New Methods for Identifying and Quantifying Soil Redoximorphic Features

Soil redoximorphic features (SRFs) have provided scientists with insight into relative soil moisture for approximately 60 years. More recently, attempts have been made to infer greater detail of soil water regime and correlate saturation periods from qualitative or semi-quantitative measures of SRFs. In the future, it is reasonable to assume increasing information on SRFs may be used to estimate soil hydraulic properties. Current methods for documenting SRFs include field determinations of presence/absence, color description, and visual estimation of quantity and size. Observations under varying light conditions, bias across investigators, and the human inability to quantify additional measures (e.g., shape index, contiguity, fractal dimension, contagion/interspersion) draw attention to needed improvements if SRFs will be increasingly relied on for environmental management decisions. This research presents new methods to increase reliability and information gained from SRFs by digital image capture, standardized Munsell© color identification, and classification and calculation of spatial metrics. Study sites include two cropped fields located in the Central Claypan Major Land Resource Area of northcentral Missouri, USA. Soils included in this research are characterized by a high clay content (>50%) in argillic horizons, consisting largely of smectitic clay minerals. Lateral water movement above the argillic horizon has been identified as a major hydrologic pathway. Fifty pedons (25 per field) have been selected for sampling in order to span a range of depth to argillic horizon and upslope contributing area. This method relies on hydraulic coring and transportation of intact cores to the laboratory. Digital SRF images are captured from replicate 50 cm² areas under controlled lighting and moisture conditions. Further classification of image pixels by Munsell© color and quantification of SRFs allow a greater number of morphologic variables to be documented, then reduced to a subset which may best correlate with variants of terrain analyses including predicted lateral flow movement.