Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau

Abstract

The study showed that the performances of IDW, OK, and RBFNN‐RK in predicting SMC were generally much better than that of MLR‐RK. Specifically, IDW performed best for soil depths of 200‒300 and 400‒500 cm. This was attributed to the more uniform distribution (smoother change of spatial clusters) of SMC in these two layers. The OK method performed best for the 10‐ to 40‐ and 40‐ to 100‐cm soil layers, which was due to the strong spatial dependence of the two layers. The RBFNN‐RK performed best for the 0‐ to 10‐, 100‐ to 200‐, and 300‐ to 400‐cm soil layers, because RBFNN‐RK captures nonlinear relations of SMC with environmental factors. Ordinary kriging, IDW, and RBFNN‐RK interpolation can therefore be used to predict regional SMC for different soil layers in CLP region. The RBFNN‐RK method was recommended for predicting regional SMC in complex topographic hilly‐gully regions where there is nonlinear relation between SMC and environmental variables.