Abstract
ABSTRACT This study aimed to predict soil properties using visible–near infrared (VIS-NIR) spectroscopy combined with partial least square regression (PLSR) modeling. Special emphasis was given to evaluating effect of pre-processing methods on prediction accuracy and important wavelengths. A total of 114 samples were collected and involved in chemical and spectral analyzes. PLSR model of each soil property was calibrated for all pre-processing methods using all samples, and leave-one-out cross-validation was used to make comparisons between them. Then, PLSR model of each best pre-processing method was calibrated using a 75% of all samples and correspondingly validated with the remaining a 25%. Model accuracy was evaluated based on coefficient of determination (R2), root mean-squared errors (RMSE), and residual prediction deviations (RPD). The high correlation coefficients were found between the tested soil properties and reflectance spectra. The pre-processing methods considerably improved prediction accuracy and filtering methods outperformed linearization methods, and the latter outperformed normalization methods. The performance of cross-validation, calibration and independent validation was similar. An excellent prediction (RPD>2.5) model was obtained for soil organic carbon (SOC) and calcium-carbonate (CaCO3), good quantitative (2.0< RPD<2.5) prediction for sand, silt, and clay, fair prediction (1.4< RPD<1.8) for pH, and poor prediction (1.0< RPD<1.4) for hygroscopic water content (WC). Important wavelengths varied depending on soil property, but some wavelengths were common. This study can be a precursor to building a pioneering soil spectral database, calibrating satellite data, and hyperspectral image mapping of soils as well as digital soil mapping, environmental, and erosion modeling in the Caucasus Mountains.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have