您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Comparison of Four Chemometric Techniques for Estimating Leaf Nitrogen Concentrations in Winter Wheat (Triticum Aestivum) Based on Hyperspectral Features

文献类型： 外文期刊

作者： Li, Zh. 1 ; Nie, Ch. 2 ; Wei, Ch. 1 ; Xu, X. 2 ; Song, X. 2 ; Wang, J. 1 ;

作者机构： 1.Zhejiang Univ, Inst Agr Remote Sensing & Informat Applicat, Hangzhou 310003, Zhejiang, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Minist Agr, Key Lab Agriinformat, Beijing, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Agr Stand & Testing, 11 Shuguang Huayuan Mid Rd, Beijing 100097, Peoples R China

关键词： hyperspectral remote sensing;stepwise multiple linear regression;partial least squares regression;artificial neural network;support vector machines

期刊名称：JOURNAL OF APPLIED SPECTROSCOPY （ 影响因子：0.741； 五年影响因子：0.718 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Four chemometric techniques for estimating LNC in winter wheat were compared by spectral features. The predictive power and impact of sample size were evaluated. Key results include: (1) partial least squares regression (PLSR) and support vector machines regression (SVR) performed better than the other two methods, with coefficient of determination (r (2)) values in the calibration set of 0.82 and 0.81 and the normalized root mean square error (NRMSE) values in the validation set of 5.48 and 5.94%, respectively; (2) the lowest accuracy was achieved using stepwise multiple linear regression (SMLR), with r (2) and NRMSE values of 0.78 and 6.52%, respectively; (3) the predictive power of the back propagation neural network (BPN) was enhanced as sample size increased. Sample size less than 80 is not recommended when using BPN. These results suggest that PLSR and SVR are preferred choices to estimate LNC in winter wheat, and BPN is recommended when a sufficient sample size is available.

  • 相关文献

[1]Comparative analysis of three regression methods for the winter wheat biomass estimation using hyperspectral measurements. Xingang Xu,Yuanyuan Fu,Guijun Yang,Haikuan Feng,Xiaoyu Song,Jihua Wang. 2013

[2]CHARACTERIZATION OF POWDERY MILDEW IN WINTER WHEAT USING MULTI-ANGULAR HYPERSPECTRAL MEASUREMENTS. Jinling Zhao,Lin Yuan,Linsheng Huang,Dongyan Zhang,Jingcheng Zhang,Xiaohe Gu. 2013

[3]Identifying Leaf-Scale Wheat Aphids Using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer. Jinling Zhao,Dongyan Zhang,Juhua Luo,Dacheng Wang,Wenjiang Huang. 2012

[4]Leaf nitrogen spectral reflectance model of winter wheat (Triticum aestivum) based on PROSPECT: simulation and inversion. Yang, Guijun,Zhao, Chunjiang,Sun, Chenhong,Yang, Guijun,Zhao, Chunjiang,Feng, Haikuan,Li, Zhenhai,Li, Heli,Pu, Ruiliang. 2015

[5]Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression. Li Cun-jun,Wang Ji-hua,Wang Qian,Wang Da-cheng,Song Xiao-yu,Wang Yan,Huang Wen-jiang,Li Cun-jun,Wang Ji-hua,Huang Wen-jiang. 2012

[6]Estimation of leaf chlorophyll content in winter wheat using variable importance for projection (VIP) with hyperspectral data. He, Peng,Xu, Xingang,Li, Zhenhai,Feng, Haikuan,Yang, Guijun,Zhang, Yongfeng,He, Peng,Xu, Xingang,Li, Zhenhai,Feng, Haikuan,Yang, Guijun,Zhang, Yongfeng,He, Peng,He, Peng,Zhang, Baolei. 2015

[7]Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Yue, Jibo,Yang, Guijun,Li, Zhenhai,Wang, Yanjie,Feng, Haikuan,Xu, Bo,Yue, Jibo,Yue, Jibo,Li, Changchun,Wang, Yanjie,Yang, Guijun,Li, Zhenhai,Wang, Yanjie,Feng, Haikuan,Xu, Bo,Yang, Guijun,Li, Zhenhai,Xu, Bo. 2017

[8]Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image. Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Wang, Jihua,Zhang, Jingcheng,Wang, Jihua,Zhang, Jingcheng,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Wang, Jihua,Shi, Yeyin. 2014

[9]Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area. Yang, Guijun,Yang, Guijun,Yang, Guijun,Zhao, Chunjiang,Huang, Wenjiang,Wang, Jihua,Pu, Ruiliang.

[10]Hyperspectral Discrimination and Response Characteristics of Stressed Rice Leaves Caused by Rice Leaf Folder. Liu, Zhanyu,Ding, Xiaodong,Zhou, Bin,Liu, Zhanyu,Cheng, Jia-an,Huang, Wenjiang,Li, Cunjun,Xu, Xingang,Shi, Jingjing. 2012

[11]Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat. Xie, Qiaoyun,Huang, Wenjiang,Liang, Dong,Huang, Linsheng,Zhang, Dongyan,Chen, Pengfei,Wu, Chaoyang,Yang, Guijun,Zhang, Jingcheng. 2014

[12]Identifying Leaf-Scale Wheat Aphids Using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer. Zhao, Jinling,Zhang, Dongyan,Luo, Juhua,Wang, Dacheng,Huang, Wenjiang. 2012

[13]Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index from in situ and simulated hyperspectral data. Kong, Weiping,Huang, Wenjiang,Zhou, Xianfeng,Kong, Weiping,Zhou, Xianfeng,Song, Xiaoyu,Casa, Raffaele. 2016

[14]Associating new spectral features from visible and near infrared regions with optimal combination principle to monitor leaf nitrogen concentration in barley. Xu Xin-Gang,Zhao Chun-Jiang,Wang Ji-Hua,Li Cun-Jun,Yang Xiao-Dong. 2013

[15]New Vegetation Index Fusing Visible-Infrared and Shortwave Infrared Spectral Feature for Winter Wheat LAI Retrieval. Li Xin-chuan,Xu Xin-gang,Jin Xiu-liang,Zhang Jing-cheng,Song Xiao-yu,Li Xin-chuan,Xu Xin-gang,Jin Xiu-liang,Zhang Jing-cheng,Song Xiao-yu,Li Xin-chuan,Bao Yan-song. 2013

[16]Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Yuan, Huanhuan,Yang, Guijun,Wang, Yanjie,Liu, Jiangang,Yu, Haiyang,Feng, Haikuan,Xu, Bo,Zhao, Xiaoqing,Yang, Xiaodong,Yuan, Huanhuan,Li, Changchun,Wang, Yanjie,Yuan, Huanhuan,Yang, Guijun,Liu, Jiangang,Feng, Haikuan,Yang, Xiaodong,Yang, Guijun,Yu, Haiyang,Xu, Bo,Zhao, Xiaoqing,Yang, Xiaodong. 2017

[17]A Method to Reconstruct the Solar-Induced Canopy Fluorescence Spectrum from Hyperspectral Measurements. Zhao, Feng,Guo, Yiqing,Verhoef, Wout,Gu, Xingfa,Liu, Liangyun,Yang, Guijun. 2014

[18]Band Depth Analysis and Partial Least Square Regression Based Winter Wheat Biomass Estimation Using Hyperspectral Measurements. Fu Yuan-yuan,Wang Ji-hua,Fu Yuan-yuan,Wang Ji-hua,Yang Gui-jun,Song Xiao-yu,Xu Xin-gang,Feng Hai-kuan,Fu Yuan-yuan,Wang Ji-hua,Yang Gui-jun,Song Xiao-yu,Xu Xin-gang,Feng Hai-kuan. 2013

[19]The Study of Winter Wheat Biomass Estimation Model Based on Hyperspectral Remote Sensing. Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Meng, Lumin. 2016

[20]CHARACTERIZATION OF POWDERY MILDEW IN WINTER WHEAT USING MULTI-ANGULAR HYPERSPECTRAL MEASUREMENTS. Zhao, Jinling,Yuan, Lin,Zhang, Dongyan,Zhang, Jingcheng,Gu, Xiaohe,Huang, Linsheng,Zhang, Dongyan. 2013

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台