IGSNRR OpenIR
Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data
Hao, Pengyu1,2,3; Tang, Huajun1; Chen, Zhongxin1; Liu, Zhengjia4
2018-08-31
Source PublicationPEERJ
ISSN2167-8359
Volume6Pages:30
Corresponding AuthorHao, Pengyu(haopy8296@163.com) ; Tang, Huajun(tanghuajun@caas.cn)
AbstractSubstantial 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.
KeywordClassification Cotton Maize Early-season Wheat Hengshui Sentinel Image time series Improved artificial immune network
DOI10.7717/peerj.5431
WOS KeywordTIME-SERIES ; VEGETATION INDEX ; LAND-COVER ; FEATURE-SELECTION ; GREAT-PLAINS ; MODIS DATA ; CLASSIFICATION ; CHINA ; PHENOLOGY ; AREA
Indexed BySCI
Language英语
Funding ProjectChina 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]
Funding OrganizationChina Postdoctoral Science Foundation ; National Natural Science Foundation of China ; China Ministry of Agriculture Introduction of International Advanced Agricultural Science and Technology Program (948 Program)
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000443318400002
PublisherPEERJ INC
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54333
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHao, Pengyu; Tang, Huajun
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Hao, Pengyu]'s Articles
[Tang, Huajun]'s Articles
[Chen, Zhongxin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Hao, Pengyu]'s Articles
[Tang, Huajun]'s Articles
[Chen, Zhongxin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Hao, Pengyu]'s Articles
[Tang, Huajun]'s Articles
[Chen, Zhongxin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.