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Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR

文献类型：期刊论文

作者Du, Lin1,2; Gong, Wei2,3; Shi, Shuo2,3; Yang, Pan2; Sun, Jia2; Zhu, Bo2; Song, Shalei4
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2016-02-01
卷号44页码:136-143
关键词Precision agriculture Hyperspectral LIDAR Support vector machine Wavelength selection Channel correction Nitrogen content
英文摘要Precision agriculture has become a global research hotspot in recent years. Thus, a technique for rapidly monitoring a farmland in a large scale and for accurately monitoring the growing status of crops needs to be established. In this paper, a novel technique, i.e., hyperspectral LIDAR (HL) which worked based on wide spectrum emission and a 32-channel detector was introduced, and its potential in vegetation detection was then evaluated. These spectra collected by HL were used to classify and derive the nitrogen contents of rice under four different nitrogen content levels with support vector machine (SVM) regression. Meanwhile the wavelength selection and channel correction method for achieving high spectral resolution were discussed briefly. The analysis results show that: (1) the reflectance intensity of the selected characteristic wavelengths of HL system has high correlation with different nitrogen contents levels of rice. (2) By increasing the number of wavelengths in calculation, the classification accuracy is greatly improved (from 54% with 4 wavelengths to 83% with 32 wavelengths) and so the regression coefficient r(2) is (from 0.51 with 4 wavelengths to 0.75 with 32 wavelengths). (3) Support vector machine (SVM) is a useful regression method for rice leaf nitrogen contents retrieval. These analysis results can help farmers to make fertilization strategies more accurately. The receiving channels and characteristic wavelengths of HL system can be flexibly selected according to different requirements and thus this system will be applied in other fields, such as geologic exploration and environmental monitoring. (C) 2015 Elsevier B.V. All rights reserved.
WOS标题词Science & Technology ; Technology
类目[WOS]Remote Sensing
研究领域[WOS]Remote Sensing
关键词[WOS]SUPERCONTINUUM LASER SOURCE ; SUPPORT VECTOR REGRESSION ; REMOTE-SENSING IMAGES ; VEGETATION INDEXES ; DECISION TREE ; CHLOROPHYLL CONTENT ; SCANNING DATA ; SENSED DATA ; AREA INDEX ; LAND-COVER
收录类别SCI
语种英语
WOS记录号WOS:000364891100014
公开日期2015-12-29
源URL[http://ir.wipm.ac.cn/handle/112942/9058]  
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.Wuhan Univ, Sch Phys & Technol, Wuhan 430072, Peoples R China
2.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
3.Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China
4.Chinese Acad Sci, Wuhan Inst Phys & Math, State Key Lab Magnet Resonance & Atom & Mol Phys, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Du, Lin,Gong, Wei,Shi, Shuo,et al. Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2016,44:136-143.
APA Du, Lin.,Gong, Wei.,Shi, Shuo.,Yang, Pan.,Sun, Jia.,...&Song, Shalei.(2016).Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,44,136-143.
MLA Du, Lin,et al."Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 44(2016):136-143.

入库方式： OAI收割

来源：武汉物理与数学研究所

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