Abstract

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 ( , 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.

Highlights

  • The characterization of plant nutrients, e.g., nitrogen (N) and phosphorus (P), is important to understand the process of plant growth in natural ecosystems [1] and helps to understand the foraging behavior, habitat selection and migration of some animals [2,3]

  • This study aimed to evaluate the performances of univariate linear regression with nine published vegetation indices (VIs), three classical multivariate regression methods (SMLR, partial least squares regression (PLSR) and support vector regression (SVR)) and successive projections algorithm (SPA)-MLR in estimating the nutrients (N and P) of C. cinerascens with canopy hyperspectral reflectance

  • This study compared the performances of univariate linear regression with nine published VIs

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Summary

Introduction

The characterization of plant nutrients, e.g., nitrogen (N) and phosphorus (P), is important to understand the process of plant growth in natural ecosystems [1] and helps to understand the foraging behavior, habitat selection and migration of some animals [2,3]. Over the last four decades, the advances in reflectance spectroscopy, airborne and satellite technology have made it feasible to be more independent of routine wet-chemistry analyses for plant nutrients, and they have provided opportunities for scientists to understand the temporal and spatial changes of plant nutrients at a landscape or regional scale [4,5,6,7,8,9]. Among these studies, univariate regression with vegetation indices (VIs) and multivariate regression methods are commonly used to extract useful information for nutrient characterization.

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