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

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.

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%).

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.