Method for spatial variety of soil organic matter based on radial basis function neural network

Fast and accurate simulation of the spatial distribution of soil properties from the study on soil spatial variability and spatial interpolation was the basis for precision agriculture and environmental protection. In this paper, 80 topsoil samples were collected in a 40 km2 test area in Meishan, Sichuan Province. Nonlinear mapped relations between spatial coordinates and neighbor samples and the content of soil organic matter were established based on radial basis function neural network (RBF2) to simulate the distribution of the content of soil organic matter in the test area. Compared with ordinary kriging method (OK) and radial basis function neural network method only using spatial coordinates as inputs of net (RBF1), the predicted errors achieved by RBF2 were much smaller, which were reduced by 9.87%, 13.09% and 1.97%, 2.36%, respectively; even samples were cut in half, the predicted error was still reduced by 10.23% and 2.33%, respectively, compared with OK and RBF1 which used in all samples. Besides, RBF2, which was able to make the interpolation maps and had smaller difference comparatively in different samples, could express the spatial heterogeneity of soil organic matter well. Thus, the spatial heterogeneity information of soil properties could be achieved exactly and quickly by the method of radial basis function neural network which used spatial coordinates and neighbor samples information as inputs of net.