Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information
Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information
复制标题
使用地形指数和分类变量作为辅助信息的土壤有机质空间预测
DOI：
10.1016/j.geoderma.2011.07.012
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发表时间：
2012-02
期刊：
影响因子：
6.1
通讯作者：
Du, Yichun
中科院分区：
文献类型：
--
作者：
Zhang, Shiwen;Huang, Yuanfang;Shen, Chongyang;Ye, Huichun;Du, Yichun
土壤有机质是土壤质量的重要指标之一。土壤有机质空间变异的准确信息对土壤可持续利用和管理至关重要。虽然利用空间相关的辅助信息来提高土壤性质的预测精度在步数学中得到了广泛的认可，但并非所有的研究都考虑了分类变量的影响（例如，土地利用类型、土壤质地和土壤发生类型），没有系统分析辅助变量与待预测土壤性质的关系。本文旨在研究是否包括分类变量可以提高SOM预测的准确性的基础上系统的变异性分析。采用最小显著差异法（LSD）和Pearson相关分析，系统定量分析了土壤有机质与地形指数、土地利用类型、土壤质地和土壤发生类型等环境变量的关系。利用多元线性逐步回归、普通克立格和回归克立格对土壤有机质的空间分布进行了预测。结果表明，土壤有机质的空间分布主要受地形指数、土壤质地和土壤发生类型的影响。当添加分类变量作为预测因子时，基于海拔的预测的均方根误差（经常用作辅助变量）降低。我们的研究表明，引入分类变量，如土壤遗传类型，提高了预测精度为一个给定的预测方法。与此同时，对要预测的变量和辅助变量之间的关系进行系统和探索性分析，对于确保良好的预测也很重要。
Soil organic matter (SOM) is one of the most important indicators of the soil quality. Accurate information about the spatial variation of SOM is critical to sustainable soil utilization and management. Although utilizing spatially correlated auxiliary information to improve the prediction accuracy of soil properties has been widely recognized in pedometrics, not all studies have taken account of the influence of categorical variables (e.g., land use types, soil texture and soil genetic types) and did not systematically analyze the relationship between auxiliary variables and soil properties to be predicted. This paper aimed to examine whether inclusion of categorical variables can improve the accuracy of SOM prediction based on systematical analyses of variability. The least-significant difference (LSD) method and Pearson correlation analysis were used to systematically and quantitatively analyze the relationship between SOM and other environment variables (terrain indices, land use types, soil texture and soil genetic types). Spatial distribution of SOM was predicted by multiple linear stepwise regressions, ordinary Kriging and regression Kriging. Results indicated that spatial distribution of SOM was mainly affected by terrain indices, soil texture and soil genetic types. The root mean squared error of predictions based on elevation, which is used frequently as an auxiliary variable, was reduced when categorical variables were added as predictors. Our study suggested that introduction of categorical variables, such as soil genetic types, improved the prediction accuracy for a given prediction method. At the same time, systematic and exploratory analyses of the relationship between variables to be predicted and auxiliary was also important to ensure good predictions.
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DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
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