Digital mapping of a soil profile

Abstract

Summary: Detailed information on soil profiles is required for site characterization, soil mapping, studies on pedogenesis, soil–landscape modelling and soil classification. In this study, we used digital soil mapping techniques to map the profile wall of an Alfisol (90-cm depth × 100-cm width). Geochemical data (sampled at 10 cm × 10 cm) and digital images (resolution 1 cm × 1 cm) were used as input data in models that predicted a range of properties across the soil profile. Fuzzy c-means clustering was applied to the profile and colour maps, and a confusion index was calculated. The colour coordinates were strongly correlated with SOC (soil organic carbon), sand and silt contents, and weathering indices. Random forest using the RGB model (R 2 = 0.57, RMSE (root mean square error) = 2.41) showed less accurate prediction of SOC content than random forest using the CIE L*a*b* (R 2 = 0.84, RMSE = 1.57) and HSV, hue, saturation, chroma (R 2 = 0.85, RMSE = 1.46) models. Detailed profile maps showed considerable within-horizon variation and identified animal holes, roots and limestone fragments. Colour and profile maps of soil properties and weathering indices matched field-delineated soil horizons. Clusters obtained by soil properties and weathering indices represented the field-delineated horizons and had the largest overall accuracy (P = 0.85 and 0.86, respectively). The confusion index showed that the horizons had wavy and somewhat irregular boundaries. We conclude that colour coordinates are useful for predicting and mapping soil properties in soil profiles. Random forest can be used to produce high-resolution soil profile maps, and fuzzy c-means clustering is useful for delineating soil horizons. Highlights: We investigated the use of digital images in soil horizon delineation and within soil horizon variation We used various colour models to map soil profile properties and define horizons Colour and profile maps of soil properties and weathering indices matched field delineated soil horizons. Random forest can produce high-resolution soil profile maps, and fuzzy c-means clustering can delineate soil horizons.