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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)

Abstract

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.

Keywords

Leaf nitrogen content (LNC) Remote sensing Winter wheat Comparison 

Notes

Acknowledgments

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).

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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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