Three-dimensional stochastic simulations of soil clay and its response to sampling density

- The paper simulated soil clay content in three dimensions and revealed its response to sampling density.
- Considering the root mean square error accurate plot, standard error map, and quartile deviation map, SD1 had the best effect of three-dimensional stochastic simulation, followed by SD4.
- For soil clay, three-dimensional sampling can be appropriate to reduce the number of samples required in the lower horizon in order to reduce the sampling workload.

Clay is an active component in the mechanical composition of soil. The quantitative study of the spatial distribution of soil clay content is crucial to soil microecological research and agricultural or environmental management. The main purpose of the paper was to simulate soil clay content in three dimensions and reveal its response to sampling density based on sequential Gaussian simulation. The results showed the following: (1) With a reduction in samples, especially in the A horizon, spatial correlation was relatively enhanced and randomness weakened. (2) The spatial distribution of soil clay showed soil had high clay content in the mid-eastern region of the Haidian District, Beijing, and clay content was generally low in the other areas; (3) With a decrease of sampling density, the simulated spatial distribution of clay became gradually more homogeneous. The stochastic simulation results for two kinds of sampling densities, i.e., SD1 and SD4 were closer to the original measured values; the general distribution was discrete and could more accurately reflect the local volatility of the original data distribution; (4) Considering the root mean square error (RMSE), accurate plot, standard error map, and quartile deviation map, SD1 had the best effect of three-dimensional stochastic simulation, followed by SD4; (5) For soil clay, three-dimensional sampling can be applicable to reduce samples required in the lower horizon in order to reduce the sampling workload.