Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions

Abstract

The common methods of determining soil carbon (C), nitrogen (N) and their isotopic compositions (δ13C and δ15N) are expensive and time-consuming. Therefore, alternative low-cost and rapid methods are sought to address this issue. This study aimed to investigate the potential of hyperspectral image analysis to predict soil total carbon (TC), total nitrogen (TN), δ13C and δ15N. Hyperspectral images were captured from 96 ground soil samples using a laboratory-based visible to near-infrared (VNIR) hyperspectral camera in the spectral range of 400–1000 nm. Partial least squares regression (PLSR) models were developed to correlate the values of TC, TN, δ13C and δ15N, obtained from isotope ratio mass spectrometry method, with their spectral reflectance. The developed models provided acceptable predictions with high coefficient of determination (R2c) and low root mean square error (RMSEc) of calibration set for TC (R2c = 0.82; RMSEc = 1.08%), TN (R2c = 0.87; RMSEc = 0.02%), δ13C (R2c = 0.82; RMSEc = 0.27‰) and δ15N (R2c = 0.90; RMSEc = 0.29‰). The prediction abilities of the models were then evaluated using the spectra of an external test set (24 samples). The models provided excellent predictions with high R2t and ratio of performance to deviation (RPD) of test set for TC (R2t = 0.76; RPD = 2.02), TN (R2t = 0.86; RPD = 2.08), δ13C (R2t = 0.80; RPD = 2.00) and δ15N (R2t = 0.81; RPD = 1.94). The results indicated that the laboratory-based hyperspectral image analysis has the potential to predict soil TC, TN, δ13C and δ15N.