干旱区生态环境知识资源中心(Arid): Hyperspectral Infrared Sounder Cloud Detection Using Deep Neural Network Model

Arid

DOI	10.1109/LGRS.2020.3023683
	Hyperspectral Infrared Sounder Cloud Detection Using Deep Neural Network Model
	Liu, Qian; Xu, Hui; Sha, Dexuan; Lee, Tsengdar; Duffy, Daniel Q.; Walter, Jeff; Yang, Chaowei
通讯作者	Yang, CW (corresponding author)，George Mason Univ, Dept Geog & Geoinformat Sci, Fairfax, VA 22030 USA.
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
EISSN	1558-0571
出版年	2022
卷号	19
英文摘要	Detection of cloud contaminated field of views (FOV) from satellite hyperspectral infrared sounders is essential for numerical weather prediction. A new cloud detection model is developed for the cross-track infrared sounder (CrIS) using the artificial deep neural network (DNN) technique. The truth cloud information used is from another instrument of Visible Infrared Imaging Radiometer Suite (VIIRS) deployed on the same platform of CrIS. The training data set is built from CrISx2013;VIIRS collocated measurements randomly selected from different months to represent different atmospheric and surface conditions. Then, we use the VIIRS cloud mask collocated within the CrIS footprint to train the CrIS spectra for cloud detection. Specifically, the CrIS spectra were transformed into their principal components (PCs), with only the top 75 PCs used as the predictors rather than the entire CrIS channels, for the purpose of better regression and convergence during the training process and faster prediction. Results were examined globally by the considered truth derived from the VIIRS cloud mask. Generally, the spatial distribution of the proposed CrIS cloud detection result agrees with that from the VIIRS, with a high model accuracy of 93x0025;. Further analysis indicates that the proposed CrIS cloud detection result is slightly better over daytime than nighttime with the accuracy values of 94x0025; versus 91x0025;. The ocean areas have a higher cloud detection accuracy than continental land with accuracy values of 95x0025; versus 88x0025;. In addition, sometimes the DNN model would recognize the thin cloud as clear sky, as their spectra are very similar to each other. False detected pixels are also found over snow- or ice-covered and desert areas. This is possibly due to the VIIRS cloud mask that has a relatively low accuracy over these areas.
英文关键词	Clouds Atmospheric modeling Atmospheric measurements Hyperspectral imaging Numerical models Pollution measurement Cloud detection cross-track infrared sounder (CrIS) deep neural network (DNN) Geo artificial intelligence (GeoAI) hyperspectral infrared sounder
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000735565000020
WOS关键词	ALGORITHM ; NPP
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376791
作者单位	[Liu, Qian; Sha, Dexuan; Yang, Chaowei] George Mason Univ, Dept Geog & Geoinformat Sci, Fairfax, VA 22030 USA; [Xu, Hui] Univ Maryland, Earth Syst Sci Interdisciplinary Res Ctr, College Pk, MD 20740 USA; [Lee, Tsengdar] NASA Headquarters, Washington, DC 20546 USA; [Duffy, Daniel Q.] NASA, Ctr Climate Simulat, Greenbelt, MD 20771 USA; [Walter, Jeff] NASA, Langley Res Ctr, Atmospher Sci Data Ctr ASDC, Hampton, VA 23666 USA
推荐引用方式 GB/T 7714	Liu, Qian,Xu, Hui,Sha, Dexuan,et al. Hyperspectral Infrared Sounder Cloud Detection Using Deep Neural Network Model[J],2022,19.
APA	Liu, Qian.,Xu, Hui.,Sha, Dexuan.,Lee, Tsengdar.,Duffy, Daniel Q..,...&Yang, Chaowei.(2022).Hyperspectral Infrared Sounder Cloud Detection Using Deep Neural Network Model.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19.
MLA	Liu, Qian,et al."Hyperspectral Infrared Sounder Cloud Detection Using Deep Neural Network Model".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022).