Visible and Near-Infrared Reflectance Spectroscopy for Assessment of Soil Properties in the Caucasus Mountains, Azerbaijan. Issue 16 (7th September 2020)

• Record Type: Journal Article
• Authors: Mammadov, Elton; Denk, Michael; Riedel, Frank; Lewinska, Karolina; Kaźmierowski, Cezary; Glaesser, Cornelia
• Is Part Of: Communications in soil science and plant analysis. Volume 51:Issue 16(2020)
• Page Start: 2111
• Page End: 2136
• Publication Date: 2020-09-07
• Subjects: Soil properties -- VIS-NIR spectroscopy -- partial least squares regression -- the Caucasus Mountains; Soil science -- Periodicals; Plants -- Chemical analysis -- Periodicals; Agricultural chemistry -- Periodicals; 631.405
• DOI: 10.1080/00103624.2020.1820027
• Languages: English
• ISSNs: 0010-3624

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 (R 2 ), 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.