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基于定性和定量辅助变量的土壤有机质空间分布预测——以四川三台县为例
Prediction of distribution of soil organic matter based on qualitative and quantitative auxiliary variables: a case study in Santai County in Sichuan Province

查看参考文献32篇

李启权 1   王昌全 1 *   岳天祥 2   李冰 1   张新 1   高雪松 1   张毅 1   袁大刚 1  
文摘 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。
其他语种文摘 Soil organic matter (SOM) is one of the most important indicators of soil quality. Accurate spatial information 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 establish 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 network model was trained using the average content of SOM of each soil genus, topographical factors and vegetation index as auxiliary information to predict the spatial distribution of SOM content within each soil genus. Results showed that, the SOM content ranged from 4.20 to 47.60 g kg~(-1), with an average value of 17.97 g kg~(-1), 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 vegetation 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 average content of SOM (computed by subtracting the average SOM content of the relative soil genus from the original value of each soil sample), indicating that the above three quantitative factors further resulted in the spatial variation of SOM besides soil types. The prediction map obtained by the proposed method was more consistent with the true geographical information than ordinary Kriging (OK), regression Kriging (RK) and neural network combined with ordinary Kriging (RBFNN+OK). Moreover, ST+RBFNN method significantly reduced the prediction errors. Compared to OK, RK and RBFNN+OK, the mean absolute error (MAE) of ST+ RBFNN method was reduced by 31.76%, 28.45% and 26.68%, the mean relative error (MRE) was reduced by 35.90%, 32.55% and 30.75%, and the root mean squared error (RMSE) was reduced by 22.60%, 19.88% and 18.43%. Moreover, this method also showed better capability of predicting the extremum of the validation data. The prediction errors were reduced by 6.88% to 43.70% than the other three methods in predicting the extremum of the validation points (10% of normal distribution of the data). This result suggested that it is helpful for improving the prediction accuracy to employ both qualitative and quantitative variables as auxiliary information in spatial prediction of soil properties, and this proposed method provides a useful research idea for digital soil mapping.
来源 地理科学进展 ,2014,33(2):259-269 【核心库】
关键词 土壤有机质 ; 空间分布预测 ; 神经网络模型 ; 辅助变量 ; 丘陵区 ; 四川省三台县
地址

1. 四川农业大学资源环境学院, 成都, 611130  

2. 中国科学院地理科学与资源研究所, 北京, 100101

语种 中文
文献类型 研究性论文
ISSN 1007-6301
学科 农业基础科学;自动化技术、计算机技术
基金 国家自然科学基金项目
文献收藏号 CSCD:5062857

参考文献 共 32 共2页

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引证文献 36 篇

1 江叶枫 应用集成BP神经网络模型预测土壤有机质空间分布 江苏农业学报,2017,33(5):1044-1050
CSCD被引 8 次

2 罗由林 川中丘陵县域土壤碳氮比空间变异特征及其影响因素 应用生态学报,2015,26(1):177-185
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