https://doi.org/10.1016/j.geoderma.2015.07.012

Estimation and mapping of soil organic carbon storage in three dimensions for large areas is increasingly in demand for climate change reporting, but such estimates at large spatial scales and different soil depths are generally time consuming and expensive. This paper investigates the potential to reduce the number of required soil observations to one point close to the soil surface by applying negative exponential vertical depth functions of soil carbon distribution. This approach assumes that the carbon content at a depth of 1 m, under relatively stable vegetation conditions, can be functionally related to the concentration at the soil surface in the absence of major recent disturbances (e.g. landslides, soil stock piling, etc.). Vertical distribution functions were developed for 38 soil profiles in the Midlands area of KwaZulu-Natal, South Africa, and normalized using the value observed close to the surface, followed by the grouping of soils with similar vertical distribution patterns. Normalization reduced the number of model parameters and enabled the multiplication of the exponential decline curve characteristics with a soil carbon stock value observed at the surface to represent an adequately represented value of soil carbon distribution to 1 m at that observation point. The integral of the exponential function was used to calculate the soil carbon storage and raster layers were developed for each of the parameters in the integral to map soil carbon storage to 1 m. The exponential decline function adequately fits the soil carbon data and is shown to relate to soil type. The resultant maps predicted soil organic carbon stocks to 1 m in forest plantations and grasslands to vary from 19 to 46 km · m^{−2}.

**Highlights**

• Soils characterized by an average vertical exponential decline in SOC content

• Subsequent normalization of volumetric SOC using the surface value

• SOC decline rates characterized for soil type groups using exponential coefficient k

• Two independent variables (Cv0 and k) isolated for SOC mapping and accounting

• These two variables sufficient for SOC mapping in areas covered by soil survey