Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data

doi:10.1109/JSTARS.2018.2855564

	Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data
	Pan, Haizhu 1,2; Chen, Zhongxin 1,2; Ren, Jianqiang 1,2; Li, He 3; Wu, Shangrong 1,2
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2019-02-01
卷号	12 期号:2 页码:482-492
关键词	Canopy water content (CWC) leaf area index (LAI) lookup table (LUT) neural network (NN) North China vegetation indices Sentinel-2 winter wheat
ISSN号	1939-1404
DOI	10.1109/JSTARS.2018.2855564
通讯作者	Chen, Zhongxin(chenzhongxin@caas.cn)
英文摘要	Leaf area index (LAI) and canopy water content (CWC) are important variables for monitoring crop growth and drought, which can be estimated from remotely sensed data. The goal of this study was to evaluate the suitability of the Sentinel-2 multispectral instrument (S2 MSI) data for winter wheat LAI and CWC estimation with three different inversion approaches in the main farming region in North China. During the winter wheat key growth stages in 2017, 22 fields, each with five independent samples, the total number of sample plot is 110, were designed for experimental measurements. In this study, the LAI and CWC were retrieved separately using empirical models through different spectral indices, neural network (NN) algorithms, and lookup table (LUT) methods based on the PROSAIL model. The accuracies of the estimated LAI and CWC were assessed through in situ measurements. The results show that the LUT inversion approach was more suitable for LAI and CWC estimation than the spectral index-based empirical model or the NN algorithm. With the LUT approach, LAI was obtained with a root mean square error (RMSE) of 0.43m(2).m(-2) and a relative RMSE (RRMSE) of 11% using seven S2MSI bands, and CWC was obtained with an RMSE of 0.41 kg.m(-2), and an RRMSE of 32% using five S2 MSI bands. In all the three methods, S2MSI was sensitive to LAI variation and able to reach higher accuracies when red edge bands were used. However, CWC inversion was still a challenge using S2 MSI data.
资助项目	National Natural Science Foundation of China[NSFC-6166113606] ; National Natural Science Foundation of China[41471364] ; China Ministry of Agriculture Introduction of International Advanced Agricultural Science and Technology Program (948 Program) project[2016-X38] ; China Scholar Council[201703250080]
WOS关键词	HYPERSPECTRAL VEGETATION INDEXES ; FUEL MOISTURE-CONTENT ; RED-EDGE BANDS ; BIOPHYSICAL VARIABLES ; REMOTE ESTIMATION ; GREEN LAI ; CHLOROPHYLL ; CROP ; REFLECTANCE ; INVERSION
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:000460663600009
资助机构	National Natural Science Foundation of China ; China Ministry of Agriculture Introduction of International Advanced Agricultural Science and Technology Program (948 Program) project ; China Scholar Council
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/49207]
专题	中国科学院地理科学与资源研究所
通讯作者	Chen, Zhongxin
作者单位	1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China 2.Minist Agr, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Pan, Haizhu,Chen, Zhongxin,Ren, Jianqiang,et al. Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2019,12(2):482-492.
APA	Pan, Haizhu,Chen, Zhongxin,Ren, Jianqiang,Li, He,&Wu, Shangrong.(2019).Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,12(2),482-492.
MLA	Pan, Haizhu,et al."Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 12.2(2019):482-492.