Diffuse reflectance spectroscopy and digital soil mapping for assessing soil-associated off-road vehicle mobility risk

Abstract

Soil attributes such as granulometric fractions and Atterberg limits (LL: liquid limit, PL: plastic limit, and PI: plasticity index) are needed to assess off-road vehicle mobility (OVM) risks. Parameters describing these attributes are generally measured in soil samples collected from a few locations through cumbersome laboratory methods. Although diffuse reflectance spectroscopy (DRS) can rapidly yield estimates for soil attributes in samples collected from specific locations and digital soil mapping (DSM) can transform such discrete measurements into spatially-continuous inference systems, these two technologies are rarely used for assessing OVM risks. In this study, we combined the DRS and DSM approaches for deriving spatially-continuous estimates for the key vehicle mobility parameters (gravel, sand, and fine particles; Cu: coefficients of uniformity; Cc: coefficient of curvature; LL; and PI) and classified soils using the Unified Soil Classification System (USCS). A total of 204 soil samples were collected from the north-eastern Himalayan state of Sikkim for measuring these parameters along with spectral reflectance over the visible and near-infrared region. Results of the chemometric models in the DRS approach showed that the USCS parameters may be estimated with the coefficient of determination (R2) values as high as 0.72. The fine (<2 mm diameter) fraction spectra provided the best estimates for the Atterberg limits while a combination of spectra collected from fine and coarse (>2 mm diameter) fractions was effective in estimating other granulometric fractions except for sand, which was best estimated using the coarse fraction spectra. With the DSM approach allowing effective mapping of these parameters, a spatially-continuous framework to quantify soil-associated OVM risks was developed for Sikkim for the first time.