Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine

doi:10.1109/JSTARS.2019.2963539

	Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine
	Liu, Xinkai 2; Zhai, Han 2; Shen, Yonglin 1,2; Lou, Benke 2; Jiang, Changmin 2; Li, Tianqi 2; Hussain, Sayed Bilal 2; Shen, Guoling 2
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2020
卷号	13 页码:414-427
关键词	Large-scale crop mapping harmonic analysis multisource feature set construction multisource remote sensing images prior constraints
ISSN号	1939-1404
DOI	10.1109/JSTARS.2019.2963539
通讯作者	Shen, Yonglin(shenyl@cug.edu.cn)
英文摘要	Large-scale crop mapping is vitally important to agriculrural monitoring and management. However, traditional methods cannot well meet the needs of large-scale applications. Therefore, this study proposed a method for large-scale crop mapping based on multisource remote sensing images. To be specific, 1) harmonic analysis was conducted on normalized difference vegetation index time-series derived from moderate resolution imaging spectroradiometer images and synthetic aperture radar backscattering coefficient time-series derived from Sentinel-1 data, respectively, extracting harmonic-derived phenological features and harmonic-derived backscattering features, and then combined with spectral features from Landsat-8 and Sentinel-2 images to construct the final multisource feature set for crop classification; 2) it employed prior constraints of crop dominance and cropland distribution to reduce misclassifications in large scale crop mapping; and 3) the whole process was conducted on the Google Earth Engine online platform, which can reduce the computational burdens caused by the spatiotemporal data. In the experimental study, we evaluated three crops, including wheat, rapeseed, and corn in Qinhai in 2018, based on the classification and regression tree classifier. The results show that the Jeffries-Matusita distances between crop samples are close to 2, and the overall accuracy is 84.25%. Furthermore, this study found that the distribution of the crops in Qinghai is associated with climate, topography, and cultivation habits.
资助项目	State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[2018004] ; NationalNatural Science Foundation of China[41501459] ; NationalNatural Science Foundation of China[41771380]
WOS关键词	TIME-SERIES ; PHENOLOGICAL CLASSIFICATION ; AGRICULTURAL CROPS ; FEATURE-SELECTION ; HARMONIC-ANALYSIS ; MODIS DATA ; SAR DATA ; UNCERTAINTY ; NDVI ; INFORMATION
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000526639900034
资助机构	State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; NationalNatural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/134025]
专题	中国科学院地理科学与资源研究所
通讯作者	Shen, Yonglin
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xinkai,Zhai, Han,Shen, Yonglin,et al. Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2020,13:414-427.
APA	Liu, Xinkai.,Zhai, Han.,Shen, Yonglin.,Lou, Benke.,Jiang, Changmin.,...&Shen, Guoling.(2020).Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,13,414-427.
MLA	Liu, Xinkai,et al."Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 13(2020):414-427.