Studying sand component of soil texture using the spectroscopic method

Abstract

The mineral portion of soil is identified by its texture which refers to the relative amounts of sand, silt, and clay in the soil. The sand portion is closely related to soil quality which should be highlighted for environmental modeling operations and digital soil mapping projects. However, the identification, mapping, and monitoring of sand content at a large scale using standard laboratory analysis is laborious, time-consuming, and costly. This study aimed to predict sand content utilizing the proximal soil sensing technology. Thus, following supplementary data layers (geology, pedology, land use, etc.) and stratified randomized sampling method, 128 soil samples were collected from the depth of 20 cm of the soil. The study was conducted using the partial least squares regression (PLSR) method with leave-one-out cross-validation procedure and some preprocessing such as spectra reduction method, Savitzky-Golay smoothing, and first-order derivative algorithms. The definitive calibration model was validated using a standalone validation subset with two and four latent vectors and RP (0.83 and 0.82), R2P (0.68 and 0.67), RMSEP (8.68 and 8.83), RPDP (1.78 and 1.75), and RPIQP (2.45 and 2.41), respectively, that spotted as the most appropriate model for the sand content prediction in the study areas. Eventually, the visible and near-infrared diffuse reflectance spectroscopy (VNIR-DRS) potentiality was proved for sand content estimation. Significantly, it is feasible to upscale the sand prediction process utilizing the principal resulted in model and key spectral domains via airborne/satellite hyperspectral data, which emphatically shows the lab diffuse reflectance spectroscopy (LDRS) importance as a starting point for characterizing the informative optical wavelengths.