Predicting Soil Organic Carbon Stock Using Profile Depth Distribution Functions and Ordinary Kriging

The objective of this study was to predict and map SOC stocks at different depth intervals within the upper 1‐m depth using profile depth distribution functions and ordinary kriging. These approaches were tested for the state of Indiana as a case study. A total of 464 pedons representing 204 soil series was obtained from the National Soil Survey Center database. Another 48 soil profile samples were collected to better represent the heterogeneity of the environmental variables. Two methods were used to model the depth distribution of the SOC stocks using negative exponential profile depth functions. In Procedure A, the functions to describe the depth distribution of volumetric C content for each soil profile were fitted using nonlinear least squares. In Procedure B, the exponential functions were fitted to describe the depth distribution of the cumulative SOC stocks. The parameters of the functions were interpolated for the entire study area using ordinary kriging on 81% of the data points ( n = 414). The integral of the exponential function up to the desired depth was used to predict SOC stocks within the 0‐ to 1‐, 0‐ to 0.5‐, and 0.5‐ to 1‐m depth intervals. These estimates were validated using the remaining 19% ( n = 98) of the data. Procedure B showed a higher prediction accuracy for all depths, with higher r and lower RMSE values. The highest prediction accuracy ( r = 0.75, RMSE = 2.89 kg m −2 ) was obtained for SOC stocks in the 0‐ to 0.5‐m depth interval. Using Procedure B, SOC stocks within the top 1 m of Indiana soils were estimated to be 0.90 Pg C.