首页 > 论文 > 红外与毫米波学报 > 32卷 > 4期(pp:351-359)

基于可见光-近红外新光谱特征和最优组合原理的大麦叶片氮含量监测

Associating new spectral features from visible and near infrared regions with optimal combination principle to monitor leaf nitrogen concentration in barley

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

提出新的作物冠层叶片氮含量(LNC)高光谱遥感监测方法,以对氮素要求较高的大麦LNC监测为例,利用田间实测数据,从可见光-近红外区域的高光谱反射曲线中提取包含丰富多波段信息的斜率、夹角等新型特征参数,应用组合预测领域中的权重最优组合原理及其算法,实现对作物LNC的高光谱监测.研究表明,提出的高光谱反射曲线斜率和夹角等新型特征参数与作物LNC显著相关,并具有较好的定量响应关系,其中关键斜率参数(Kre/Kpb)和Kpb以及夹角参数(Aδ/Aα)和(Aδ/Aθ)较好地描述了LNC的动态变化; 而权重最优组合分析则表明(Kre/Kpb)和Knir1两个参数的组合最能响应LNC的光谱信息,有助于增强监测的稳定性并提高估测的精度.

Abstract

The paper proposed a method to monitor LNC in crop with hyperspectral remote sensing. Taking the LNC monitoring of barley that is more demanding for nitrogen fertilization as a case, this study employs new spectral features such as slopes and angles extracted from the normalized reflectance curves in Visible-Near Infrared region to evaluate LNC, At the same time, the optimal combination principle that was widely used in the combinated forecasting domains was presented to estimate LNC. The analysis resluts proved that most of the new spectral features propsoed in the study exhibited significant correlations with LNC. Among the new spectral features, the key features of slopes (Kre/Kpb and Kpb) and angles (Aδ/Aα and Aδ/Aθ) could well describe the dynamic pattern of LNC changes in crop. The optimal combination algorithm determined the optimal combination with Kre/Kpb and Knir1, which could increase the spectral responding to LNC, strengthen the stability of models monitoring LNC and improve the accuracy of LNC estimates.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:S127

DOI:10.3724/sp.j.1010.2013.00351

基金项目:国家自然科学基金( 41071228,41001244,41071276)

收稿日期:2012-03-14

修改稿日期:2012-11-01

网络出版日期:--

作者单位    点击查看

徐新刚:北京农业信息技术研究中心/国家农业信息化工程技术研究中心,北京100097
赵春江:北京农业信息技术研究中心/国家农业信息化工程技术研究中心,北京100097
王纪华:北京农业信息技术研究中心/国家农业信息化工程技术研究中心,北京100097
李存军:北京农业信息技术研究中心/国家农业信息化工程技术研究中心,北京100097
杨小冬:北京农业信息技术研究中心/国家农业信息化工程技术研究中心,北京100097

联系人作者:徐新刚(xxgpaper@126.com)

备注:徐新刚(1976-),男,湖北鄂州人,副研,博士,主要从事光学遥感在农业的应用研究.

【1】Hucklesby D P, Brown C M, Howell S E, et al. Late spring applications of nitrogen for efficient utilization and enhanced production of grain and grain protein in wheat [J]. Agron J, 1971, 63: 274-276.

【2】Woodard H J, Bly A. Relationship of nitrogen management to winter wheat yield and grain protein in South Dakota [J]. J Plant Nutr, 1998, 21: 217-233.

【3】GUO Sheng-Li, DANG Ting-Hui, HAO Ming-De. Effects of fertilization on wheat yield, No3--N accumulation and soil water content in semi-arid area of china [J]. Scientia Agricultura Sinica (郭胜利, 党廷辉, 郝明德. 施肥对半干旱地区小麦产量、NO3--N累积和水分平衡的影响. 中国农业科学), 2005, 38(4): 754-760.

【4】SUN Da-Ye, YANG Jia-Si. Applying sugar-nitrogen ratio to diagnose the nutrition in wheat plants [J]. Scientia Agricultura Sinica (孙大业, 杨家泗. 糖氮比在小麦植株营养诊断中的运用. 中国农业科学), 1978, 11(4): 32-39.

【5】JIN Xian-Chun, XU Wei-Sheng, ZHANG Ling, et al. Influence of physiological characteristics at grain filling stage on dying green of wheat plants [J]. Acta Agronomica Sinica (金先春, 徐威生, 张玲, 等. 不同小麦品种的灌浆生理特性对后期青枯影响的研究. 作物学报), 1994, 20(1): 99-105.

【6】Gregory P A. Biophysical and biochemical sources of variability in canopy reflectance [J]. Remote Sens Environ, 1998, 64: 234-253.

【7】Pu R, Ge S, Kelly N M, et al. Spectral absorption features as indicators of water status in coast live oak (Quevcus agrifolia) leaves [J]. Int J Remote Sens, 2003, 24: 1799-1810.

【8】Zhang Y, Chen J M, Miller J R, et al. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery [J]. Remote Sens Environ, 2008, 112: 3234-3247.

【9】Curran P J. Remote sensing of foliar chemistry [J]. Remote Sens Environ, 1989, 30: 271-278.

【10】Fourty T, Baret F, Jacquemoud S, et al. Leaf optical properties with explicit description of its biochemical composition: direct and inverse problems [J]. Remote Sens Environ, 1996, 56: 104-117.

【11】Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression [J]. Remote Sens Environ, 1999, 67: 267-287.

【12】Elvidge D E. Visible and near infrared reflectance characteristics of dry plant materials [J]. Remote Sens Environ, 1990, 11: 1775-1795.

【13】Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression [J]. Remote Sens Environ, 2003, 86: 542-553.

【14】YAO Xia, ZHU Yan, TIAN Yong-Chao, et al. Research of the optimum hyperspectral vegetation indices on monitoring the nitrogen content in wheat leaves [J]. Scientia Agricultura Sinica (姚霞, 朱艳, 田永超, 等. 小麦叶层氮含量估测的最佳高光谱参数研究. 中国农业科学), 2009, 42(8): 2716-2725.

【15】Feng W, Yao X, Zhu Y, et al. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat [J]. Eur J Agron, 2008, 28: 394-404.

【16】Chen P, Driss H, Tremblay N, et al. New index for estimating crop nitrogen concentration using hyperspectral data [J]. Remote Sens Environ, 2010, 114: 1987-1997.

【17】XU Xin-Gang, ZHAO Chun-Jiang, WANG Ji-Hua, et al. Study on relationship between new characteristic parameters of spectral curve and chlorophyll content for rice [J]. Spectroscopy and Spectral Analysis (徐新刚, 赵春江, 王纪华, 等. 新型光谱曲线特征参数与水稻叶绿素含量的关系研究. 光谱学与光谱分析), 2011, 31(1): 188-191.

【18】Jongschaap E E, Booij R. Spectral measurements at different spatial scales in potato: relating leaf, plant and canopy nitrogen status [J]. Int J Appl Earth Obs Geoinform, 2004, 5: 205-218.

【19】Wallis K F. Combining forecasts: forty years later [J]. Applied Financial Economics, 2011, 21: 33-41.

【20】Pu R. Broadleaf species recognition with in situ hyperspectral data [J]. Int J Remote Sens, 2009, 30: 2759-2779.

【21】ZHANG Qing. Application research on an optimal mix forecasting method based on ANN [J]. Systems Engineering-Theory & Applications (张青. 基于神经网络最优组合预测方法的应用研究. 系统工程理论与实践), 2001, 21(9): 90-93.

【22】WANG Shuo, TANG Xiao-Wo, ZENG Yong. A research on combination forecasting approach based on acceleration genetic algorithm [J]. Science Research Management (王硕, 唐小我, 曾勇. 基于加速遗传算法的组合预测方法研究. 科研管理), 2002, 23(3): 118-121.

【23】WU Jing-Min, ZUO Hong-Fu, CHEN Yong. A combined forecasting method based on particle swarm optimization with immunity algorithms [J]. Systems Engineering-Theory Methodology Applications (吴静敏, 左洪福, 陈勇. 基于免疫粒子群算法的组合预测方法. 系统工程理论方法应用), 2006, 15(3): 229-233.

【24】TANG Xiao-Wo, ZENG Yong, CAO Chang-Xiu. An iterative algorithm for optimal combination forecasting of non-negative weights [J]. Systems Engineering Theory Methodology Applications (唐小我, 曾勇, 曹长修. 非负权重最优组合预测的迭代算法研究. 系统工程理论方法应用), 1994, 3(4): 48-52.

【25】Horler D N H, Dockray M, Barber J. The red edge of plant leaf reflectance [J]. Int J Remote Sens, 1983, 4: 273-288.

引用该论文

XU Xin-Gang,ZHAO Chun-Jiang,WANG Ji-Hua,LI Cun-Jun,YANG Xiao-Dong. Associating new spectral features from visible and near infrared regions with optimal combination principle to monitor leaf nitrogen concentration in barley[J]. Journal of Infrared and Millimeter Waves, 2013, 32(4): 351-359

徐新刚,赵春江,王纪华,李存军,杨小冬. 基于可见光-近红外新光谱特征和最优组合原理的大麦叶片氮含量监测[J]. 红外与毫米波学报, 2013, 32(4): 351-359

被引情况

【1】赵芸,张初,刘飞,孔汶汶,何勇. 采用可见/近红外光谱检测大麦叶片过氧化氢酶与过氧化物酶含量的研究. 光谱学与光谱分析, 2014, 34(9): 2382-2386

【2】刘豪杰,李民赞,张俊逸,高德华,孙 红,吴静珠. 一种针对作物生育期光谱迁移的修正植被指数. 光谱学与光谱分析, 2019, 39(10): 3040-3046

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF