资源科学 ›› 2016, Vol. 38 ›› Issue (12): 2375-2382.doi: 10.18402/resci.2016.12.16

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采样点数量对黄河三角洲垦利县土壤盐分空间变异的影响

张晓光1,2(), 王志刚3, 宋祥云1, 刘佩茹1, 李士美1(), 杨霞4   

  1. 1.青岛农业大学资源与环境学院,青岛 266109
    2. 中国科学院南京土壤研究所土壤与农业可持续发展国家重点实验室,南京 210008
    3. 长江水利委员会长江科学院,武汉 430010
    4. 东营市农业局,东营 257091
  • 收稿日期:2016-06-23 修回日期:2016-09-22 出版日期:2016-12-20 发布日期:2016-12-20
  • 作者简介:

    作者简介:张晓光,男,山东济南市人,博士,讲师,目前从事土壤属性预测和资源环境遥感方面研究。zhangxg_66@sina.com

  • 基金资助:
    青岛农业大学高层次人才启动基金项目(1114344);土壤与农业国家重点实验室开放基金项目(Y20160007);国家自然科学基金项目(41601211)

Effect of sampling on spatial variability in soil salinity in the Yellow River Delta Area

ZHANG Xiaoguang1,2(), WANG Zhigang3, SONG Xiangyun1, LIU Peiru1, LI Shimei1(), YANG Xia4   

  1. 1. College of Resource and Environment,Qingdao Agricultural University,Qingdao 266109,China
    2. State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences,Nanjing 210008,China
    3. Changjiang River Scientific Research Institute of Changjiang Water Resources Commission,Wuhan 430010,China
    4. Agricultural Bureau of Dongying City,Dongying 257091,China
  • Received:2016-06-23 Revised:2016-09-22 Online:2016-12-20 Published:2016-12-20

摘要:

对土壤盐渍化的监测是合理利用盐渍化土壤的前提,采样点数量不同会影响土壤盐分空间变异的表达。本文以黄河三角洲典型地区垦利县为研究区,采用地统计学方法,抽取了12个样本子集,探讨不同的采样点数量对土壤盐分空间变异表达的影响。研究结果表明,黄河三角洲地区土壤盐分变异程度较大,随着样点数量的减少,对于细节的刻画能力也在减弱,样点数量过小时(<150个),不能很好地表达空间变异结构。不同采样点数量下的预测值均方根误差(RMSE)随样本量的增大而减少,说明克里格插值的准确度随样本量的减少呈现降低趋势,平均误差(ME)则随样本量的减小而没有明显的趋势。综合ME、RMSE和ASE 3个指标可以得出,满足黄河三角洲地区县域尺度土壤盐分空间变异表达的土壤样点数量不能小于150个,相当于每1000km2需采集107个样点。

关键词: 采样数量, 土壤盐分, 空间变异, 县域尺度, 黄河三角洲垦利县

Abstract:

The monitoring of spatial variation in soil salinization forms the premise of the rational use of saline soil. The number of sampling points will affect the expression of spatial variation of soil salinity. Here,geostatistical methods were used to extract 12 soil sample subsets for Kenli County in the Yellow River Delta,China. The effect of different sampling numbers on the expression of soil salinity spatial variation was explored. The results showed that a great degree of variability in soil salinity. Effective ranges of most sample sets were between 13.47km and 16.77km. When the sampling number was less than 150 sample points,effective range was decreased (< 6.10km),small samples were better in indicating the variation characteristics at small scales (< 6.10km). With a reduction in sampling numbers,the capacity for describing details was weakened. Spatial variation structure was not expressed well,especially when the number was small(<150). With increasing sampling numbers,the root mean square error (RMSE)was reduced,indicating that the accuracy of the kriging interpolation method was reduced. With increasing sampling numbers,the mean error(ME)had no obvious reduced trend. In summary,the samples number to meet the spatial variability of soil salinity should not be less than 150. We conclude that,in evaluating kriging prediction accuracy the single evaluation index may not be able to completely evaluate predicted results. Combined with the actual fact,a variety of comprehensive indexes may be needed to evaluate predicted results. These findings are applicable to other areas with similar environmental conditions to Kenli County.

Key words: sampling numbers, soil salinity, spatial variability, county scale, Kenli County of the Yellow River Delta Area