中文摘要：

准确获取土壤性质的空间分布信息,是区域土壤资源优化利用和土壤环境保护的需要.以川中丘陵区三台县为案例区,运用人工神经网络模型,构建融合区域定性及定量辅助变量的空间预测方法,模拟三台县土壤有机质的空间分布格局.结果表明,研究区土壤有机质在4.20～47.60 g kg-1之间,平均为17.97 g kg-1;变异系数为36.89％,属中等程度变异.土壤有机质的块金值与基台值之比为0.742,变程为7.0 km,即空间自相关性较弱.不同土壤类型间有机质含量差异显著;土属的空间分布较土类能更好地揭示研究区土壤有机质含量空间分布格局的差异.除土壤类型因素的影响外,坡度、地形湿度及植被盖度是研究区土壤有机质空间变异的主要因子.融合土壤类型因素和地形植被因子的神经网络模型预测结果,比普通克里格法、回归克里格法以及神经网络结合普通克里格的方法,更符合研究区地学规律和实际情况;其预测结果的平均绝对误差、平均相对误差和均方根误差较其他3种方法均降低幅度显著.同时,该方法对极值有较好的预测能力.研究为复杂环境条件下准确获取区域土壤性质的空间分布信息提供了较可行的方法.

英文摘要：

Soil organic matter （SOM） is one of the most important indicators of soil quality. Accurate spatial in- formation about SOM is critical for sustainable soil utilization and management and environmental protection. Spatially correlated auxiliary information was widely used to improve spatial prediction accuracy. However, the qualitative variables such as soil type, land use type are not being used often as auxiliary variables. In this paper we proposed a spatial prediction method （ST＋RBFNN） based on radial basis functional neural network model, using both qualitative and quantitative variables as auxiliary information, to predict the spatial distribution of soil organic matter in Santai County in Sichuan Province, located in the hilly region of mid Sichuan Basin. To es- tablish and validate this method, 2346 soil samples were collected and randomly divided into two groups, as modeling points （1877） and validation points （469）. With the modeling points, a radial basis function neural net- work model was trained using the average content of SOM of each soil genus, topographical factors and vegeta- tion index as auxiliary information to predict the spatial distribution of SOM content within each soil genus. Re- sults showed that, the SOM content ranged from 4.20 to 47.60 g kg-~, with an average value of 17.97 g kg~, a moderate variability. The nugget/sill ratio was 0.742, indicating a weak spatial dependence for SOM. Elevation and slope showed significantly negative correlation with SOM content while topographic wetness index and veg- etation index showed significantly positive correlation with SOM. Analysis of variance indicated that there were significant differences in average content of SOM among the different soil types （P〈0.01）, suggesting that soil types also had significant impact on the spatial distribution of SOM, and soil genus types were better predictors than soil groups. Slope, topographic wetness index and vegetation index showed significant correction with the residuals of aver