| 光谱学与光谱分析 |
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| Monitoring Models of the Plant Nitrogen Content Based on Cotton Canopy Hyperspectral Reflectance |
| WANG Ke-ru1,2，PAN Wen-chao1，2，LI Shao-kun1,2*，CHEN Bing2，XIAO Hua2，WANG Fang-yong2，CHEN Jiang-lu2 |
1. Institute of Crop Science, Chinese Academy of Agricultural Sciences，The National Key Facilities for Crop Genetic Resources and Improvement, NFCRI, Beijing 100081, China
2. Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crop，The Center of Crop High-yield Research, Shihezi 832003, China |
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Abstract Cotton production for accurate non-destructive, rapid monitoring of plant nitrogen content there is an urgent demand. Canopy spectral characteristics of the cotton plant and its quantitative relationship between nitrogen content, can achieve non-destructive monitoring of cotton nitrogen. Two consecutive years by different nitrogen test, cotton canopy hyperspectral data collection and simultaneous determination of canopy nitrogen content, analysis of different fertilizer treatments of cotton canopy spectral characteristics and the relationship between nitrogen content of cotton, the results show that: nitrogen content of cotton plant in different periods and spectral reflectance in the visible band (400～700 nm) was negatively related to the near-infrared 700～1 300 nm band was a significant positive correlation, and in the short-wave infrared 1 300～1 800 nm band correlation is more complicated. Canopy scale, the whole growth stage of cotton, the visible band are sensitive to nitrogen content in cotton band, and near-infrared only is the cotton boll nitrogen content of the sensitive band; short-wave infrared band only in the budding period Cotton nitrogen sensitive band. Using nitrogen-sensitive bands in different periods can be constructed Cotton Cotton Nitrogen monitoring indicators.
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Received: 2010-06-20
Accepted: 2010-09-28
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Corresponding Authors:
LI Shao-kun
E-mail: Lishk@mail.caas.net.cn
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[1] TIAN Yong-chao, ZHU Yan, YAO Xia, et al(田永超, 朱 艳, 姚 霞，等). Chinese Journal of Ecology(生态学杂志), 2007, 26(9): 1454.
[2] Kimes D S, Idso S B, Pinter P J, et al. Remote Sensing of Environment, 1980, 10：273.
[3] Curran P J, Dungan J L, Macler B A, et al. Remote Sensing of Environment, 1992, 39：153.
[4] Thomas J R，Oerther G F. Agronomy Journal, 1972, 64: 11.
[5] Shibayama M, Akiyama T A. Remote Sensing of Environment, 1993, 45: 117.
[6] Daniela Stroppiana, Mirco Boschetti, Pietro Alessandro Brivio, et al. Field Crop Research, 2009，(111): 119.
[7] ZHAO Chun-jiang, HUANG Wen-jiang, WANG Ji-hua(赵春江，黄文江，王纪华). Scientia Agricultura Sinica(中国农业科学), 2002, 35(8): 980.
[8] LI Ying-xue, ZHU Yan, TIAN Yong-chao, et al(李映雪, 朱 艳, 田永超，等). Scientia Agricultura Sinica(中国农业科学), 2005, 38(7): 1332.
[9] LI Ying-xue, ZHU Yan, TIAN Yong-chao, et al(李映雪, 朱 艳, 田永超，等). Acta Agronomica Sinica(作物学报)，2006, 32(3): 358.
[10] LI Ying-xue, ZHU Yan, TIAN Yong-chao, et al(李映雪, 朱 艳, 田永超，等). Acta Agronomica Sinica(作物学报), 2006, 32(2): 203.
[11] FENG Wei, ZHU Yan, YAO Xia, et al(冯 伟，朱 艳，姚 霞，等). Scientia Agricultura Sinica(中国农业科学)，2008, 41(7): 1937.
[12] Bodo Mistele, Urs Schmidhalter. European Journal of Agronomy, 2008，29: 184.
[13] Humburg D S, Stange K W, Robert P C. Proceedings of the Fourth International Conference on Precision Agriculture, St Paul, Minnesota, USA, 19981 Part A and Part B, 1593，1602.
[14] Lee Tarpley, Raia K Reddy, Gretchen F Sassenrath-Cole. Crop Science, 2000, 40: 1814.
[15] Kevin F Bronson, Teresita T Chua, Booker J D, et al. Soil Sci. Soc. Am. J., 2003, 67: 1439.
[16] Zhao Duli, Raja K Reddy, Vijaya Gopal Kakani, et al. Agron. J.，2005, 97: 89.
[17] YAO Xia, WU Hua-bing, ZHU Yan, et al(姚 霞，吴华兵，朱 艳，等). Cotton Science(棉花学报), 2007, 19(4): 267.
[18] WU Hua-bing, ZHU Yan, TIAN Yong-chao, et al(吴华兵，朱 艳，田永超，等). Journal of Plant Ecology(植物生态学报), 2007, 31(5): 903.
[19] HUANG Chun-yan, WANG Deng-wei, YAN Jie, et al(黄春燕，王登伟，闫 洁，等). Acta Agronomica Sinica(作物学报)，2007, 33(6): 931.
[20] SUN Li, CHEN Xi, WU Jian-jun, et al(孙 莉, 陈 曦, 武建军, 等). Remote Sensing Technology and Application(遥感技术与应用), 2005, 20(3): 315.
[21] PU Rui-liang, GONG Peng(浦瑞良，宫 鹏). Hyperspectal Remote Sensing and Its Applications(高光谱遥感及应用). Beijing: Higher Education Press(北京：高等教育出版社)，2000. 202.
[22] YAO Xia, ZHU Yan, TIAN Yong-chao, et al(姚 霞，朱 艳，田永超，等). Scientia Agricultura Sinica(中国农业科学), 2009, 42(8): 2716.
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