Mapping Soil Organic Carbon Using Local Terrain Attributes: A Comparison of Different Polynomial Models

SONG Xiaodong; LIU Feng; ZHANG Ganlin; LI Decheng; ZHAO Yuguo; YANG Jinling

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Mapping Soil Organic Carbon Using Local Terrain Attributes: A Comparison of Different Polynomial Models

机译：使用局部地形属性绘制土壤有机碳的图谱：不同多项式模型的比较

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Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of first-and second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30％ and 9.68％.The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.

机译：直接来源于数字高程模型的局部地形属性已被广泛地应用于数字土壤制图。本研究旨在评估中国黑河两带土壤有机碳（SOC）浓度的制图精度，方法是将预测方法与来自不同多项式模型的局部地形属性相结合。预测精度用作那些可能更关注实际中如何准确模拟土壤性质变化而非模型形态变量及其地貌解释的人的基准在这项研究中，应用了2种邻域类型（正方形和圆形）和6种代表性算法（Evans-Young，Horn，Zevenbergen-Thorne，Shary，Shi和Florinsky算法）。通常，第一种算法的35种组合-和二阶导数是使用两种测绘方法（即带有外部漂移的克里格结果表明，适当的局部地形属性算法可以更好地捕捉土壤性质受地形影响的区域中SOC浓度的空间变化。在使用一阶和二阶导数的不同组合中因此，最好的组合是估算值更准确。对于不同的预测方法，两个区域的相对改善幅度在0.30％至9.68％之间。由高阶算法（Zevenbergen-Thorne和Florinsky）得出的SOC图得到了因此，得出的结论是，可以通过尝试不同的地形分析算法来提高结合辅助变量的预测方法的性能。

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来源
《土壤圈（英文版）》 |2017年第4期|681-693|共13页
作者
SONG Xiaodong; LIU Feng; ZHANG Ganlin; LI Decheng; ZHAO Yuguo; YANG Jinling;
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State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 China;

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 China;

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 China;

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 China;

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 China;

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 China;

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收录信息中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
原文格式 PDF
正文语种 eng
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Mapping Soil Organic Carbon Using Local Terrain Attributes: A Comparison of Different Polynomial Models

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