The study classified the coastal plain sands of south-eastern Nigeria at the series level and modeled the classification using digital terrain attributes. The study utilized 72 secondary and 12 primary profile pits data generated from 24 and 4 locations (at 3 per location) for classification/modelling and validation respectively. The three profile pits per location represents the three topographic positions of upper, middle and lower slopes. Digital elevation model was also utilized for the generation of terrain attributes. Soil morphological characteristics were coded for suitability in statistical analysis. Hierarchical clustering was utilized in the grouping of the soil into 17 homogeneous groups referred to as soil series. Regression kriging was used to model the predicted soil series within the area covered by coastal plain sands in Akwa Ibom State. The variables that could be used in the modelling of the different classified soil series include Sand Content, aspect, flow accumulation, compound topographic index (CTI), elevation, hill shade, slope, curvature, flow direction, stream power index (SPI), profile curvature, tangential curvature (R2 = 0.21).Out of the 17 soil series classified, 14 was successfully mapped using digital technique. It was observed that 66.7% of the classified soil series were accurately predicted using digital mapping technique. The classifications carried out numerically made use of morphological discrete variables whereas digital used empirically determined continuous variables which could be more accurate. Therefore it could be inferred that the digitally produced soil classification is more accurate and 14 soil series could be identified and mapped in the study area.
Key words: pedogenesis, digital soil mapping, soil series, hierarchical clusters, regression kriging
Digital Mapping of Surface Soil Salinity in Khuzestan Province, Using Regression Kriging
