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Study on Relationship between Tobacco Canopy Spectra and LAI

  • Hongbo Qiao
  • Weng Mei
  • Yafei Yang
  • Wang Yong
  • Jishuai Zhang
  • Yu Hua
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 345)

Abstract

N nutrition is one of the most important limitation factors for crop growth and yield. Precise and timely monitoring and detection of crop N nutrient conditions is necessary for improving the efficiency of N nutrition using and crop management, reducing environmental pollution caused by over nitrogen fertilizer application. In this paper the canopy reflectance spectra during the whole growth period on tobacco field plots treated with different nitrogen levels were periodically and continually measured. LAI of tobacco of several important growth periods was meanwhile measured. The results showed that tobacco canopy spectral reflectance of different growing periods changed regularly with the increase of nitrogen fertilizer application. The canopy spectral reflectance increased in 7l0~1000 nm, while decreased in 460~680 nm. There was high correlations between NDVI and LAI. The research supply theoretical foundation for tobacco N management.

Keywords

Tobacco spectra LAI 

References

  1. 1.
    Shen, G.R., Wang, R.C.: Review of the application of vegetation remote sensing. Journal of Zhejiang University (Agric. &Life Sci. ) 27(6), 682–690 (2001) (in Chinese)MathSciNetGoogle Scholar
  2. 2.
    Curran, P.J.: Remote sensing of foliar chemistry. Remote sensing of environment 30, 271–278 (1989)CrossRefGoogle Scholar
  3. 3.
    Hinzman, L.D., Bauer, M.E., Daughtry, C.S.T.: Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat. Remote sensing of environment 19, 47–61 (1986)CrossRefGoogle Scholar
  4. 4.
    Svetlana, M.K., Taras, A.K.: Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. Journal of Plant Physiology 12(3), 1648–1655 (2007)Google Scholar
  5. 5.
    Daughtry, C.S.T., Walthall, C.L., Kim, M.S., et al.: Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment 74(2), 229–239 (2000)CrossRefGoogle Scholar
  6. 6.
    Elizabeth, J.B., Brigitte, L., Bernie, Z., et al.: Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model. International Journal of Applied Earth Observation and Geoinformation 9(4), 360–374 (2007)CrossRefGoogle Scholar
  7. 7.
    Bouman, B.A.M.: Linking physical remote sensing model with crop growth simulation models, applied for sugar beet. International of Remote Sensing 13(2), 2565–2581 (1992)CrossRefGoogle Scholar
  8. 8.
    Wu, J.D., Wang, D., Marvin, E.B.: Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies. Field Crops Research 102(1), 33–42 (2007)CrossRefGoogle Scholar
  9. 9.
    Hung, T.N., Byun, W.L.: Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regress. European Journal of Agronomy 24(4), 349–356 (2006)Google Scholar
  10. 10.
    Liu, G.S.: Tobacco Cultivation, pp. 38–39. China Agriculture Press, Beijing (2003)Google Scholar
  11. 11.
    Feng, W., Yao, X., Zhu, Y., et al.: Monitoring Leaf Nitrogen Concentration by Hyperspectral Remote Sensing in Wheat. Journal of Triticeae Crops 28(5), 851–860 (2008) (in Chinese)Google Scholar
  12. 12.
    Vaesen, K., Gilliams, S., Nackaerts, K.: Ground-measured spectral signatures as indicators of ground cover and leaf area index: the case of paddy rice. Field Crops Research 69(1), 13–25 (2001)CrossRefGoogle Scholar
  13. 13.
    Xue, L.H., Cao, W.X., Luo, W.H., et al.: Relationship Between Spectral Vegetation Indices and LAI In Rice. Acta Phytoecologica Sinica 28(1), 47–52 (2004) (in Chinese)Google Scholar
  14. 14.
    Aparicio, N., Villegas, D., Araus, J.L., et al.: Relationship between growth traits and spectral veg2etation indices in Durum wheat. Agronomy Journal 42, 1547–1555 (2002)Google Scholar
  15. 15.
    John, M., John, C.O., Jennifer, L.M.: Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research 46, 467–472 (1995)CrossRefGoogle Scholar
  16. 16.
    Carlos, C., Lianne, M.D., Pierre, D., et al.: Inter-relationships of applied nitrogen, SPAD, and yield of leafy and non-leafy maize genotypes. Journal of Plant Nutrition 24(8), 1173–1194 (2001)CrossRefGoogle Scholar
  17. 17.
    Zeng, J.M., Yao, H., Li, T.F., et al.: Chlorophyll Content Determination and its Relationship with SPAD Readings in Flue-cured Tobacco. Molecular Plant Breeding 7(1), 56–62 (2009) (in Chinese)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Hongbo Qiao
    • 1
  • Weng Mei
    • 1
  • Yafei Yang
    • 1
  • Wang Yong
    • 2
  • Jishuai Zhang
    • 2
  • Yu Hua
    • 1
  1. 1.College of information and management scienceHenan agricultural universityP. R. China
  2. 2.Sanmenxia Branch of Henan Tobacco CompanySanmenxiaP. R. China

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