Comparison of Remote Sensing Estimation Methods for Winter Wheat Leaf Nitrogen Content

  • Chunlan Zhang
  • Fuquan Tang
  • Heli LiEmail author
  • Guijun Yang
  • Haikuan Feng
  • Chang Liu
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)


Leaf nitrogen content (LNC) is a good indicator of the nutritional status of winter wheat, and remote sensing monitoring of nitrogen level in winter wheat growth period can not only grasp the crop nutrient and growth conditions, but also help to improve the yield and quality. In this study, field data of canopy reflectance and LNC of winter wheat of three critical growth stages were collected for different treatments during 2014/2015 and 2015/2016. The correlation between LNC of winter wheat and 16 spectral indices was compared and analyzed, and then 4 spectral indices of NDSI (R594, R506), RSI (R592, R506), mSR705 and mNDVI705 were selected. On the basis of this, linear regression (LR) model, multiple stepwise regression (MSR) model and random forest regression (RFR) model were constructed and validated with independent data sets in 2014/2015. To further compare the accuracy, stability and applicability of three inversion models, the robustness tests were conducted based on the independent data sets under three different conditions in 2015/2016. The result showed that the RFR model had the best estimation accuracy among the three models, and the value of R2 and RMSE in modeling set respectively were 0.962 and 0.276, and the value of R2 and RMSE in validation set were 0.898 and 0.401. In addition, the RFR model had a higher R2 and lower RMSE than the other two models under each condition. It indicated that the RFR model combined with multiple spectral indices and random forest algorithm had higher precision and applicability, so it can effectively and rapidly retrieve the LNC of winter wheat.


Leaf nitrogen content (LNC) Remote sensing Winter wheat Comparison 



This work was supported in part by the National key research and development program (2016YFD0200600, 2016YFD02006030) and National Natural Science Foundation of China (No. 41671411; 41471351, 41601346).


  1. 1.
    Tian, Y.C., Zhu, Y., Yao, X., Liu, X.J., Cao, W.X.: Nondestructive monitoring of crop nitrogen nutrition based on spectral information. Chin. J. Ecol. (09), 1454–1463(2007)Google Scholar
  2. 2.
    Li, Y.M., Zhang, X.J., Zhang, L.G.: Hyperspectral nitrogen estimation model of remote sensing in rice. Jiangsu Agric. Sci. 44(8), 435–439 (2014)Google Scholar
  3. 3.
    Wang, R.H., Song, X.Y., Li, Z.H.: Estimation of winter wheat nitrogen nutrition index using hyperspectral remote sensing. Trans. Chin. Soc. Agric. Eng. 30(19), 191–198 (2014)Google Scholar
  4. 4.
    Nguyen, H.T., Kim, J.H., Nguyen, A.T.: Using canopy reflectance and partial least squares regression to calculate within-field statistical variation in crop growth and nitrogen status of rice. Precis. Agric. 7(4), 249–264 (2006)CrossRefGoogle Scholar
  5. 5.
    He, L., et al.: Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data. Remote Sens. Environ. 174(174), 122–133 (2016)CrossRefGoogle Scholar
  6. 6.
    Inoue, Y.S., Sakaiya, E.J., Zhu, Y., Takahashi, W.: Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sens. Environ. 126, 210–221 (2012)CrossRefGoogle Scholar
  7. 7.
    Zhang, C.H., Kovacs, J.M., Wachowiak, M.P.: Relationship between hyperspectral measurements and mangrove leaf nitrogen concentrations. Remote Sens. 5(2), 891–908 (2013)CrossRefGoogle Scholar
  8. 8.
    Tong, Q.X., Zhang, B., Zheng, L.F.: Hyperspectral Remote Sensing. Higher Education Press, Beijing (2006)Google Scholar
  9. 9.
    Jiang, H.L., Yang, H., Chen, X.P., Wang, S.D., Li, X.K., Liu, K.: Research on accuracy and stability of inversing vegetation chlorophyll content by spectral index method. Spectrosc. Spectr. Anal. 35(04), 975–981 (2015)Google Scholar
  10. 10.
    Hunt, E.R., Doraiswamy, P.C., McMurtrey, J.E.: A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Appl. Earth Obs. Geoinf. 21, 103–112 (2013)CrossRefGoogle Scholar
  11. 11.
    Peñuelas, J., Isla, R., Filella, I.: Visible and near-infrared reflectance assessment of salinity effects on Barley. Crop Sci. 37(1), 198–202 (1997)CrossRefGoogle Scholar
  12. 12.
    Tanaka, S., Kawamura, K., Maki, M.: Spectral index for quantifying leaf area index of winter wheat by field hyperspectral measurements: a case study in Gifu prefecture, central Japan. Remote Sens. 7(5), 5329–5346 (2015)CrossRefGoogle Scholar
  13. 13.
    Gitelson, A.A., Merzlyak, M.N.: Quantitative estimation of chlorophyll-a using reflectance spectral: experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B-Biol. 22(3), 247–252 (1994)CrossRefGoogle Scholar
  14. 14.
    Asner, G.P., Martin, R.E., Knapp, D.E.: Spectroscopy of canopy chemicals in humid tropical forests. Remote Sens. Environ. 115(12), 3587–3598 (2011)CrossRefGoogle Scholar
  15. 15.
    Gitelson, A.A., Merzlyak, M.N.: Spectral reflectance changes associated with autumn senescence of aesculus Hippocastanum L. and Acer Platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 143(3), 286–292 (1994)CrossRefGoogle Scholar
  16. 16.
    He, J., Liu, B.F., Li, J.: Monitoring model of leaf a area index of winter wheat based on hyperspectral reflectance at different growth stages. Trans. Chin. Soc. Agric. Eng. 30(24), 141–150 (2014)Google Scholar
  17. 17.
    Datt, B.: A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using eucalyptus leaves. J. Plant Physiol. 154(1), 30–36 (1999)CrossRefGoogle Scholar
  18. 18.
    Sims, D.A., Gamon, J.A.: Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81(2), 337–354 (2002)CrossRefGoogle Scholar
  19. 19.
    Vogelmann, J.E., Rock, B.N., Moss, D.M.: Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 14(8), 1563–1575 (1993)CrossRefGoogle Scholar
  20. 20.
    Yin, X.J., Zhang, Q., Zhao, Q.Z.: Remote sensing inversion of nitrogen content based on SVM in processing tomato early blight leaves. Trans. Chin. Soc. Agric. Mach. 45(11), 280–285+39 (2014)Google Scholar
  21. 21.
    Richardson, A.J., Weigand, C.: Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens. 43(12), 1541–1552 (1977)Google Scholar
  22. 22.
    Chen, J.M.: Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 22(3), 229–242 (1996)CrossRefGoogle Scholar
  23. 23.
    Gitelson, A.A., Vina, A., Ciganda, V.: Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32(8), 1–4 (2005)CrossRefGoogle Scholar
  24. 24.
    Dash, J., Curran, P.J.: Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Adv. Space Res. 39(1), 100–104 (2007)CrossRefGoogle Scholar
  25. 25.
    Baret, F., Guyot, G.: Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ. 35(2), 161–173 (1991)CrossRefGoogle Scholar
  26. 26.
    Lelong, C.C.D., Burger, P., Jubelin, G.: Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 8(5), 3557–3585 (2008)CrossRefGoogle Scholar
  27. 27.
    Gupta, R.K., Vijayan, D., Prasad, T.S.: Comparative analysis of red-edge hyperspectral indices. Adv. Space Res. 32(11), 2217–2222 (2003)CrossRefGoogle Scholar
  28. 28.
    Inoue, Y., Guérif, M., Baret, F.: Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation. Plant, Cell Environ. 39, 2609–2623 (2016)CrossRefGoogle Scholar
  29. 29.
    Qin, Z.F., Chang, Q.R., Xie, B.N., Shen, J.: Rice leaf nitrogen content estimation based on hyperspectral imagery of UAV in yellow river diversion irrigation district. Trans. Chin. Soc. Agric. Eng. 32(23), 77–85 (2016)Google Scholar
  30. 30.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  31. 31.
    Tian, Y.C., Gu, K.J., Xu, C.: Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant Soil 376(1/2), 193–209 (2014)CrossRefGoogle Scholar
  32. 32.
    Xia, T., Wu, W.B., Zhou, Q.B.: Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing. Trans. Chin. Soc. Agric. Eng. 29(3), 139–147 (2013)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Chunlan Zhang
    • 1
  • Fuquan Tang
    • 2
  • Heli Li
    • 3
    Email author
  • Guijun Yang
    • 3
  • Haikuan Feng
    • 3
  • Chang Liu
    • 3
  1. 1.College of Architecture EngineeringShandong Xiehe UniversityJinanChina
  2. 2.College of GeomaticsXi’an University of Science and TechnologyXi’anChina
  3. 3.National Engineering Research Center for Information Technology in AgricultureBeijingChina

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