Advertisement

New NNI Model in Winter Wheat Based on Hyperspectral Index

  • Wang Jianwen
  • Li Zhenhai
  • Xu Xingang
  • Zhu Hongchun
  • Feng Haikuan
  • Liu Chang
  • Gan Ping
  • Xu Xiaobin
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)

Abstract

Nitrogen nutrition index (NNI) can monitor winter wheat nitrogen status precisely. Current studies by remote sensing data are to construct the above-ground biomass (AGB) and plant nitrogen concentration (PNC) with spectral indices, respectively, and then substitute them into established NNI equation. This leads to an accumulation of unavoidable error. Therefore, the objective in the study was to construct a direct NNI equation with remote sensing data to reduce this error. Field measurements data including AGB, PNC and canopy hyperspectral at different winter wheat growth stages during 2012/2013, 2013/2014, 2014/2015, 2015/2016 growing seasons in Beijing, China were collected. This study was endeavored to establish a vegetation index critical N dilution curve (Nvic) with two different spectral indices, RTVI (Red edge Triangular Vegetation Index) and NDVI/PPR (the ratio of the normalized difference vegetation index to the plant pigment ratio), which are sensitive to AGB and PNC, respectively. The vegetation index NNI (NNIvi) was calculated from the ratio between the NDVI/PPR and Nvic. Results showed that (1) Nvic can be described by an equation, Nvic = 1106.4(VIRTVI)−1.512, where RTVI ranged from 2.39 to 22.14; the determination coefficient (R2) was 0.57; (2) The NNI based on the above Nvic dilution curve was in good accordance with the classical NNI, with the root mean square error (RMSE), normalized RMSE (nRMSE) and normalized average error (NAE) of 0.194, 22%, and 11%, respectively. The critical nitrogen dilution model constructed in this study was available for winter wheat nitrogen status monitoring. Thus, this study offers a new method which was suitable and convenient for estimating the NNI of the winter wheat and it can reduce quadric error for constructing NNI through indices directly instead of inversing AGB and PNC.

Keywords

Winter wheat NNI Spectral indices The critical N dilution curve 

Notes

Acknowledgments

This research was funded by the National Natural Science Foundation of China (Grant no. 61661136003, 41471285,41601346) and National Key Technologies of Research and Development Program (2017YFD0201501, 2016YFD0300603-5).

References

  1. 1.
    Chen, P.: A comparison of two approaches for estimating the wheat nitrogen nutrition index using remote sensing. Remote Sens. 7(4), 4527–4548 (2015)CrossRefGoogle Scholar
  2. 2.
    Zhao, B., Yao, X., Tian, Y.C.: New critical nitrogen curve based on leaf area index for winter wheat. Agron. J. 106(2), 379 (2014)CrossRefGoogle Scholar
  3. 3.
    Yao, X., Ata-Ul-Karim, S.T., Zhu, Y., et al.: Development of critical nitrogen dilution curve in rice based on leaf dry matter. Eur. J. Agron. 55(2), 20–28 (2014)CrossRefGoogle Scholar
  4. 4.
    Schröder, J.J., Neeteson, J.J., Oenema, O., Struik, P.C.: Does the crop or the soil indicate how to save nitrogen in maize production? Reviewing the state of the art. Field Crop Res. 66, 151–164 (2000)CrossRefGoogle Scholar
  5. 5.
    He, Z., Qiu, X., Ataulkarim, S.T., et al.: Development of a critical nitrogen dilution curve of double cropping rice in South China. Front. Plant Sci. 8, 638 (2017)CrossRefGoogle Scholar
  6. 6.
    López-Bellido, L., López-Bellido, R.J., Redondo, R.: Nitrogen efficiency in wheat under rainfed Mediterranean conditions as affected by split nitrogen application. Field Crop Res. 94, 86–97 (2005)CrossRefGoogle Scholar
  7. 7.
    Xia, T., Miao, Y., Wu, D., et al.: Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote Sens. 8(7), 605 (2016)CrossRefGoogle Scholar
  8. 8.
    Padilla, F.M., Peña-Fleitas, M.T., Gallardo, M., et al.: Determination of sufficiency values of canopy reflectance vegetation indices for maximum growth and yield of cucumber. Eur. J. Agron. 84, 1–15 (2017)CrossRefGoogle Scholar
  9. 9.
    Ata-Ul-Karim, S.T., Liu, X., Lu, Z., et al.: In-season estimation of rice grain yield using critical nitrogen dilution curve. Field Crops Res. 195, 1–8 (2016)CrossRefGoogle Scholar
  10. 10.
    Lemaire, G., Marie-Hélène, J., François, G.: Diagnosis tool for plant and crop N status in vegetative stage theory and practices for crop N management. Eur. J. Agron. 28, 614–624 (2008)CrossRefGoogle Scholar
  11. 11.
    Chen, P.F., Haboudane, D., Tremblay, N., et al.: New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens. Environ. 114(9), 1987–1997 (2010)CrossRefGoogle Scholar
  12. 12.
    Cilia, C., Panigada, C., Rossini, M., et al.: Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sens. 6(7), 6549–6565 (2014)CrossRefGoogle Scholar
  13. 13.
    Huang, S., Miao, Y., Zhao, G., et al.: Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sens. 7(8), 10646–10667 (2015)CrossRefGoogle Scholar
  14. 14.
    Bremner, J.M., Mulvancy, C.S.: Nitrogen-total. In: Page, A.L. (ed.) Methods of Soil Analysis, Part II, pp. 595–624. American Society of Agronomy, Madison (1982)Google Scholar
  15. 15.
    Chen, P.F., Nicolas, T., Wang, J.H., et al.: New index for crop canopy fresh biomass estimation. Spectrosc. Spectral Anal. 30(2), 512 (2010)Google Scholar
  16. 16.
    Jin, X., Li, Z., Feng, H., et al.: Newly combined spectral indices to improve estimation of total leaf chlorophyll content in cotton. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(11), 4589–4600 (2014)CrossRefGoogle Scholar
  17. 17.
    Zhao, B., Tahir, A.U.K.S., Yao, X., et al.: A new curve of critical nitrogen concentration based on spike dry matter for winter wheat in Eastern China. PLoS ONE 11(10), e0164545 (2016)CrossRefGoogle Scholar
  18. 18.
    Justes, E., Mary, B., Meynard, J.M., et al.: Determination of a critical nitrogen dilution curve for winter wheat crops. Ann. Bot. 74(4), 397–407 (1994)CrossRefGoogle Scholar
  19. 19.
    Haiying, L., Hongchun, Z.: Hyperspectral characteristic analysis for leaf nitrogen content in different growth stages of winter wheat. Appl. Opt. 55(34), D151 (2016)CrossRefGoogle Scholar
  20. 20.
    Cilia, C., Panigada, C., Rossini, M., et al.: Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing 6(7), 6549–6565 (2014)CrossRefGoogle Scholar
  21. 21.
    Eitel, J.U.H., Magney, T.S., Vierling, L.A., et al.: LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Res. 159(159), 21–32 (2014)CrossRefGoogle Scholar
  22. 22.
    Ata-Ul-Karim, S.T., Liu, X., Lu, Z., et al.: Estimation of nitrogen fertilizer requirement for rice crop using critical nitrogen dilution curve. Field Crops Res. 201, 32–40 (2017)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Wang Jianwen
    • 1
    • 2
    • 3
    • 4
    • 5
  • Li Zhenhai
    • 2
    • 3
    • 4
    • 5
  • Xu Xingang
    • 2
    • 3
    • 4
    • 5
  • Zhu Hongchun
    • 1
  • Feng Haikuan
    • 2
    • 3
    • 4
    • 5
  • Liu Chang
    • 2
    • 3
    • 4
    • 5
  • Gan Ping
    • 1
    • 2
    • 3
    • 4
    • 5
  • Xu Xiaobin
    • 1
    • 2
    • 3
    • 4
    • 5
  1. 1.College of GeomaticsShandong University of Science and TechnologyQingdaoChina
  2. 2.Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. ChinaBeijing Research Center for Information Technology in AgricultureBeijingChina
  3. 3.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  4. 4.Key Laboratory for Information Technologies in AgricultureThe Ministry of AgricultureBeijingChina
  5. 5.Beijing Engineering Research Center of Agricultural Internet of ThingsBeijingChina

Personalised recommendations