| 基于光谱红边参数的棉花黄萎病叶片氮素含量诊断研究 |
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| 引用本文： | 陈兵,王方永,韩焕勇,刘政,邓福军,林海,余渝,李少昆,王克如.基于光谱红边参数的棉花黄萎病叶片氮素含量诊断研究[J].棉花学报,2013,25(3):254-261. |
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| 作者姓名： | 陈兵 王方永 韩焕勇 刘政 邓福军 林海 余渝 李少昆 王克如 |
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| 作者单位： | 1. 新疆农垦科学院棉花研究所/农业部西北内陆区棉花生物学与遗传育种重点实验室/国家棉花改良中心新疆生产建设兵团分中心,新疆石河子,832003
2. 中国农业科学院作物科学研究所/国家农作物基因资源与基因改良重大科学工程,北京100081;新疆兵团绿洲生态农业重点开放实验室/石河子大学,新疆石河子832000 |
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| 基金项目： | 国家自然基金，兵团科技攻关项目，新疆农垦科学院科技引导计划 |
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| 摘 要： | 以黄萎病胁迫下棉花叶片为供试材料，分析了黄萎病棉叶氮素含量(LNC)与光谱红边参数间的关系，建立了黄萎病棉叶LNC(Leaf nitrogen content) 的光谱红边参数诊断模型。结果表明：(1) 随着黄萎病严重程度的增加，棉叶LNC逐渐减小，且差异显著；（2）黄萎病棉叶红边参数红边位置(REP) 、红边振幅（Dr）、红谷位置(Lo) 、红边深度（Depth672）和红边面积(Area672) 均减小，红边宽度(Lwidth) 增加，且Area672的值减小的幅度最大，Dr减小的幅度最小，Lwidth的值增加的幅度较大； (3) 黄萎病棉叶LNC含量均与红边参数REP、Lo、Depth672和Area672呈极显著正相关，与Lwidth呈极显著负相关，与Dr未达显著相关；(4)基于红边参数建立的黄萎病棉叶LNC含量的诊断模型均达到极显著水平（P<0.01），其中以Area672为自变量建立的黄萎病棉叶LNC的诊断模型的精度最高，R2超过0.7，RMSE小于0.6，RE小于0.007，能很好地诊断黄萎病棉叶LNC。
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| 关 键 词： | 棉花 黄萎病 胁迫 高光谱 红边参数 氮素含量 诊断模型 |
| 收稿时间： | 2012-07-09; |
Monitoring Nitrogen Contents in Leaves of Cotton under Verticillium Wilt Stress Based on Spectra Red-edge Parameters |
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| CHEN Bing,WANG Fang-yong,HAN Huan-yong,LIU Zheng,DENG Fu-jun,LIN Hai,YU Yu,LI Shao-kun,WANG Ke-ru,XIAO Chun-hua.Monitoring Nitrogen Contents in Leaves of Cotton under Verticillium Wilt Stress Based on Spectra Red-edge Parameters[J].Cotton Science,2013,25(3):254-261. |
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| Authors: | CHEN Bing WANG Fang-yong HAN Huan-yong LIU Zheng DENG Fu-jun LIN Hai YU Yu LI Shao-kun WANG Ke-ru XIAO Chun-hua |
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| Abstract: | Using data from red-edge parameters of haperspectra, we endeavored to provide an expedient way to extract leaf nitrogen contents(LNC) of cotton infected with Verticillium wilt, and to lay the groundwork for estimating cotton yield infected by Verticillium wilt using remote sensing technology. The relationship between LNC and red edge parameters were analyzed, and diagnose models of spectra red edge parameters were established for cotton leaves infected by Verticillium wilt. The main results are as follows: (1) With the increase of leaf severity level, LNC little by little decreased, difference is significant. (2) In all red edge parameters, REP, Dr, Lo, Depth672 and Area672 all decreased, and the degree of decrease was maximum to Area672 value, the degree of decrease was minimum to Dr value; but Lwidth largely increased. (3) LNC had best significant positive correlations with REP, Lo, Depth672 and Area672, had best significant negative correlations with Lwidth, and no best significant correlations with Dr. (4) Diagnose models of LNC to disease cotton leaves on the basis of spectra red edge parameters all attached best significant correlations (P<0.01). The diagnose models based on Area672 had best estimated precision for LNC, and R2 were over 0.7, RMSE less than 0.6, RE less than 0.007. So using red edge parameters of haperspectra monitor LNC of cotton infected by Verticillium wilt is accurate and convenient, and those models can better diagnose LNC. |
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| Keywords: | Verticillium wilt disease stress hyper spectra red edge parameters nitrogen contents diagnose models |
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