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作物学报

作物学报 ›› 2009, Vol. 35 ›› Issue (9): 1681-1690.doi: 10.3724/SP.J.1006.2009.01681

• 耕作栽培·生理生化 • 上一篇    下一篇

水稻高光谱红边位置与叶层氮浓度的关系

田永超，杨杰，姚霞，朱艳，曹卫星*   

  1. 南京农业大学/江苏省信息农业高技术研究重点实验室，江苏南京210095
  • 收稿日期:2008-12-11 修回日期:2009-04-17 出版日期:2009-09-12 网络出版日期:2009-07-04
  • 通讯作者: 曹卫星，E-mail: caow@njau.edu.cn
  • 基金资助:

    本研究由国家自然科学基金项目（30571092和30671215），国家高技术研究发展计划（863计划）项目（2006AA10Z202和2006AA10Z271），高校博士点基金项目（20070307035），国家科技支撑计划项目（2006BAD10A01）资助。

Quantitative Relationship between Hyper-spectral Red Edge Position and Canopy Leaf NitrogenConcentration in Rice

TIAN Yong-Chao,YANG Jie,YAO Xia,ZHU Yan,CAO Wei-Xing*   

  1. Jiangsu Key Laboratory for Information Agriculture / Nanjing Agricultural University, Nanjing 210095, China
  • Received:2008-12-11 Revised:2009-04-17 Published:2009-09-12 Published online:2009-07-04
  • Contact: CAO Wei-Xing，E-mail: caow@njau.edu.cn

PDF

3930

被引次数

18

摘要：

实时无损监测叶片氮素状况对水稻精确氮素管理具有重要意义。本研究基于多年不同施氮水平和不同水稻品种的田间试验观测资料，系统分析了水稻高光谱红边区域和位置特征与冠层叶片氮浓度的定量关系。结果表明，水稻冠层的红边区域光谱受施氮水平和品种影响较大，一阶导数光谱在红边区域出现“三峰”现象。经典的红边位置(660~750 nm之间光谱反射率的一阶导数最大值)由于“三峰”特征现象而对水稻氮素浓度变化不够敏感，难以适用于水稻氮素状况的准确监测。基于倒高斯模型、线性内插法和线性外推法构造的红边位置随水稻氮浓度呈现连续变化模式，适用于水稻叶层氮浓度的定量监测；另外，基于695 nm、700 nm和705 nm等3个波段的拉格朗日算法也可估测水稻叶层氮浓度。比较不同红边位置发现，改进型线性外推法较其他几种算法更能有效地监测水稻冠层叶片氮浓度。

关键词: 水稻, 红边位置, 改进型线性外推法, 氮浓度, 监测模型

Abstract:

Real-time and non-destructive monitoring of crop nitrogen status is needed for precision management and dynamic regulation in rice fertilization. This research made a systematic analysis on the characteristics of the first-derivative reflectance spectra in red edge area, and the quantitative relationships between red edge position (REP) with different algorithms and canopy leaf nitrogen concentrations in the conditions of different nitrogen rates and rice varieties in different seasons of field-grown rice. The results showed that spectrum in red edge area was significantly affected by nitrogen levels and varietal types, and “three-peak” feature could be observed with the first derivative spectrum in this area. Traditional REP (the maximum value of the fist derivative spectra in 670–780 nm range) was not sensitive to canopy leaf nitrogen concentration because of the three-peak feature, but the REPs based on inverted Gaussian fitting, linear four-point interpolation, linear extrapolation and adjusted linear extrapolation generated continuous REP data, and could be used to estimate canopy leaf nitrogen concentration. Besides, REP from a three-point Lagrangian interpolation with three first-derivatives bands (695, 700 and 705 nm) also had a good relationship with canopy leaf nitrogen concentration. Comparison of these REPs based on different approaches indicated that the adjusted linear extrapolation method (755FD730+675FD700) / (FD730+FD700) gave the best prediction of canopy leaf nitrogen concentration, with relative simple algorithm, and thus is a good REP parameter for monitoring canopy leaf nitrogen concentration in rice.

Key words: Rice, Red edge position, Adjusted linear extrapolation, Leaf nitrogen concentration, Monitoring model

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

[2] Zhang N Q, Wang M H, Wang N. Precision agriculture—a worldwide overview. Comput Electron Agric, 2002, 36: 113-132

[3] Welsh J P, Wood G A, Godwin R J, Taylor J C, Earl R, Blackmore S, Knight S M. Developing strategies for spatially variable nitrogen application in cereals, part II: wheat. Biosyst Eng, 2003, 84: 495-511

[4] Takebe M, Yoneyama T, Inada K, Murakami T. Spectral reflectance ratio of rice canopy for estimating crop nitrogen status. Plant Soil, 1990, 122: 295-297

[5] Blackmer T M, Schepers J S, Varvel G E, Walter-Shea E A. Nitrogen deficiency detection using shortwave radiation from irrigated corn canopies. Agron J, 1996, 88: 1-5

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

[7] Richardson A J, Everitt J H, Gausman H W. Radiometric estimation of biomass and nitrogen content of Alicia grass. Remote Sens Environ, 1983, 13: 179-184

[8] Thomas J R, Gausman H W. Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agron J, 1977, 69: 799-802

[9] Xue L H, Cao W X, Luo W H, Dai T B, Zhu Y. Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agron J, 2004, 96: 135-142

[10] Bonham-Carter G F. Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data. Comput Geosci, 1988, 14: 339-356

[11] Gates D M, Keegan H J, Schleter J C, Weidner V R. Spectral properties of plants. Appl Optics, 1965, 4: 11-20

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

[13] Filella I, Peñuelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int J Remote Sens, 1994, 15: 1459-1470

[14] Wu C-S(吴长山), Xiang Y-Q(项月琴), Zheng L-F(郑兰芬). Estimating chlorophyll density of crop canopies using hyperspectral data. J Remote Sens (遥感学报), 2000, 4(3): 228-232 (in Chinese with English abstract)

[15] Zhao C-J(赵春江), Huang W-J(黄文江), Wang J-H(王纪华). Studies on the red edge parameters of spectrum in winter wheat under different varieties, fertilizer and water treatments. Sci Agric Sin (中国农业科学), 2002, 35(8): 980-987(in Chinese with English abstract)

[16] Collins W. Remote sensing of crop type and maturity. Photogramm Eng Rem S, 1978, 44: 43-55

[17] Miller J R, Hare E W, Wu J. Quantitative characterization of the vegetation red edge reflectance. An inverted-Gaussian reflectance model. Int J Remote Sens, 1990, 11: 1755-1773

[18] Boochs F, Kupfer G, Dockter K, Kuhbauch W. Shape of the red edge as vitality indicator for plants. Int J Remote Sens, 1990, 11: 1741-1753

[19] Cho M A, Skidmore A K. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sens Environ, 2006, 101: 181-193

[20] Guyot G, Baret F, Jacquemoud S. Imaging spectroscopy for vegetation studies. In: Imaging Spectroscopy: Fundamentals and Prospective Application, Kluwer Academic Publishers (Dordrecht), 1992. pp 145-165

[21] Dawson T P, Curran P J. A new technique for interpolating the reflectance red edge position. Int J Remote Sens, 1998, 19: 2133-2139

[22] Nguyen H T, Lee B W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur J Agron, 2006, 24: 349-356

[23] Clevers J G P W, Jong S M D, Epema G F. Derivation of the red edge index using the MERIS standard band setting. Int J Remote Sens, 2002, 23: 3169-3184

[24] Curran P J, Dungan J L, Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiol, 1990, 7: 33-38

[25] 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. Remote Sens Environ, 2003, 86: 542-553

[26] Haboudane D, Miller J R, Pattey E. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens Environ, 2004, 90: 337-352

[27] Lamb D W, Steyn-ross M, Schaares P. Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: Modelling and experimental observations. Int J Remote Sens, 2002, 23: 3619-3648

[28] Jongschaap R E E, Booij R. Spectral measurements at different spatial scales in potato: relating leaf, plant and canopy nitrogen status. Int J Appl Earth Obs, 2004, 5: 204-218

[29] Guanter L, Richter R, Moreno J. Spectral calibration of hyperspectral imagery using atmospheric absorption features. Appl Optics, 2006, 45: 2360-2370
Schläpfer D, McCubbin I B, Kindel B. Wildfire smoke analysis using the 760 nm oxygen absorption feature. 4th EARSeL Workshop on Imaging Spectroscopy, Warsaw, 2005. pp 1-10
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