Soil total nitrogen estimation of alpine grassland using visible/near-infrared spectra: a comparison of multivariate techniques with different spectral transformations

Abstract

Rapid, precise, and quantitative assessment of soil total nitrogen (TN) of alpine grassland is crucial for monitoring the environmental conditions of grasslands. The right choice of preprocessing and calibration methods may improve the model performance of soil property estimation in visible/near infrared reflectance spectroscopy (VNIRS). VNIRS (350 to 2500 nm) measurements were made of 80 soil samples from the northeast part of the Tibetan Plateau. To reduce the signal noise, the Savitzky–Golay technique was implemented and six spectral transformations were evaluated with the aim of identifying the best transformation for predicting soil TN. The performance of three linear calibration models, namely, stepwise multiple linear regression, principal component regression, partial least squares regression (PLSR), and a combination of these models with a nonlinear back propagation neural network (BPNN) was tested for accuracy in the measurement of soil TN. Our results indicate the following: (1) models using the derivative transformations of spectra as inputs perform better than those using nonderivative transformations, and the first derivative of the reciprocal of reflectance [(1 / R) ′ ] is the best spectral transformation for soil TN estimation; (2) models based on the PLSR and companion BPNN-latent variables (LVs) yield better predictions and smallest errors of soil TN. We demonstrate that (1 / R) ′ is the best spectral transformation and consistently improves the estimation models of soil TN. Among multivariate techniques, the BPNN-LVs is recommended for estimation of soil TN with great accuracy.