSNET reaches 0.88 with a deviation of -0.21, which is the same correlation coefficient as the AIRS dust AOD and the Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product. In the AIRS DTH retrieval model, there is an option to include the collocated MODIS deep blue (DB) AOD as additional input for daytime retrieval; the independent dust heights from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used for AIRS DTH validation, and results show that the DTHs derived from the combined AIRS BT measurements and MODIS DB AOD product have better accuracy than those from AIRS BT measurements alone. The correlation coefficient of DTHs between AIRS and independent CALIOP dust heights is 0.79 with a standard deviation of 0.41 km when MODIS DB AOD product is included in the retrieval model. A series of case studies from different seasons were examined to demonstrate the feasibility of retrieving dust parameters from AIRS and potential applications. The method and approaches can be applied to process measurements from advanced infrared (IR) sounder and high-resolution imager onboard the same platform.
英文关键词	dust aerosol optical depth dust top height retrieval AIRS MODIS
类型	Article
语种	英语
国家	Peoples R China ; USA
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000507333400047
WOS关键词	INFRARED OPTICAL DEPTH ; ASIAN DUST ; AEROSOL PROPERTIES ; SYNERGISTIC USE ; MINERAL DUST ; DESERT DUST ; MODIS ; RETRIEVAL ; CHINA ; ALTITUDE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
EI主题词	2019-12-02
来源机构	中国科学院大气物理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/311491
作者单位	1.Beijing Inst Appl Meteorol, Beijing 100029, Peoples R China; 2.Chinese Acad Sci, Inst Atmospher Phys, LAGEO, Beijing 100029, Peoples R China; 3.Univ Wisconsin, CIMSS, Madison, WI 53706 USA; 4.Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China; 5.Chinese Acad Meteorol Sci, Key Lab Atmospher Chem LAC, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Yao, Zhigang,Li, Jun,Zhao, Zengliang,et al. Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method[J]. 中国科学院大气物理研究所,2019,11(24).
APA	Yao, Zhigang,Li, Jun,Zhao, Zengliang,Zhu, Lin,Qi, Jin,&Che, Huizheng.(2019).Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method.REMOTE SENSING,11(24).
MLA	Yao, Zhigang,et al."Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method".REMOTE SENSING 11.24(2019).