Spatio-temporal differences in cloud cover of Landsat-8 OLI observations across China during 2013-2016

doi:10.1007/s11442-018-1482-0

	Spatio-temporal differences in cloud cover of Landsat-8 OLI observations across China during 2013-2016
	Xiao, Chiwei 1,2; Li, Peng 1,2,3; Feng, Zhiming 1,2; Wu, Xingyuan 3
刊名	JOURNAL OF GEOGRAPHICAL SCIENCES
	2018-04-01
卷号	28 期号:4 页码:429-444
关键词	cloud cover (CC) spatio-temporal changes Landsat-8 OLI acquisition probability (AP) China
ISSN号	1009-637X
DOI	10.1007/s11442-018-1482-0
通讯作者	Li, Peng(lip@igsnrr.ac.cn)
英文摘要	Currently, the historical archive images of Landsat family sensors are probably the most effective data products for tracking global longitudinal changes since the 1970s. However, the issue of the degree and extent of cloud coverage is always a challenge and varies distinctively worldwide. So far, acquisition probability (AP) analyses of cloud cover (CC) of Landsat observations have been conducted with different sensors at regional scale. To our knowledge, CC probability analysis for the newly-launched Landsat-8 Operational Land Imager (OLI) across China is not reported. In this paper, monthly, seasonal, and annual APs for Landsat OLI (44,228 in total) images over China acquired from April 2013 to October 2016 with various CC thresholds were analyzed. The results showed that: first, the cumulative average APs of all OLI data over China at the CC thresholds ae30% was about 49.6% which illustrated the availability of OLI imagery across China. Second, the spatial patterns of 10%, 20%, and 30% CC thresholds of OLI observations, coincided well with the precipitation distributions separated by the respective 200 mm, 400 mm, and 800 mm isohyetal lines. Third, the APs of images with the 30% CC threshold are the highest in autumn and winter especially in October of 58.7%, while the corresponding lowest probability occurred in June of 41.0%. Finally, the spatial differences in APs of targeted images with ae30% CC thresholds were quite significant. At regional scales, the arid and semi-arid areas, Inland River and Songliao River basins, and northwestern side of the Hu Huanyong population line had the larger probabilities of obtaining high-quality images. Our study suggested that OLI imagery satisfy the data requirements needed for land surface monitoring, although there existed obvious spatio-temporal differences in APs over China at the 30% CC threshold.
资助项目	National Natural Science Foundation of China[41430861] ; National Key Research and Development Program of China[2016YFC0503500] ; Opening Fund of Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University[PK2016004]
WOS关键词	CONTERMINOUS UNITED-STATES ; LAND-COVER ; SHADOW DETECTION ; BORDER REGION ; IMAGERY ; PATTERNS ; IMPACTS ; AVAILABILITY ; SYSTEMS ; ETM
WOS研究方向	Physical Geography
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000426586300004
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China ; Opening Fund of Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/57068]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Peng
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang 330022, Jiangxi, Peoples R China
推荐引用方式 GB/T 7714	Xiao, Chiwei,Li, Peng,Feng, Zhiming,et al. Spatio-temporal differences in cloud cover of Landsat-8 OLI observations across China during 2013-2016[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2018,28(4):429-444.
APA	Xiao, Chiwei,Li, Peng,Feng, Zhiming,&Wu, Xingyuan.(2018).Spatio-temporal differences in cloud cover of Landsat-8 OLI observations across China during 2013-2016.JOURNAL OF GEOGRAPHICAL SCIENCES,28(4),429-444.
MLA	Xiao, Chiwei,et al."Spatio-temporal differences in cloud cover of Landsat-8 OLI observations across China during 2013-2016".JOURNAL OF GEOGRAPHICAL SCIENCES 28.4(2018):429-444.