Journal of Integrative Agriculture ›› 2019, Vol. 18 ›› Issue (7): 1562-1570.DOI: 10.1016/S2095-3119(19)62686-9

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  • 收稿日期:2019-01-03 修回日期:2019-04-12 出版日期:2019-07-01 发布日期:2019-07-01

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

DUAN Dan-dan1, 2, 3, 4, ZHAO Chun-jiang2, 3, 4, LI Zhen-hai2, 3, 4, YANG Gui-jun2, 3, 4, ZHAO Yu1, 2, 3, 4, QIAO Xiao-jun2, 3, 4, ZHANG Yun-he2, 3, 4, ZHANG Lai-xi2, 3, 4, YANG Wu-de1        
  

  1. 1 Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu 030801, P.R.China
    2 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
    3 Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs/Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
    4 Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, P.R.China
  • Received:2019-01-03 Revised:2019-04-12 Online:2019-07-01 Published:2019-07-01
  • Contact: Correspondence YANG Wu-de, Tel: +86-10-51503810, E-mail: sxauywd@126.com
  • About author:DUAN Dan-dan, E-mail: duandd@nercita.org.cn;
  • Supported by:
    This study was supported by the Natural Science Foundation of Beijing Academy of Agriculture and Forestry Sciences (BAAFS), China (QNJJ201834), the National Natural Science Foundation of China (41471285 and 41671411), and the National Key R&D Program of China (2017YFD0201501).

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Abstract:

The use of remote sensing to monitor nitrogen (N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden.  In this study, we model the total leaf N concentration (TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution (VND).  The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model.  The results show that: (1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer.  (2) The effective layer for remote sensing detection varied at different growth stages.  The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively.  (3) The TLNC model considering the VND has high predicting accuracy and stability.  For models based on the greenness index (GI), mND705 (modified normalized difference 705), and normalized difference vegetation index (NDVI), the values for the determining coefficient (R2), and normalized root mean square error (nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively.  Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.

Key words: nitrogen concentration ,  hyperspectral ,  vertical nitrogen distribution ,  winter wheat