![]() The ordinary kriging and inverse distance weighted methods performed poorly due to the poor spatial autocorrelation of soil moisture at small catchment scale with complex terrain, where the environmental impact factors were discontinuous in space. ![]() The results showed that the prediction accuracy differed significantly between each method in complex terrain. ![]() The performance of each method was assessed quantitatively in terms of mean-absolute-percentage-error, root-mean-square-error, and goodness-of-prediction statistic. Four spatial interpolation methods, including ordinary kriging, inverse distance weighting, linear regression and regression kriging were used for modeling, randomly partitioning the data set into 2/3 for model fit and 1/3 for independent testing. A data set of 153 soil water profiles (1 m) from the semiarid hilly gully Loess Plateau of China was used, generated under a wide range of land use types, vegetation types and topographic positions. Our objective in the present study was to analyze the suitability of several popular interpolation methods for complex terrains and propose an optimal method. However, few interpolation methods perform satisfactorily for complex terrains. Many spatial interpolation methods perform well for gentle terrains when producing spatially continuous surfaces based on ground point data. ![]() ![]() This article has been cited by other articles in PMC. ![]()
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