Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy

Abstract

Reflectance spectroscopy provides an alternate method to non-destructively characterize key soil properties. Different approaches, including chemometrics techniques or specific absorption features, have been proposed to estimate soil properties from visible and near-infrared (VNIR, 400-1200 nm) and shortwave infrared (SWIR, 1200-2500 nm) reflectance domains. The main goal of this study was to test the performance of two distinct methods for soil texture estimation by VNIR-SWIR reflectance measurements: i) the Continuum Removal (CR) technique that was used to correlate specific spectral absorption features with clay, silt and sand content, and ii) the Partial Least-Squares Regression (PLSR) method, which is a classical statistical multivariate technique, that uses the full-spectrum data. At this aim, the surface reflectance of 100 soil samples collected from different sites in Sicily and covering a wide range of textures were measured in laboratory using an ASD FieldSpec Pro spectroradiometer (350-2500 nm). The results of our work indicated that the PLSR technique performed better than the CR approach. Particularly, the assessment of soil texture accuracy performed using root mean squared error (RMSE) and coefficient of determination (R2) showed that the CR approach allowed to obtain a moderate prediction only for the clay texture fraction. Differently, using PLSR technique, the levels of accuracy resulted high for the clay fraction (RMSE=5.8%, R2=0.87) and satisfactory for the sand (RMSE=7.7%, R2=0.80) and silt fractions (RMSE=7.2%, R2=0.60). Moreover the use of PLSR technique allowed to establish the “key wavelengths” of the investigated spectrum range that should be considered “essential” for the prediction of soil textures, suggesting the optimal settings for airborne or satellite sensors usable in the future for accurate mapping of soil textures.