成分克里格和不同对数比协同克里格在大尺度土壤机械组成制图中的应用

Application of Compositional Kriging and Several Types of Log-ratio Co-Kriging in a Case Study of Large-Scale Soil Texture Mapping

  • 摘要:
    目的 针对土壤机械组成的组分特性,应用成分克里格和不同对数比形式的协同克里格方法对大尺度土壤机械组成进行预测,并评价其制图效果。
    方法 基于广东省1863个样点的土壤机械组成数据,使用普通克里格、协同克里格,以及5种常用的组分制图方法——成分克里格和4种不同对数转换的协同克里格,开展广东省土壤机械组成制图,并检验其准确度。
    结果 普通克里格仅在研究区23%的范围内满足组分数据的常和性,准确度较差;协同克里格能间接地解决组分数据的常和性问题,准确度较好;成分克里格能直接解决组分数据的常和性问题,准确度最好;4种对数比协同克里格方法虽然也能直接解决组分数据的常和性问题,但其准确度仅仅略好于普通克里格,而低于其他方法,其中结合了等角对数比转换的协同克里格准确度相对较好。
    结论 成分克里格在大尺度土壤机械组成制图中表现最好,其次是协同克里格和结合等角对数比转换的协同克里格,普通克里格表现最差。在未来的大尺度土壤机械组成制图研究中,还需进一步探索利用环境变量来提高预测的准确度,以及考虑采样和其他因素的影响。

     

    Abstract:
    Objective To cope with compositional characteristics of soil texture data, compositional kriging and log-ratio cokriging were tested in a case study of large-scale soil texture mapping.
    Method Based on soil texture data of 1863 samples collected in Guangdong Province, ordinary kriging, cokriging, compositional kriging method and four types of log-ratio cokriging, were used to map soil texture, and accuracies of the methods were evaluated.
    Result The maps produced using ordinary kriging had only 23% of the region met the basic requirement of constant sum, and their accuracies were the worst. Cokriging indirectly solved the problem of constant sum for soil texture data, and generated good accuracies. Compositional kriging did not only directly solve the problem of constant sum for soil texture data, but also generated the best accuracies. The log-ratio cokriging could also directly solve the problem of constant sum, but generated accuracies slightly better than ordinary kriging and lower than the other methods. Among the log-ratio cokriging methods, the isometric logarithmic ratio transformation performed the best.
    Conclusion Compositional kriging performed the best in large-scale soil texture mapping, followed by cokriging and the log-ratio cokriging combined with isometric logarithmic ratio transformation. In the future, large-scale soil texture mapping based on geostatistical methods still needs to further explore the use of environmental covariates to improve the accuracy, and consider the influence of sampling and other factors.

     

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