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Acta Agron Sin ›› 2011, Vol. 37 ›› Issue (06): 1039-1048.doi: 10.3724/SP.J.1006.2011.01039

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Estimation of Canopy Leaf Nitrogen Status Using Imaging Spectrometer and Digital Camera in Cotton

WANG Fang-Yong1,WANG Ke-Ru1,2,LI Shao-Kun1,2,*,GAO Shi-Ju2,XIAO Chun-Hua1,CHEN Bing1,CHEN Jiang-Lu1,LÜ Yin-Liang1,DIAO Wan-Ying1   

  1. 1 Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops / The Center of Crop High-Yield Research, Shihezi 832003, China; 2 Key Laboratory of Crop Physiology and Production, Ministry of Agriculture/ Institute of Crop Sciences, Chinese Academy of Agriculture Sciences, Beijing 100081, China
  • Received:2010-11-19 Revised:2011-03-28 Online:2011-06-12 Published:2011-04-12
  • Contact: 李少昆, E–mail: Lishk@mail.caas.net.cn, Tel:010–82108891

Abstract: Leaf nitrogen content is an important index to evaluate and estimate crop growth status, yield and quality. Real-time and non-destructive measurement of nitrogen nutrition status of plants is useful for nitrogen fertilizer management in cotton (Gossypium hirsutum L.) production. The objectives of this study were to determine the relationships of ground–based canopy spectral reflectance, spectral index and color parameters obtained by using common digital camera (Olympus C-5060) and imaging spectrometer (MSI200), with canopy leaf nitrogen content, and to develop regression models for estimating leaf nitrogen content in cotton. The results showed that canopy spectral reflectance decreased with increasing leaf nitrogen content, and the bands sensitive to leaf nitrogen content occurred the green and red regions mainly. Furthermore, the models to retrieve canopy leaf nitrogen contents using DI (R580, R680) and G–R were most feasible with the maximum determination coefficients (R2) and the minimum standard error (SE) for two visible sensors, respectively. Additional, b* (CIE 1976 L*a*b* color model) and H (HSI color model) were the optimum color parameters. On the whole, for the fitting effects, the spectral index was superior to color parameters for the same sensor, and MSI200 superior to digital camera. Then, the prediction performances of the spectral indices of digital camera were validated by using independent dataset. We found that difference indices DI (R580, R680) and G–R were the optimum indicators of canopy leaf nitrogen content with the highest predictive precision (0.8131 and 0.7636, respectively) and accuracy (1.0149 and 0.9661) and the lowest RMSE (2.3313 and 2.7406 mg g–1, approximately 6.52% and 8.24% of the mean). Hence, canopy spectral parameters in visible region may provide an effective and feasible means of estimating canopy leaf nitrogen contents quantitatively in cotton field.

Key words: Cotton canopy, Leaf nitrogen content, Imaging spectrometer, Digital camera, Spectral index, Color parameter

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