您好,欢迎访问北京市农林科学院 机构知识库!

Hyperspectral Estimation of Leaf Water Content for Winter Wheat Based on Grey Relational Analysis(GRA)

文献类型: 外文期刊

作者: Jin Xiu-liang 1 ; Xu Xin-gang 2 ; Wang Ji-hua 2 ; Li Xin-chuan 2 ; Wang Yan 1 ; Tan Chang-wei 1 ; Zhu Xin-kai 1 ; Guo Wen 1 ;

作者机构: 1.Yangzhou Univ, Minist Agr, Key Lab Crop Physiol Ecol & Cultivat Middle & Low, Key Lab Crop Genet & Physiol Jiangsu Prov, Yangzhou 225009, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词: Leaf water content;Grey relational analysis;Stepwise regression method;Partial least squares;Winter wheat;Water vegetation index

期刊名称:SPECTROSCOPY AND SPECTRAL ANALYSIS ( 影响因子:0.589; 五年影响因子:0.504 )

ISSN: 1000-0593

年卷期: 2012 年 32 卷 11 期

页码:

收录情况: SCI

摘要: The objective of the present study was to compare two methods for the precision of estimating leaf water content (LWC) in winter wheat by combining stepwise regression method and partial least squares (SRM-PLS) or PLS based on the relational degree of grey relational analysis (GRA) between water vegetation indexes (WVIs) and LWC. Firstly, data utilized to analyze the grey relationships between LWC and the selected typical WVIs were used to determine the sensitivity of different WVIs to LWC. Secondly, the two methods of estimating LWC in winter wheat were compared, one was to directly use PLS and the other was to combine SRM and PLS, and then the method with the highest determination coefficient (R-2) and lowest root mean square error (RMSE) was selected to estimate LWC in winter wheat. The results showed that the relationships between the first five WVI and LWC were stable by using GRA, and then LWC was estimated by using PLS and SRM-PLS at the whole stages with the R-2 and RMSEs being 0.605 and 0.575, 4.75% and 7.35%, respectively. The results indicated that the estimation accuracy of LWC could be improved by using GRA firstly and then by using PLS and SRM-PLS.

  • 相关文献

[1]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

[2]Using hyperspectral measurements to estimate ratio of leaf carbon to nitrogen in winter wheat. Xu, Xin-gang,Yang, Xiao-dong,Yang, Hao,Feng, Hai-kuai,Yang, Gui-jun,Song, Xiao-yu. 2014

[3]Grey Comprehensive Evaluation Model of Wheat Medium- and Low-yield Zoning via Remote Sensing Monitoring Data. Guo Wei,Zhao Chunjiang,Huang Wenjiang,Gu Xiaohe,Yang Xiaodong,Wang Huifang,Guo Wei,Guo Wei,Liu Bin. 2012

[4]Research on Error Reduction of Path Change of Liquid Samples Based on Near Infrared Trans-Reflective Spectra Measurement. Wang Ya-hong,Dong Da-ming,Zheng Wen-gang,Wang Wen-zhong,Wang Ya-hong,Zhou Ping,Ye Song,Wang Wen-zhong. 2014

[5]A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. Li, Jiangbo,Huang, Wenqian,Zhao, Chunjiang,Zhang, Baohua.

[6]Field monitoring of wheat seedling stage with hyperspectral imaging. Wu Qiong,Fang Jingjing,Ji Jianwei,Wang Cheng. 2016

[7]The Recognition of Biological Pesticide Adulteration by Attenuated Total Reflection Infrared Spectroscopy. Li Xiao-ting,Wand Dong,Ma Zhi-hong,Pan Li-gang,Wang Ji-hua,Li Xiao-ting,Wang Ji-hua,Zhu Da-Zhou.

[8]Simulation of Winter Wheat Phenology in Beijing Area with DSSAT-CERES Model. Haikuan Feng,Zhenhai Li,Peng He,Xiuliang Jin,Guijun Yang,Haiyang Yu,Fuqin Yang. 2016

[9]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

[10]Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem. Zhenhai Li,Chenwei Nie,Guijun Yang,Xingang Xu,Xiuliang Jin,Xiaohe Gu. 2014

[11]Monitoring the ratio of leaf carbon to nitrogen in winter wheat with hyperspectral measurements. Xin-gang Xu,Xiao-dong Yang,Xiao-he Gu,Hao Yang,Hai-kuan Feng,Gui-jun Yang,Xiao-yu,Song. 2015

[12]Study the Spatial-Temporal Variation of Wheat Growth Under Different Site-Specific Nitrogen Fertilization Approaches. Bei Cui,Wenjiang Huang,Xiaoyu Song,Huichun Ye,Yingying Dong. 2019

[13]EVALUATION OF ARABLE LAND YIELD POTENTIAL THROUGH REMOTE SENSING MONITORING. Song Xiaoyu,Gu Xiaohe,Wang Jihua,Chang Hong. 2014

[14]SPATIAL VARIABILITY OF WINTER WHEAT GROWTH BASED ON THE INDIVIDUAL INDEX AND THE POPULATION INDEX. Bei Cui,Xiaoyu Song,Wude Yang,Meichen Feng,Jihua Wang. 2014

[15]Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices. Xie, Qiaoyun,Huang, Wenjiang,Zhang, Bing,Dong, Yingying,Xie, Qiaoyun,Chen, Pengfei,Song, Xiaoyu,Pascucci, Simone,Pignatti, Stefano,Laneve, Giovanni. 2016

[16]Winter wheat biomass estimation based on canopy spectra. Zheng Ling,Zhu Dazhou,Zhang Baohua,Wang Cheng,Zhao Chunjiang,Zheng Ling,Liang Dong. 2015

[17]MONITORING WINTER WHEAT MATURITY BY HYPERSPECTRAL VEGETATION INDICES. Wang, Qian,Huang, Yuanfang,Wang, Qian,Li, Cunjun,Wang, Jihua,Song, Xiaoyu,Huang, Wenjiang. 2012

[18]Discrimination of yellow rust and powdery mildew in wheat at leaf level using spectral signatures. Yuan, Lin,Zhang, Jingcheng,Zhao, Jinling,Du, Shizhou,Huang, Wenjiang,Wang, Jihua. 2012

[19]SELECTION OF SPECTRAL CHANNELS FOR SATELLITE SENSORS IN MONITORING YELLOW RUST DISEASE OF WINTER WHEAT. Yuan, Lin,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Wang, Jihua. 2013

[20]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

作者其他论文 更多>>