北京市农林科学院机构知识库

Monitoring the ratio of leaf carbon to nitrogen in winter wheat with hyperspectral measurements

收藏
分享
全文链接

文献类型：外文期刊

作者： Xu, Xin-gang ¹ ; Yang, Xiao-dong ¹ ; Gu, Xiao-he ¹ ; Yang, Hao ¹ ; Feng, Hai-kuan ¹ ; Yang, Gui-jun ¹ ; Song, Xiao-yu ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： leaf C/N;spectral index;winter wheat;gray relational analysis;branch-and-bound method

期刊名称：REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XVII

ISSN： 0277-786X

年卷期： 2015 年 9637 卷

页码：

收录情况： SCI

摘要： In crop leaves the ratio of carbon to nitrogen (C/N), defined as ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), is a good indicator that can synthetically evaluate the balance of carbon and nitrogen, nutrient status in crop plants. Hence it is very important how to monitor changes of leaf C/N effectively and in real time for nutrient diagnosis and growing management of crops in fields. In consideration of the close relationships between chlorophyll, nitrogen (N) and C/N, some typical indices aimed at N estimation were tested to estimate leaf C/N in winter wheat as well as several indices aimed chlorophyll evaluation. The multi-temporal hyperspectral data from the flag-leaf, anthesis, filling, and milk-ripe stages were obtained to calculate these selected spectral indices for evaluating C/N in winter wheat. The results showed that some tested indices such as MCARI/OSAVI2, MTCI and Rep-Le had the better performance of estimating C/N. In addition, GRA (gray relational analysis) and Branch-and-Bound method were also used along with spectral indices sensitive to C/N for improving the accuracy of monitoring C/N in winter wheat, and obtained the better results with R-2 of 0.74, RMSE of 0.991. It indicates that monitoring of leaf C/N in winter wheat with hyperspectral reflectance measurements appears very potential.

相关文献

[1]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

[2]Assessing the ratio of leaf carbon to nitrogen in winter wheat and spring barley based on hyperspectral data. Xu, Xin-gang,Gu, Xiao-he,Song, Xiao-yu,Xu, Bo,Yu, Hai-yang,Yang, Gui-jun,Feng, Hai-kuan. 2016

[3]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

[4]Using new hyperspectral index to estimate leaf chlorophyll content in winter wheat. Xu, Xingang,Song, Xiaoyu,Li, Cunjun,Wang, Jihua. 2012

[5]Estimation of water productivity in winter wheat using the AquaCrop model with field hyperspectral data. Jin, Xiuliang,Jin, Xiuliang,Yang, Guijun,Li, Zhenhai,Xu, Xingang,Jin, Xiuliang,Yang, Guijun,Li, Zhenhai,Xu, Xingang,Wang, Jihua,Lan, Yubin. 2018

[6]Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data. Jin, Xiuliang,Kumar, Lalit,Li, Zhenhai,Xu, Xingang,Yang, Guijun,Li, Zhenhai,Xu, Xingang,Yang, Guijun,Wang, Jihua. 2016

[7]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[8]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[9]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

[10]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

[11]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

[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]Monitoring and Forecasting Winter Wheat Freeze Injury and Yield from Multi-Temporal Remotely Sensed Data. Wang, Huifang,Huo, Zhiguo,Zhou, Guangsheng,Wu, Li,Wang, Huifang,Feng, Haikuan. 2016

作者其他论文更多>>

UssNet: a spatial self-awareness algorithm for wheat lodging area detection

作者：Zhang, Jun;Wu, Qiang;Duan, Fenghui;Liu, Cuiping;Xiong, Shuping;Ma, Xinming;Cheng, Jinpeng;Feng, Mingzheng;Dai, Li;Wang, Xiaochun;Yang, Hao;Yang, Guijun;Chang, Shenglong

关键词：Unmanned aerial vehicle; State space models; Wheat lodging area identification; Semantic segmentation
Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering

作者：Xu, Bo;Zhao, Chunjiang;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao

关键词：tassel; 3D phenotyping; TreeQSM; genotyping; clustering
Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

作者：Chen, Riqiang;Feng, Haikuan;Hu, Haitang;Chen, Riqiang;Ren, Lipeng;Yang, Guijun;Cheng, Zhida;Zhao, Dan;Zhang, Chengjian;Feng, Haikuan;Hu, Haitang;Yang, Hao;Chen, Riqiang;Zhang, Chengjian;Ren, Lipeng;Feng, Haikuan

关键词：maize; chlorophyll; radiative transfer model; feature selection; transfer learning
Retrieving the chlorophyll content of individual apple trees by reducing canopy shadow impact via a 3D radiative transfer model and UAV multispectral imagery

作者：Zhang, Chengjian;Chen, Zhibo;Chen, Riqiang;Zhang, Wenjie;Zhang, Chengjian;Chen, Riqiang;Zhang, Wenjie;Zhao, Dan;Yang, Guijun;Xu, Bo;Feng, Haikuan;Yang, Hao

关键词：Chlorophyll content; Shadows; Vegetation index (VI); Radiative transfer models (RTMs); Hybrid inversion model; Individual apple tree crown
A robust two-stage framework for maize above-ground biomass prediction integrating spectral remote sensing and allometric growth model

作者：Yang, Mohan;Wu, Qiang;Zhang, Jun;Cheng, Jinpeng;Xiong, Shuping;Ma, Xinming;Qi, Jianbo;Yang, Guijun;Yang, Hao;Cheng, Jinpeng;Yang, Guijun;Wang, Zanpu;Wang, Zhenyu

关键词：Maize; Above-ground biomass (AGB); Radiative transfer model; Allometric growth model; Spectral remote sensing
Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves

作者：Bi, Kaiyi;Niu, Yifang;Niu, Zheng;Hao, Yishuo;Wang, Li;Yang, Hao;Hao, Yishuo

关键词：multi-spectral point cloud; UAV; oblique photography; reflectance anisotropy; Maize
Identification of seed maize fields from hyperspectral imagery by fusion of spectral and spatial features

作者：Cheng, Jinpeng;Cao, Xiaoyu;Wu, Qiang;Ma, Xinming;Xiong, Shuping;Cheng, Jinpeng;Yang, Hao;Zhang, Na;Yang, Guijun;Zhang, Na;Huang, Linsheng;Yan, Zhiyu;Wang, Hongbin;Yang, Guijun

关键词：Hyperspectral image classification; Seed maize; Class means matrix clustering; Morphology profiles; Machine learning

Monitoring the ratio of leaf carbon to nitrogen in winter wheat with hyperspectral measurements

作者其他论文 更多>>

UssNet: a spatial self-awareness algorithm for wheat lodging area detection

Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering

Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

Retrieving the chlorophyll content of individual apple trees by reducing canopy shadow impact via a 3D radiative transfer model and UAV multispectral imagery

A robust two-stage framework for maize above-ground biomass prediction integrating spectral remote sensing and allometric growth model

Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves

Identification of seed maize fields from hyperspectral imagery by fusion of spectral and spatial features

意 见 箱

作者其他论文更多>>

意见箱