TOWARDS DECOMPOSING THE EFFECTS OF FOLIAR NITROGEN CONTENT AND CANOPY STRUCTURE ON RICE CANOPY SPECTRAL VARIABILITY THROUGH MULTI-SCALE SPECTRAL ANALYSIS

The effect of canopy structure on the remote sensing of foliar nitrogen content has been debated in recent years, due to the uncertain mechanism of estimating foliar nitrogen content through canopy reflectance in the near-infrared region. Although this effect was investigated using the radiative transfer modeling of canopy structural influence, the complicated modeling implementation is still of limited practical use and does not make full use of the spectral details in hyperspectral data. This study proposes to decompose the spectral responses to variations in canopy structure and foliar nitrogen content using a multi-scale spectral analysis tool, called continuous wavelet analysis (CWA). Our results on a rice field-plot experiment demonstrated that the leaf nitrogen content (LNC) were best correlated to the wavelet feature (730 nm, scale 4) with a r(2) value of 0.62. The wavelet feature (730 nm, scale 6), which was represented with the same wavelength but a higher scale, exhibited strong correlation with the leaf area index (LAI) (r(2)=0.80). These two wavelet features characterized spectral variation at different scales and could serve as indicators for separating the spectral effects of LAI and LNC. The findings suggest the wavelet tool is promising for better understanding the effect of canopy structure on the spectroscopic estimation of foliar nitrogen and for building structure-insensitive models for LNC prediction.