Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis

Dong Li; Xue Wang; Hengbiao Zheng; Kai Zhou; Xia Yao; Yongchao Tian; Yan Zhu; Weixing Cao; Tao Cheng

doi:10.1186/s13007-018-0344-1

Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis

Plant Methods. 2018 Aug 29:14:76. doi: 10.1186/s13007-018-0344-1. eCollection 2018.

Authors

Dong Li¹, Xue Wang¹, Hengbiao Zheng¹, Kai Zhou¹, Xia Yao¹, Yongchao Tian¹, Yan Zhu¹, Weixing Cao¹, Tao Cheng¹

Affiliation

¹ National Engineering and Technology Center for Information Agriculture (NETCIA), Key Laboratory of Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095 China.

Abstract

Background: The visible and near infrared region has been widely used to estimate the leaf nitrogen (N) content based on the correlation of N with chlorophyll and deep absorption valleys of chlorophyll in this region. However, most absorption features related to N are located in the shortwave infrared (SWIR) region and the physical mechanism of leaf N estimation from fresh leaf reflectance spectra remains unclear. The use of SWIR region may help us reveal the underlying mechanism of casual relationships and better understand the spectral responses to N variation from fresh leaf reflectance spectra. This study combined continuous wavelet analysis (CWA) and water removal technique to improve the estimation of N content and leaf mass per area (LMA) by reducing the effect of water absorption and enhancing absorption signals in the SWIR region. The performance of the wavelet-based method was evaluated for estimating leaf N content and LMA of rice and wheat crops from fresh leaf reflectance spectra collected over a 2-year field experiment and compared with normalization difference (ND)-based spectral indices.

Results: The LMA and area-based N content (N_area) exhibited better correlations with the determined wavelet features derived from the water-removed (WR) spectra (LMA: R² = 0.71, N_area: R² = 0.77) than those from the measured reflectance (MR) spectra (LMA: R² = 0.62, N_area: R² = 0.64). The wavelet features performed remarkably better than the optimized ND indices for the estimations of LMA and N_area with MR spectra or WR spectra. Based on the best estimations of LMA and N_area with wavelet features from WR spectra, the mass-based N content (N_mass) could be retrieved with a high accuracy (R² = 0.82, RMSE = 0.32%) in the indirect way. This accuracy was higher than that for N_mass obtained in the direct use of a single wavelet feature (R² = 0.68, RMSE = 0.42%).

Conclusions: The enhancement of absorption features in the SWIR region through the CWA applied to water-removed (WR) spectra was able to improve the spectroscopic estimation of leaf N content and LMA as compared to that obtained with the reflectance spectra of fresh leaves. The success in estimating LMA and N with this method would advance the spectroscopic estimations of grain quality parameters for staple crops and individual dry matter constituents for various vegetation types.

Keywords: Nitrogen content; Shortwave infrared; Water-removed; Wavelet analysis.