Evaluation of Vis-NIR preprocessing combined with PLS regression for estimation soil organic carbon, cation exchange capacity and clay from eastern Croatia

Abstract

This study aimed to evaluate (i) the potential of visible-near-infrared (Vis-NIR) preprocessing techniques combined with a partial least square (PLS) regression to estimate soil organic carbon (SOC), the cation exchange capacity (CEC), and clay content and (ii) the effects of the wavelengths selection on the predictive capability of the PLS models. The study was conducted on a total of 132 topsoil samples (0–25 cm) collected on arable land in Eastern Croatia. Raw reflectance data and six commonly used spectral preprocessing, namely, multiplicative scatter correction (MSC), standard normal variate (SNV), de-trending (DT), continuum removal (CR) and Savitzky–Golay first and second derivative (SG1.der and SG2.der) were compared to identify the best for predicting selected soil properties. The PLS models calibrated with the SG1.der preprocessing achieved the best predictive performance for all soil properties compared to other methods used. The R2, RPD and RMSE of the SG1.der -PLS model in the validation mode for SOC, CEC and clay were 0.81, 2.12, 2.57 g C kg−1; 0.78, 1.95, 1.80 cmol(+) kg−1 and 0.83, 2.20, 37.2 g kg−1, respectively. The lowest predictability had PLS models that used soil raw reflectance data. These results indicate that the appropriate choice of Vis-NIR preprocessing can optimize the PLS calibration performance for accurate predicting SOC, CEC and clay in arable soils of the study area. The selection of informative wavelengths (IW) obtained using the PLS regression β-coefficient approach resulted in a highly reduced number of wavelengths in the IW-PLS models. Informative wavelengths in the IW-PLS model for SOC lay in the full Vis-NIR spectrum but mainly in the near-infrared (NIR), whereas for the CEC and clay in the NIR spectrum. In addition, IW-PLS models have a lower number of principal components and almost identical prediction capability relative to models based on full spectra (FS–PLS). Our results suggest that the Vis-NIR spectroscopy is a powerful tool to estimate soil properties rapidly, non-destructively and inexpensively applicable in some real situations e.g., monitoring and updating information on soil properties for precise soil management and soil quality assessment.