Estimating leaf nitrogen concentration from similarities in fresh and dry leaf spectral bands using a model population analysis framework

ABSTRACT

Fresh leaf spectral reflectance is primarily influenced by leaf water content and structural aspects such as the inter-cellular spaces within the spongy mesophyll, which also interfere with the estimation of the leaf nitrogen content. It is therefore essential to identify spectral bands that are least affected by the above perturbing factors for improving leaf nitrogen estimation for fresh leaves across any landscape. Wavelengths selection plays a vital role in identifying the best spectral features for assessing leaf nitrogen concentration from hyperspectral data of dry and fresh leaves. The primary objective of this study was to determine typical optimal bands for leaf nitrogen estimation from spectra (400–2500 nm) of whole fresh and dry leaves for the same specimens of Eucalyptus grandis. This was achieved via the use of competitive adaptive re-weighted sampling (CARS), and Monte Carlo cross-validation-competitive adaptive re-weighted sampling (MCCV-CARS) band selection approaches. Bands selected (931 nm, 1003 nm, 1027 nm, 1036 nm, 1177 nm, and 1180 nm) via the MCCV-CARS approach yielded the highest estimation accuracy for both fresh predicted coefficient of determination (R²_cal) = 0.82 and predicted root mean square error (RMSE_P) = 0.14) and dry leaves (R²_P = 0.88 and RMSE_P = 0.13) when compared to CARS (2044 nm, 2107 nm, and 2188 nm) only. The identified spectral features could be relevant for assessing leaf nitrogen concentration for different seasons, for example, wet to dry season.

Acknowledgments

The authors would like to thank Mondi Business Paper and the Council for Scientific and Industrial Research for respectively providing logistical and funding support for this project. They would also like to thank Dr Mark Norris-Rogers and Mr Marius du Plessis (Mondi BP) for their assistance during the field data collection. Also, we will like to acknowledge Matlab codes CARS and MCCV-CARS (copyright Hongdong Li, 2011); http://code.google.com/p/carspls.

Disclosure statement

No potential conflict of interest was reported by the authors.