Evaluating different methods for grass nutrient estimation from canopy hyperspectral reflectance

Junjie Wang, Tiejun Wang, Andrew K. Skidmore, Tiezhu Shi, Guofeng Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
12 Downloads (Pure)


The characterization of plant nutrients is important to understand the process of plant growth in natural ecosystems. This study attempted to evaluate the performances of univariate linear regression with various vegetation indices (VIs) and multivariate regression methods in estimating grass nutrients (i.e., nitrogen (N) and phosphorus (P)) with canopy hyperspectral reflectance. Synthetically considering predictive accuracy, simplicity, robustness and interpretation, the successive projections algorithm coupled with multiple linear regression (SPA-MLR) method was considered optimal for grass nutrient estimation at the canopy level, when compared with the performances of 12 statistical modeling methods, i.e., univariate linear regression with nine published VIs and three classical multivariate regression methods (stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector regression (SVR)). The simple ratio index (R'730/R'705, R' is derivative reflectance) model had comparable performance to SPA-MLR model for P estimation. SPA-MLR provided comparable prediction accuracies with only three first derivative spectral bands for N (715, 731 and 2283 nm) and P (714, 729 and 1319 nm) estimations, compared with PLSR and SVR models, which used the full spectrum. Moreover, SPA-MLR provided robust prediction with the lowest bias values for N (-0.007%) and P (0.001%) estimations, and the fitting line between predicted and measured values was closer to the 1:1 line than the other models. Finally, most of the bands selected by SPA-MLR indirectly relate to foliar chlorophyll content, which suggests good physical interpretation.

Original languageEnglish
Pages (from-to)5901-5917
Number of pages17
JournalRemote Sensing
Issue number5
Publication statusPublished - May 2015
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2015. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • canopy level
  • grass nutrients
  • hyperspectral reflectance
  • statistical modeling


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