| Soil property is the result of combined action of nature and human factors.soil is non-homogeneous body, but space-time successive variant,with higher spatial heterogeneity. Soil has spatial heterogeneity in large scale or small scale. Because of economy and manpower, sampling sites are definite, so in order to obtain soil spatial information, we should rely on interpolation method. Due to the complexity and uncertainty of soil property, the precision of common interpolation method always can not often meet request. So, improving the spatial interpolation method to obtain the laws of soil spatial variability with lesser sampling sites became a hotspot in the research field now.TaiHe town and Shangyi town, MeiShan county were selected for the research. Two samples were collected in different scale, one was collected in large scale including 80 soil points,their interval was 700m, the other one was collected in small scale including 30 soil points and their interval was 50m. Available zinc and available cuprum were selected as the object for research.The samples were divided into training and validation datum sets. In order to research the performance of GARBF Neural Networks for soil spatial information interpolation, 4 sampling schemes were designed based on the two training sample set and the performance of GARBF Neural Networks was compared with RBF Neural Networks and Ordinary Kriging method which were widely applied.GARBF Neural Networks(Genetic Algorithm Radias Basis Function Neural Networks), which had strong nonlinear computing competence, was an effective tool for solving the nonlinear system problem. In this paper, the coordinate of the soil points and the values of the 5 soil points which were close to the points needed interpolating were designed as the input of net, so there were 7 nodes in the input layer, the value of the soil property of sampling sites or unknown soil point was the output of the net and there was 1 node in the output layer. In the course of studying and being trained, the model optimized the weights between RBF neural network hidden layer and output layer, using Genetic Algorithm.until the Network learns the relationship between input and output, GARBF can predict the content of each interpolation site. The result of predict value save as .txt file, it can be transferred Raster file and formed the interpolation figure of grid by using the ArcGIS 9.0. The results indicated:(1) The large and small scale under four different sampling schemes (a & b & c & d),the scatter figure of three methods (GARBF and RBF and Ordinary Kriging) showed GARBF used genetic algorithm optimize weight of RBF neural network, and raised the fitting capacity for training sample .The fitting capacity of three methods applied to the same area followed the sequence of GARBF > RBF > Ordinary Kriging.(2) Average absolute error and root mean square error were chosen to judge precision of the interpolation methods, whether the points was collected in large or small scale, the interpolation precision of GARBF neural network method excelled RBF neural network and the Kriging method. The main results as follow:In large scale, GARBF as compared with RBF, the approximate errors of the training samples about available cuprum were reduced by 0.02～0.01 in Scheme a and by 0.20～0.22 in Scheme b, available zinc by 0.22～0.25 in Scheme a and by 0.10～0.11 in Scheme b. The interpolation errors of the test samples about available cuprum by 0.23～0.26 in Scheme a and by 0.16～0.12 in Scheme b, available zinc by 0.13～0.11 in Scheme a and by 0.02～0.13 in Scheme b. GARBF as compared with Ordinary Kriging, the approximation errors of the training samples about available cuprum were reduced by 1.18～1.43 in Scheme a and by 0.98～1.16 in Scheme b, available zinc by 1.19～1.47 in Scheme a and by 1.46～1.87 in Scheme b. The interpolation errors of the test samples about available cuprum by 0.57～0.30 in Scheme a and by 0.02～0.05 in Scheme b, available znic by 0.14～0.20 in Scheme a and by 0.51～0.24 in Scheme b.In small scale, GARBF as compared with RBF, the approximate errors of the training samples about available cuprum were reduced by 0.01～0.01 in Scheme c and by 0.21～0.25 in Scheme d,available zinc by 0.10～0.12 in Scheme c and by 0.10～0.11 in Scheme d, then The interpolation errors of the test samples about available cuprum by 0.12～0.12 in Scheme c and by 0.01～0.04 in Scheme d, available zinc by 0.04～0.07 in Scheme c and by 0.05～0.05 in Scheme d; GARBF as compared with Ordinary Kriging, the approximation errors of the training samples about available cuprum were reduced by 0.49～0.69 in Scheme c and by 1.19～1.31 in Scheme d ,available zinc by 0.38～0.53 in Scheme c and by 0.36～0.60 in Scheme d ,The interpolation errors of the test samples about available cuprum by 0.37～0.42 in Scheme c and by 0.53～0.59 in Scheme d , available znic by 0.01～0.08 in Scheme c and by 0.08～0.27 in Scheme d. So it was obvious that the GARBF neural network was the least in error and the highest in interpolation precision.(3) In different scale, there were some difference in the interpolation map of three methods.The interpolation map of three methods had similar distribution tendency in large scale. But in small scale, the interpolation map of three methods had some diversity. That is, total tendency was different and the rang of raster distribution represented the elements was different too, and it was possiblily related to decrease of sampling point. In general, the interpolation map of nerual network showed the elements of soil spatial heterogeneity predominantly, GARBF neural network respect for the value of the original data especially, the overall distribution was discrete relative, plaque was abundant, distribution of the data variability was prominent and reflected the soil properties of the spatial distribution in the actual situation preferably. The reason was genetic algorithm overcame the tendency of neural networks to land in local optima and expanded the scope of search of spatial information pertaining to soil, thus to a certain extent avoiding a similar problem of "smooth effect" like Ordinary Kriging.
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