Hyperspectral Estimation of Leaf Area Index of Winter Wheat Based on Akaike’s Information Criterion

  • Haikuan Feng
  • Fuqin YangEmail author
  • Guijun Yang
  • Haojie Pei
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 509)


Leaf Area Index (LAI) is an important parameter for assessing the crop growth and winter wheat yield prediction. The objectives of this study were(1) to establish and verify a model for the LAI of winter wheat, where the regression models, extended the Grey Relational Analysis (GRA), Akaike’s Information Criterion (AIC), Least Squares Support Vector Machine (LSSVM) and (ii) to compare the performance of proposed models GRA-LSSVM-AIC. Spectral reflectance of leaves and concurrent LAI parameters of samples were acquired in Tongzhou and Shunyi districts, Beijing city, China, during 2008/2009 and 2009/2010 winter wheat growth seasons. In the combined model, GRA was used to analyse the correlation between vegetation index and LAI, LSSVM was used to conduct regression analysis according to the GRA for different vegetation index order of the number of independent variables, AIC was used to select the optimal models in LSSVM models. Our results indicated that GRA-LSSVM-AIC optimal models came out robust LAI evaluation (R = 0.81 and 0.80, RMSE = 0.765 and 0.733, individually). The GRA-LSSVM-AIC had higher applicability between different years and achieved prediction of LAI estimation of winter wheat between regional and annual levels, and had a wide range of potential applications.


Leaf area index Akaike’s Information Criterion Grey Relational Analysis Least Squares Support Vector Machine 


  1. 1.
    Wang, J., Zhao, C., Huang, W., et al.: Quantitative Remote Sensing and its Application in Agriculture. Thomson Learning Press, Beijing (2008)Google Scholar
  2. 2.
    Pinter, P.J., et al.: Remote sensing for crop management. Photogramm. Eng. Remote Sens. 69, 647–664 (2003)CrossRefGoogle Scholar
  3. 3.
    Maki, M., Homma, K.: Empirical regression models for estimating multiyear leaf area index of rice from several vegetation indices at the field scale. Remote Sens. 6, 4764–4779 (2014)CrossRefGoogle Scholar
  4. 4.
    Potithep, S., Nagai, S., Nasahara, K.N., Muraoka, H., Suzuki, R.: Two separate periods of the LAI–VIs relationships using in situ measurements in a deciduous broadleaf forest. Agric. For. Meteorol. 169, 148–155 (2013)CrossRefGoogle Scholar
  5. 5.
    Inoue, Y., Iwasaki, K.: Spectral estimation of radiation absorptance and leaf area index in corn canopies as affected by canopy architecture and growth stage. Jpn. J. Crop Sci. 60, 578–580 (1991)CrossRefGoogle Scholar
  6. 6.
    Li, F., et al.: Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precis. Agric. 11, 335–357 (2010)CrossRefGoogle Scholar
  7. 7.
    Xie, Q., Huang, W., Liang, D., et al.: Comparative study on remote sensing invertion methods for estimating winter wheat leaf area index. Spectrosc. Spectr. Anal. 34(5), 489–493 (2014)Google Scholar
  8. 8.
    Lin, H., Liang, L., Zhang, L., et al.: Wheat leaf area index inversion with hyperspectral remote sensing based on support vector regression algorithm. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 29(11), 139–146 (2013)Google Scholar
  9. 9.
    Liang, H., Yang, M., Zhang, L., et al.: Chlorophyll content inversion with hyperspectral technology for wheat canopy based on support vector regression algorithm. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 28(20), 162–171 (2012)Google Scholar
  10. 10.
    Cai, Q., Jiang, J., Tao, L., et al.: Estimation of winter wheat leaf area index with joint principal component analysis and least squares support vector model. J. Triticeae Crop. 34(9), 1292–1296 (2014)Google Scholar
  11. 11.
    Jin, X.L., Xu, X.G., Wang, J.H., et al.: Hyperspectral estimation of leaf water content for winter wheat based on grey relational analysis. TSpectroscopy Spectr. Anal. 32(11), 103–3106 (2012)Google Scholar
  12. 12.
    Jin, X., Xu, X., Song, X., et al.: Estimation of leaf water content in winter wheat using grey relational analysis – partial least squares modeling with hyperspectral data. Agron. J. 105(5), 1085–1392 (2013)CrossRefGoogle Scholar
  13. 13.
    Xia, T., Wu, W., Zho, Q., et al.: Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 29(3), 139–147 (2013). (in Chinese with English abstract)Google Scholar
  14. 14.
    Lee, K.-S., Cohen, W.B., Kennedy, R.E., Maiersperger, T.K., Gower, S.T.: Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sens. Environ. 91, 508–520 (2004)CrossRefGoogle Scholar
  15. 15.
    Shunfa, J., Yibao, W., Yuli, X., et al.: AIC principle and its application in the polynomial models of the crop yield. Acta Agric. Shanghai 1(3), 73–78 (1985)Google Scholar
  16. 16.
    Li, Z., Xu, X., Jin, X., et al.: Remote sensing prediction of winter wheat protein content based on nitrogen translocation and GRA-PLS method. Sci. Agric. Sin. 47(19), 3780–3790 (2014)Google Scholar
  17. 17.
    Akaike, H.: Problem of control and information. In: Petrov, B.N., Csaki, F., (eds.) Proceedings of 2nd International Symposium on Information Theory, pp. 267–281. Akademina kiado, Budapest (1973)Google Scholar
  18. 18.
    Deering, D.W., Harlan, J.C.: Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. Texas A & M University, Remote Sensing Center (1974)Google Scholar
  19. 19.
    Pearson, R.L., Miller, D.L.: Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In: Proceedings of the English International Symposium on Remote Sensing of Environment, vol. 2, pp. 1375–1381 (1972)Google Scholar
  20. 20.
    Rondeaux, G., Steven, M., Baret, F.: Optimization of soil-adjusted vegetation indices. Remote. Sens. Environ. 55(2), 95–107 (1996)CrossRefGoogle Scholar
  21. 21.
    Haboudane, D., Miller, J.R., Tremblay, N., et al.: Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81(2/3), 416–426 (2002)CrossRefGoogle Scholar
  22. 22.
    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(2–3), 337–354 (2002)CrossRefGoogle Scholar
  23. 23.
    Gitelson, 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
  24. 24.
    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
  25. 25.
    Penuelas, J., Baret, F., Filella, I.: Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31(2), 221–230 (1995)Google Scholar
  26. 26.
    Gamon, J.A., Penuelas, J., Field, C.B.: A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficienc. Remote Sens. Environ. 41(1), 35–44 (1992)CrossRefGoogle Scholar
  27. 27.
    Haboudane, D., Miller, J.R., Pattey, E., et al.: 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. 90, 337–352 (2004)CrossRefGoogle Scholar
  28. 28.
    Qi, J., Chehbouni, A., Huete, A.R., et al.: A modified soil adjusted vegetation index. Remote Sens. Environ. 48(2), 119–126 (1994)CrossRefGoogle Scholar
  29. 29.
    Chen, J.: Evaluation of vegetation indices and modified simple ratio for boreal applications. Can. J. Remote. Sens. 22, 229–242 (1996)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Haikuan Feng
    • 1
  • Fuqin Yang
    • 1
    • 2
    • 3
    Email author
  • Guijun Yang
    • 1
  • Haojie Pei
    • 1
    • 2
  1. 1.Beijing Research Center for Information Technology in AgricultureBeijingChina
  2. 2.College of Geoscience and Surveying EngineeringChina University of Mining and TechnologyBeijingChina
  3. 3.College of Civil EngineeringHenan Institute of EngineeringZhengzhouChina

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