Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information
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- 作者：Shiwen Zhang ; Yuanfang Huang ; yfhuang@cau.edu.cn ; Chongyang Shen ; Huichun Ye ; Yichun Du
- 关键词：Spatial prediction ; Soil organic matter ; Categorical variables ; Terrain information ; Variability
- 刊名：Geoderma
- 出版年：2012
- 出版时间：February, 2012
- 年：2012
- 卷：171-172
- 期：Complete
- 页码：35-43
- 全文大小：2187 K
文摘
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.