地理科学进展 ›› 2014, Vol. 33 ›› Issue (2): 259-269.doi: 10.11820/dlkxjz.2014.02.012

• 土壤与水文 • 上一篇    下一篇

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

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   

  1. 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
  • 收稿日期:2013-08-01 修回日期:2013-12-01 出版日期:2014-02-25 发布日期:2014-02-25
  • 作者简介:李启权(1980-),男,四川泸县人,博士,副教授,主要从事水土资源与环境系统模拟研究,E-mail:liqq@lreis.ac.cn。
  • 基金资助:
    国家自然科学基金项目(41201214)。

Prediction of distribution of soil organic matter based on qualitative and quantitative auxiliary variables:a case study in Santai County in Sichuan Province

LI Qiquan1, WANG Changquan1, YUE Tianxiang2, LI Bing1, ZHANG Xin1, GAO Xuesong1, ZHANG Yi1, YUAN Dagang1   

  1. 1. College of Resources and Environment, Sichuan Agricultural University, Chengdu 611130, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2013-08-01 Revised:2013-12-01 Online:2014-02-25 Published:2014-02-25
  • Contact: 王昌全(1962-),男,四川新都人,博士,教授,主要从土壤与环境可持续研究,E-mail:wchangquan@126.com。 E-mail:liqq@lreis.ac.cn;wchangquan@126.com
  • About author:S158;TP183

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。

关键词: 辅助变量, 空间分布预测, 丘陵区, 神经网络模型, 四川省三台县, 土壤有机质

Abstract: 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.

Key words: auxiliary variables, hilly region, neural network model, Santai County, Sichuan Province, soil organic matter, spatial prediction

中图分类号: 

  • S158