Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

doi:10.7717/peerj.5431

	Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data
	Hao, Pengyu 1,2,3; Tang, Huajun 1; Chen, Zhongxin 1; Liu, Zhengjia 4
刊名	PEERJ
	2018-08-31
卷号	6 页码:30
关键词	Classification Cotton Maize Early-season Wheat Hengshui Sentinel Image time series Improved artificial immune network
ISSN号	2167-8359
DOI	10.7717/peerj.5431
通讯作者	Hao, Pengyu(haopy8296@163.com) ; Tang, Huajun(tanghuajun@caas.cn)
英文摘要	Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries-Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer's accuracy (PA) and user's accuracy (UA) values were higher than 95% when an April 1-May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1-August 15. As spring maize and cotton mature in late August and September-October, respectively, these two crops can be accurately mapped 4-6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.
资助项目	China Postdoctoral Science Foundation[BX201700286] ; National Natural Science Foundation of China[NSFC-61661136006] ; China Ministry of Agriculture Introduction of International Advanced Agricultural Science and Technology Program (948 Program)[2016-X38]
WOS关键词	TIME-SERIES ; VEGETATION INDEX ; LAND-COVER ; FEATURE-SELECTION ; GREAT-PLAINS ; MODIS DATA ; CLASSIFICATION ; CHINA ; PHENOLOGY ; AREA
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	PEERJ INC
WOS记录号	WOS:000443318400002
资助机构	China Postdoctoral Science Foundation ; National Natural Science Foundation of China ; China Ministry of Agriculture Introduction of International Advanced Agricultural Science and Technology Program (948 Program)
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/54332]
专题	中国科学院地理科学与资源研究所
通讯作者	Hao, Pengyu; Tang, Huajun
作者单位	1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Beijing, Peoples R China 2.Shenzhen Univ, Natl Adm Surveying Mapping & GeoInformat, Key Lab Geoenvironm Monitoring Coastal Zone, Shenzhen, Peoples R China 3.Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Hao, Pengyu,Tang, Huajun,Chen, Zhongxin,et al. Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data[J]. PEERJ,2018,6:30.
APA	Hao, Pengyu,Tang, Huajun,Chen, Zhongxin,&Liu, Zhengjia.(2018).Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data.PEERJ,6,30.
MLA	Hao, Pengyu,et al."Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data".PEERJ 6(2018):30.