Assessment of calibration methods for nitrogen estimation in wet and dry soil samples with different wavelength ranges using near-infrared spectroscopy

Abstract Choosing an appropriate wavelength range, extracting optimal wavelength variables, and selecting suitable statistical analysis methods are of great importance for improving the prediction accuracy of soil nitrogen (N) with near-infrared (NIR) spectroscopy. In this study, the prediction performances of two different wavelength ranges, a short wavelength range (SWR) of 900–1,700 nm and a full wavelength range (FWR) of 900–2,500 nm, are evaluated for the measurement of soil N content. Spectral scanning is performed on wet and dry-sieve soil samples to assess the effect of moisture on the prediction performance of soil N. Two calibration methods, a commonly used linear partial least squares regression (PLSR) and a nonlinear back propagation neural network (BPNN), are used. To understand if it is possible to reduce the number of wavelength variables without decreasing prediction accuracy, we introduce a successive projection algorithm (SPA) to extract wavelength variables that are minimally redundant. The results show that models developed within FWR outperform those developed within SWR, regardless of wet or dry soil conditions, which can be attributed to the presence of more spectral information related to soil N in FWR. Compared with PLSR, BPNN is a better choice for predicting soil N, because BPNN models provide higher accuracy. The best prediction performance is achieved by BPNN method in FWR using a SPA with Rp2 = 0.93, RMSEP = 0.0297% and RPD = 4.00 of wet soil samples, and Rp2 = 0.99, RMSEP = 0.0132% and RPD = 8.76 of dry soil samples. Additionally, we demonstrate that using the SPA algorithm significantly reduces the number of wavelength variables while maintaining high prediction accuracy. The characteristic wavelengths selected by the SPA algorithm follow the principle of material spectral absorption. It is worth noting that dry soil conditions lead to superior performance over wet soil conditions for the measurement of soil N, which can be attributed to the removal effect of moisture content from the wavelength region and the utilization of important absorption features. However, even under wet soil conditions, the simplified calibration models based on selected SPA variables obtain excellent quantitative prediction using the BPNN method in the SWR range, with Rp2 = 0.91, RMSEP = 0.0305%, and RPD = 3.47. It is important to expand large-scale detection applications for the measurement of soil N.