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Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance
Sun, Jia1; Yang, Jian2; Shi, Shuo1,3; Chen, Biwu1; Du, Lin2; Gong, Wei1,3; Song, Shalei4
2017-09-01
Source PublicationREMOTE SENSING
Volume9Issue:9
SubtypeArticle
AbstractNitrogen (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.
KeywordLeaf Nitrogen Concentration Hyperspectral Lidar Multispectral Lidar Regression Machine Learning
WOS HeadingsScience & Technology ; Technology
Funding OrganizationNational 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)
DOI10.3390/rs9090951
WOS KeywordSUPPORT VECTOR REGRESSION ; HYPERSPECTRAL LIDAR ; NEURAL-NETWORKS ; PADDY RICE ; CHLOROPHYLL CONTENT ; SQUARES REGRESSION ; VEGETATION INDEXES ; LINEAR-REGRESSION ; CROP ; INVERSION
Indexed BySCI
Language英语
Funding OrganizationNational 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)
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000414138700081
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.apm.ac.cn/handle/112942/11557
Collection高技术创新与发展中心
Affiliation1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China
2.China Univ Geosci, Fac Informat Engn, Wuhan 430074, Hubei, Peoples R China
3.Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China
4.Chinese Acad Sci, Wuhan Inst Phys & Math, Wuhan 430071, Hubei, Peoples R China
Recommended Citation
GB/T 7714
Sun, Jia,Yang, Jian,Shi, Shuo,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.,Yang, Jian.,Shi, Shuo.,Chen, Biwu.,Du, Lin.,...&Song, Shalei.(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).
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