地学前缘 ›› 2011, Vol. 18 ›› Issue (6): 34-40.

• 论文 • 上一篇    下一篇

不同采样点数量下土壤有机质含量空间预测方法对比

苏晓燕, 赵永存, 杨浩, 陆访仪, 孙维侠, 王火焰, 黄标, 胡文友   

  1. 1. 南京师范大学 地理科学学院， 江苏 南京 210046
    2. 中国科学院 土壤环境与污染修复重点实验室(南京土壤研究所)， 江苏 南京 210008
  • 收稿日期:2011-10-10 修回日期:2011-10-31 出版日期:2011-11-25 发布日期:2011-12-05
  • 作者简介:苏晓燕(1987—)，女，硕士研究生，自然地理学专业。E-mail:suxiaoyan7236@163.com
  • 基金资助:

    国家自然科学基金科学部主任基金项目(41040009)；中国科学院知识创新工程重要方向项目(KZCX2EWQN404)；国家重点基础研究发展计划“973”项目(2010CB950702)

A comparison of predictive methods for mapping the spatial distribution of soil organic matter content with different sampling densities.

  1. 1. School of Geography Science, Nanjing Normal University, Nanjing 210046, China
    2. Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
  • Received:2011-10-10 Revised:2011-10-31 Online:2011-11-25 Published:2011-12-05

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摘要：

运用普通克里格、泛克里格、协同克里格和回归克里格4种方法，结合由DEM获取的高程因子以及土壤全氮和阳离子交换量(CEC),预测了黑龙江省海伦市耕地有机质含量的空间分布。不同样点数量下海伦市土壤有机质含量的空间变异结构分析表明，样点数量多并不一定能够识别土壤有机质含量的结构性连续组分，最优化的布置采样点位置可能比单纯增加采样点的数量更重要。不同样点数量下的空间预测精度分析表明，以全氮为协同变量的协同克里格预测精度最高，能反映出海伦市土壤有机质含量空间分布的局部变异细节，并且能够解释超过50%的有机质方差；而普通克里格、泛克里格和回归克里格3种方法预测误差均较高，回归克里格并不一定能提高预测精度。获取与土壤有机质含量相关性高且完整覆盖研究区的辅助数据是提高有机质含量空间预测精度，降低采样点数量的重要途径之一。

关键词: 土壤有机质, 普通克里格, 泛克里格, 协同克里格, 回归克里格

Abstract:

Soil organic matter (SOM) is extremely important for maintaining soil fertility and linking carbon cycle within terrestrial ecosystem. In order to quantify the spatial patterns of SOM content in Hailun County of China, ordinary kriging (OK), universal kriging (UK), cokriging (CK), regression kriging (RK), and secondary data derived from soil total nitrogen (TN), cation exchangeable capacity (CEC) and digital elevation model (DEM) were employed to map the spatial distribution patterns of SOM with different sampling densities. The results of SOM spatial structures identified by different sampling densities indicated that the increase of soil sampling densities may not benefit the identification of SOM continuous component and optimal design of sampling locations was even more important than improving sampling densities. With respect to spatial predictive performance, CK with TN as covariable always had the highest accuracy, and more than 50% of the SOM variation can be explained by CK method. The local detail of SOM variability derived from CK method also outperformed OK, UK, and RK, and the predictive accuracy of RK method may not be improved when compared with univariate OK method. The soil sampling density is not the exclusive factor affecting the spatial predictive accuracy of SOM, and secondary data (ancillary information) significantly correlated to SOM as well as denser coverage of the study area would be more helpful for improving the spatial predictive performance of SOM with limited number of soil samples.

Key words: soil organic matter, ordinary kriging, universal kriging, cokriging, regression kriging

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