Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance

doi:10.3390/rs9090951

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	Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance
	Sun, Jia 2; Shi, Shuo 2,4; Chen, Biwu 2; Du, Lin 3; Song, Shalei 1; Gong, Wei 2,4; Yang, Jian 3
刊名	REMOTE SENSING
	2017-09-01
卷号	9 期号:9
关键词	Leaf Nitrogen Concentration Hyperspectral Lidar Multispectral Lidar Regression Machine Learning
DOI	10.3390/rs9090951
文献子类	Article
英文摘要	Nitrogen (N) is important for the growth of crops. Estimating leaf nitrogen concentration (LNC) accurately and nondestructively is important for precision agriculture, reduces environmental pollution, and helps model global carbon and N cycles. Leaf reflectance, especially in the visible and near-infrared regions, has been identified as a useful indicator of LNC. Except reflectance passively acquired by spectrometers, the newly developed multispectral LiDAR and hyperspectral LiDAR provide possibilities for measuring leaf spectra actively. The regression relationship between leaf reflectance spectra and rice (Oryza sativa) LNC relies greatly on the algorithm adopted. It would be preferable to find one algorithm that performs well with respect to passive and active leaf spectra. Thus, this study assesses the influence of six popular linear and nonlinear methods on rice LNC retrieval, namely, partial least-square regression, least squares boosting, bagging, random forest, back-propagation neural network (BPNN), and support vector regression of different types/kernels/parameter values. The R-2, root mean square error and relative error in rice LNC estimation using these different methods were compared through the passive and active spectral measurements of rice leaves of different varieties at different locations and time (Yongyou 4949, Suizhou, 2014, Yangliangyou 6, Wuhan, 2015). Results demonstrate that BPNN provided generally satisfactory performance in estimating rice LNC using the three kinds of passive and active reflectance spectra.
WOS关键词	SUPPORT VECTOR REGRESSION ; HYPERSPECTRAL LIDAR ; NEURAL-NETWORKS ; PADDY RICE ; CHLOROPHYLL CONTENT ; SQUARES REGRESSION ; VEGETATION INDEXES ; LINEAR-REGRESSION ; CROP ; INVERSION
WOS研究方向	Remote Sensing
语种	英语
WOS记录号	WOS:000414138700081
资助机构	National Natural Science Foundation of China(41601360 ; National Natural Science Foundation of China(41601360 ; Wuhan Morning Light Plan of Youth Science and Technology(2017050304010308) ; Wuhan Morning Light Plan of Youth Science and Technology(2017050304010308) ; Natural Science Foundation of Hubei Province(2015CFA002) ; Natural Science Foundation of Hubei Province(2015CFA002) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)(CUG170661) ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)(CUG170661) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; 41611130114) ; 41611130114) ; National Natural Science Foundation of China(41601360 ; National Natural Science Foundation of China(41601360 ; Wuhan Morning Light Plan of Youth Science and Technology(2017050304010308) ; Wuhan Morning Light Plan of Youth Science and Technology(2017050304010308) ; Natural Science Foundation of Hubei Province(2015CFA002) ; Natural Science Foundation of Hubei Province(2015CFA002) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Fundamental Research Funds for the Central Universities(2042016kf0008) ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)(CUG170661) ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)(CUG170661) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(15R01) ; 41611130114) ; 41611130114)
内容类型	期刊论文
源URL	[http://ir.wipm.ac.cn/handle/112942/11557]
专题	武汉物理与数学研究所_高技术创新与发展中心
作者单位	1.Chinese Acad Sci, Wuhan Inst Phys & Math, Wuhan 430071, Hubei, Peoples R China 2.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China 3.China Univ Geosci, Fac Informat Engn, Wuhan 430074, Hubei, Peoples R China 4.Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China
推荐引用方式 GB/T 7714	Sun, Jia,Shi, Shuo,Chen, Biwu,et al. Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance[J]. REMOTE SENSING,2017,9(9).
APA	Sun, Jia.,Shi, Shuo.,Chen, Biwu.,Du, Lin.,Song, Shalei.,...&Yang, Jian.(2017).Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance.REMOTE SENSING,9(9).
MLA	Sun, Jia,et al."Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance".REMOTE SENSING 9.9(2017).