Mapping cation exchange capacity using a Veris-3100 instrument and invVERIS modelling software

• Published in: The Science of the total environment 2017-12, Vol.599-600, p.2156-2165
• Main Authors: Koganti, T., Moral, F.J., Rebollo, F.J., Huang, J., Triantafilis, J.
• Format: Article
• Language: English
• Subjects: Cation exchange capacity; Digital soil mapping; Electrical conductivity; Proximal soil sensing; Quasi-3d inversion; Shrink-swell potential
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2017.05.074

The cation exchange capacity (CEC) is one of the most important soil properties as it influences soil's ability to hold essential nutrients. It also acts as an index of structural resilience. In this study, we demonstrate a method for 3-dimensional mapping of CEC across a study field in south-west Spain. We do this by establishing a linear regression (LR) between the calculated true electrical conductivity (σ - mS/m) and measured CEC (cmol(+)/kg) at various depths. We estimate σ by inverting Veris-3100 data (ECa - mS/m) collected along 47 parallel transects spaced 12m apart. We invert the ECa data acquired from both shallow (0–0.3m) and deep (0–0.9m) array configurations, using a quasi-three-dimensional inversion algorithm (invVeris V1.1). The CEC data was acquired at 40 locations and from the topsoil (0–0.3m), subsurface (0.3–0.6m) and subsoil (0.6–0.9m). The best LR between σ and CEC was achieved using S2 inversion algorithm using a damping factor (λ)=18. The LR (CEC=1.77+0.33×σ) had a large coefficient of determination (R2=0.89). To determine the predictive capability of the LR, we validated the model using a cross-validation. Given the high accuracy (root-mean-square-error [RMSE]=1.69 cmol(+)/kg), small bias (mean-error [ME]=−0.00cmol(+)/kg) and large coefficient of determination (R2=0.88) and Lin's concordance (0.94), between measured and predicted CEC and at various depths, we conclude we were well able to predict the CEC distribution in topsoil and the subsurface. However, the predictions made in the subsoil were poor due to limited data availability in areas where ECa changed rapidly from small to large values. In this regard, improvements in prediction accuracy can be achieved by collection of ECa in more closely spaced transects, particularly in areas where ECa varies over short spatial scales. [Display omitted] •Veris-3100 data firstly inverted by a quasi-3d inversion algorithm.•Measured soil CEC strongly correlated with inverted electrical conductivity.•A LR model established to predict soil CEC with inverted electrical conductivity.•Soil CEC mapped at various depths (0–0.9m) across a 16-ha field using the LR.