Rapid Detection of Soil Texture Attribute based on Mid-Infrared Spectral Library In Salt Affected Soils of Hungary

Abstract

Quantifying among others the soil's physical properties is essential for the assessment of the diverse soil environmental functions including water balance of soils and pore structure, water erosion and various soil hydraulic properties. The mid-infrared (MIR) spectroscopy is a useful technique to predict soil attributes with high accuracy, efficiency and low cost. In this study, we examined the ability of our MIR soil spectral library in predicting the clay, silt, sand content of salt affected Hungarian soils. This research is part of a project to establish a MIR spectral library in the frame of the Hungarian Soil Information and Mentoring System (SIMS) survey. Salt affected soils type data was extracted from the spectral library then transformation of spectral reflectance values to absorbance values were performed. Moving average filtering method was applied to absorbance spectra before performing principal components analysis. To determine outlier samples and to select the proper samples for model calibration, Mahalanobis distance-based outlier detection method and Kennard-Stone Sampling selection method were applied on the principal component scores. Spectral and reference soil data were combined and split into training and testing datasets. MIR prediction models were built for sand, clay, and silt content using Partial Least Square Regression (PLSR) method. Coefficient determination, root mean square error and ratio performance to deviation were used to assess the models performance. The prediction accuracies of calibration sets for soil physical texture were excellent while the validation results were slightly lower but still with a good level of prediction.