LAUSR

Abstract The leaf or canopy reflectance spectra of vegetation have been widely employed in estimating foliar nitrogen (N) concentration; however, they alone may not actually reflect the spectral and detailed information at a sampling plot. In this study, the potential spectral details of Carex ( C. cinerascens ) at a plot scale were derived using discrete wavelet transform, in which a simple operation of addition was employed to combine the reconstructed leaf and canopy reflectance at the fourth decomposition level (named “leaf-canopy d4 reflectance”). Partial least squares regression (PLSR), successive projections algorithm-based multiple linear regression (SPA-MLR) and random forest regression (RFR) models with leaf, canopy and leaf-canopy d4 reflectance were established and validated for foliar N estimation, respectively. The results showed that the PLSR (R 2 CV  = 0.718, determination coefficient of cross-validation; R 2 Val  = 0.743, determination coefficient of independent validation; RPD = 1.91, residual prediction deviation), SPA-MLR (R 2 CV  = 0.709, R 2 Val  = 0.747, RPD = 1.97) and RFR (R 2 CV  = 0.714, R 2 Val  = 0.783, RPD = 2.16) models with leaf-canopy d4 reflectance outperformed their corresponding models with leaf or canopy reflectance. We conclude that the wavelet-based coupling of leaf and canopy reflectance spectra has great potential in the accurate estimation of foliar N concentration. This proposed strategy helps to understand the spectral details of vegetation at a plot scale, providing the potential for improving the plot-based estimation of plant nutrients in grassland, precision agriculture or forestry.