| 光谱学与光谱分析 |
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| Inversion of Winter Wheat Foliage Vertical Distribution Based on Canopy Reflected Spectrum by Partial Least Squares Regression Method |
| WANG Ji-hua1,HUANG Wen-jiang1,LAO Cai-lian2,ZHANG Lu-da2,LUO Chang-bing3,WANG Tao2,LIU Liang-yun1,SONG Xiao-yu1,MA Zhi-hong1 |
1. National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China
2. China Agricultural University,Beijing 100094,China
3. Research Center for Shinonglüfang,Beijing 100094,China |
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Abstract With the widespread application of remote sensing (RS) in agriculture,monitoring and prediction of crop nutrition condition attracts attention of many scientists. Foliar nitrogen content (N) is one of the most important nutrients for plant growth,and vertical leaf N gradient is an important indicator of crop nutrition situation. Investigations have been made on N vertical distribution to describe the growth status of winter wheat. Results indicate that from the canopy top to the ground surface,N shows an obvious gradient decreasing trend. The objective of this study was to discuss the inversion method of N vertical distribution with canopy reflected spectrum by the partial least squares regression (PLS) method. PLS was selected for the inversion of upper,middle and lower layers of N. To improve the accuracy of prediction,the N in the upper layer as well as in the middle and bottom layers should be taken into consideration when crop nutrition condition is appraised by RS data. The established models by the observed data in year 2001-2002 were validated by the data in year 2003-2004. The inversion precision and error were acceptable. It provided a theoretic basis for widely and non-damaged variable rate nitrogen application of winter wheat by canopy reflected spectrum.
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Received: 2006-03-16
Accepted: 2006-06-26
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Corresponding Authors:
WANG Ji-hua
E-mail: wangjh@nercita.org.cn
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| Cite this article: |
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WANG Ji-hua,HUANG Wen-jiang,LAO Cai-lian, et al. Inversion of Winter Wheat Foliage Vertical Distribution Based on Canopy Reflected Spectrum by Partial Least Squares Regression Method [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(07): 1319-1322.
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| URL: |
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https://www.gpxygpfx.com/EN/Y2007/V27/I07/1319 |
[1] Shibayama M,Akiyama T. Japanese Journal of Crop Science,1986,55(4): 433.
[2] HUANG Wen-jiang,WANG Ji-hua,WANG Zhi-jie,et al. International Journal of Remote Sensing,2004,25(12): 2409.
[3] ZHOU Qi-fa,WANG Ji-hua. Journal of Plant Nutrition,2003,26(3): 607.
[4] WANG Tao,ZHANG Lu-da,LAO Cai-lian,et al(王 韬，张录达，劳彩莲，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2006，26(10)： 1915.
[5] YANG Chun-yan,LIU Qiang,NIU Zheng,et al(颜春燕，刘 强，牛 铮，等). Science in China(中国科学D辑·地球科学)，2005，35(9)： 881.
[6] Charles-Edwards D,Stutzel H,Ferraris R,et al. Annals of Botany,1987,60: 421.
[7] Anten N,Schieving F,Werger M. Oecologia,1995,101: 504.
[8] Vouillot M,Devienne F. Annals of Botany,1999,83: 569.
[9] Milroy S,Bange M,Sadras V. Annals of Botany,2001,87: 325.
[10] Wessman C,Aber J,Peterson D. International Journal of Remote Sensing,1989,10: 1293.
[11] Shiraiwa T,Sinclair T. Crop Science,1993,33: 804.
[12] Lemaire G,Onillon B,Gosse G. Annals of Botany,1991,68: 483.
[13] Connor D J,Sadras V O,Hall A J. Oecologia,1995,101: 274.
[14] Serrano L,Filella I,Penuelas J. Crop Science,2000,40: 723.
[15] WANG Ji-hua,WANG Zhi-jie,HUANG Wen-jiang,et al(王纪华，王之杰，黄文江，等). Journal of Remote Sensing(遥感学报)，2004，8(3)： 36.
[16] WANG Zhi-jie,WANG Ji-hua,LIU Liang-yun,et al. Field Crops Research,2004,90(2-3): 311.
[17] TANG Qi-yi,FENG Ming-guang(唐启义,冯明光). DPS Data Processing System for Practical Statistics(实用统计分析及其DPS数据处理系统). Beijing: Science Press(北京: 科学出版社),2002. 35.
[18] WANG Hui-wen(王惠文). Partial Least Square Regression Method and Its Application(偏最小二乘回归方法及其应用). Beijing: National Defence Industry Press(北京： 国防工业出版社)，1999. 45.
[19] ZHAO Li-li,ZHAO Long-lian,LI Jun-hui,et al(赵丽丽,赵龙莲,李军会,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2004,24(1): 41.
[20] ZHANG Lu-da,JIN Ze-chen,SHEN Xiao-nan,et al(张录达,金泽宸,沈晓南,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2005,25(9): 1400. |
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