干旱区生态环境知识资源中心(Arid): A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations

Arid

DOI	10.5194/amt-13-2257-2020
	A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
	Wang, Chenxi 1,2; Platnick, Steven 2; Meyer, Kerry 2; Zhang, Zhibo 3; Zhou, Yaping 1,2
通讯作者	Wang, Chenxi
来源期刊	ATMOSPHERIC MEASUREMENT TECHNIQUES
ISSN	1867-1381
EISSN	1867-8548
出版年	2020
卷号	13 期号:5 页码:2257-2277
英文摘要	We trained two Random Forest (RF) machine learning models for cloud mask and cloud thermodynamic-phase detection using spectral observations from Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad-viewing zenith angle range, which is a great benefit to overall model performance. The allday model uses three VIIRS infrared (IR) bands (8.6, 11, and 12 mu m), and the daytime model uses five Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 mu m) together with the three IR bands to detect clear, liquid water, and ice cloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland, grassland, snow and ice, barren desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models was compared against collocated CALIOP products from 2017. It is shown that, when using a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison to the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top three algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, particularly for pixels over snow and ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.
类型	Article
语种	英语
国家	USA
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000531894100001
WOS关键词	NPP VIIRS ; PART I ; MODIS ; RETRIEVAL ; LAND ; VALIDATION ; PRODUCTS ; LIDAR ; VERIFICATION ; PERFORMANCE
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319079
作者单位	1.Univ Maryland Baltimore Cty, Joint Ctr Earth Syst Technol, Baltimore, MD 21228 USA; 2.NASA, Earth Sci Div, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; 3.Univ Maryland Baltimore Cty, Dept Phys, Baltimore, MD 21228 USA
推荐引用方式 GB/T 7714	Wang, Chenxi,Platnick, Steven,Meyer, Kerry,et al. A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations[J],2020,13(5):2257-2277.
APA	Wang, Chenxi,Platnick, Steven,Meyer, Kerry,Zhang, Zhibo,&Zhou, Yaping.(2020).A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations.ATMOSPHERIC MEASUREMENT TECHNIQUES,13(5),2257-2277.
MLA	Wang, Chenxi,et al."A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations".ATMOSPHERIC MEASUREMENT TECHNIQUES 13.5(2020):2257-2277.