Comparison of Spatial Interpolation Methods for Soil Total Nitrogen Content at Large Scale Using Low Density Soil Survey Data in Northeast China

CHEN Chang-hua; CHEN Xi-yun; XU Ying

doi:10.13961/j.cnki.stbctb.2014.06.036

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Comparison of Spatial Interpolation Methods for Soil Total Nitrogen Content at Large Scale Using Low Density Soil Survey Data in Northeast China

- Comparison of Spatial Interpolation Methods for Soil Total Nitrogen Content at Large Scale Using Low Density Soil Survey Data in Northeast China
- Bulletin of Soiland Water Conservation Vol. 33, Issue 6, Pages: 153-161(2014)
- 作者机构：
  
  北京师范大学地理学与遥感科学学院,北京,100875
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2014.06.036
  CLC：
- Published：2014
- 稿件说明：
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CHEN Chang-hua, CHEN Xi-yun, XU Ying. Comparison of Spatial Interpolation Methods for Soil Total Nitrogen Content at Large Scale Using Low Density Soil Survey Data in Northeast China[J]. Bulletin of Soiland Water Conservation, 2014, 33(6): 153-161.
DOI：

CHEN Chang-hua, CHEN Xi-yun, XU Ying. Comparison of Spatial Interpolation Methods for Soil Total Nitrogen Content at Large Scale Using Low Density Soil Survey Data in Northeast China[J]. Bulletin of Soiland Water Conservation, 2014, 33(6): 153-161. DOI： 10.13961/j.cnki.stbctb.2014.06.036.

摘要

基于全国第二次土壤普查东北地区土壤数据

以ArcGIS和GS

软件为支撑

对比分析了反距离加权法(IDW)、径向基函数(RBF)、普通克里金(OK)和回归克里金(RK)4种地统计空间插值方法在7个不同样本容量下土壤全氮含量(STNC)的空间插值效果。结果表明

由普查数据得到的东北地区STNC在0.08~21.48 g/kg之间

数据变异性较大;STNC空间结构表现出中强度空间自相关性

空间自相关范围大于同区域的小尺度采样研究;样本容量

171时

STNC空间变异性发生变化

空间结构特征和精度检验水平难以确信。4种空间插值方法对STNC空间趋势表达均呈现从东北向西南方向递减规律

空间趋势预测效果为:RK >OK >RBF >IDW。RK方法通过线性回归分析添加了阳离子交换容量(CEC)、年均温(MAT)、土层厚度(d)和pH值等辅助信息

比IDW

RBF和OK方法的插值精度分别提高了19.40%

18.50%和16.15%;在不同样本容量下RK方法的插值精度较为稳定且对无样点区STNC的空间趋势预测也体现出了更多细节信息

因此对于大尺度低密度采样的土壤属性空间插值可重点考虑RK方法。

Abstract

Based on the relatively low density sampling data from China's second national soil survey in northeast China

we compared the erformance of four spatial interpolation methods

inverse distance weighting(IDW)

radial basis function(RBF)

ordinary Kriging(OK) and regression Kriging(RK)

under seven sample sizes in generating digital map of soil total nitrogen content at large scale based on the software ArcGIS and GS+. Results showed that soil total nitrogen content was in the range of 0.08~21.48 g/kg with great variability. The Nugget effect showed a medium strong spatial autocorrelation of soil total nitrogen content in the region

and the range of spatial autocorrelation was greater than research on smaller scale in the same region; Spatial variability of soil total nitrogen content changed when sample size was less than 171

in this case

spatial structure and accuracy test of interpolation were unbelievable. All of the four compared methods predicted the spatial pattern of soil total nitrogen content well with decreasing from northeast to southwest. The accuracy of interpolation changed in the order of RK >OK >RBF >IDW. With incorporated auxiliary variables of soil cation exchange capacity

soil depth

soil pH value and annual mean air temperature

the RK improved accuracy by 19.40%

18.50% and 16.15% than IDW

RBF and OK

respectively. It also exhibited more details on soil total nitrogen content variation at the areas with sparse sample points. It suggested that the RK is a potential spatial interpolation method to improve the soil mapping accuracy at large area with low density sample sites.

关键词

Keywords

references

Lam N S. Spatial Interpolation[M]//International Encyclopedia of Human Geography. Elsevier Science Ltd., 2009.

Takata Y, Funakawa S, Akshalov K, et al. Spatial prediction of soil organic matter in northern Kazakhstan based on topographic and vegetation information[J]. Soil Science and Plant Nutrition, 2007,53(3):289-299.

张素梅,王宗明,张柏,等.利用地形和遥感数据预测土壤养分空间分布[J].农业工程学报,2010,26(5):188-194.

Simbahan G C, Dobermann A, Goovaerts P, et al. Fine-resolution mapping of soil organic carbon based on multivariate secondary data[J]. Geoderma, 2006,132(3/4):471-489.

McKenzie N J, Ryan P J. Spatial prediction of soil properties using environmental correlation[J]. Geoderma, 1999,89(1/2):67-94.

Bui E N, Moran C J. A strategy to fill gaps in soil survey over large spatial extents an example from the Murray-Darling basin of Australia[J]. Geoderma, 2003,111(1/2):21-44.

Zhang Shiwen, Huang Yuanfang, Shen Chonggan, et al. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information[J]. Geoderma, 2012,171(S):35-43.

Schloeder C A, Zimmerman N E, Jacobs M J. Comparison of methods for interpolating soil properties using limited data[J]. Soil Science Society of America Journal, 2001,65(2):470-479.

石小华,杨联安,张蕾.土壤速效钾养分含量空间插值方法比较研究[J].水土保持学报,2006,20(2):68-72.

Webster R. Soil resources and their assessment[J]. Phil.Trans.R.London:B, 1997,352(1356):963-973.

刘付程,史学正,于东升,等.基于地统计学和GIS的太湖典型地区土壤属性制图研究:以土壤全氮制图为例[J].土壤学报,2004,41(1):20-27.

Stacey K F, Lark R M, Whitmore A P, et al. Using a process model and regression Kriging to improve predictions of nitrous oxide emissions from soil[J]. Geoderma, 2006,135:107-117.

Odeh I O A, McBratney A B, Chittleborough D J. Further results on prediction of soil properties from terrain attributes:Heterotopic Cokriging and regression-Kriging[J]. Geoderma, 1995, 67(3-4):215-226.

Webster R, Oliver M A. Sample adequately to estimate variograms of soil properties[J]. Journal of Soil Science, 1992,43(1):177-192.

Sumfleth K, Duttmann R. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators[J]. Ecological Indicators, 2008,8(5):485-501.

Menard S W. Applied logistic regression analysis[M]. Thousand Oaks:CA:SAGE Publication, Inc., 1995.

Ziadat F M. Analyzing digital terrain attributes to predict soil attributes for a relatively area[J]. Soil Science Society of America Journal, 2005,69(5):1590-1599.

Wei Jianbing, Xiao Duning, Zhang Xingyi, et al. Topography and land use effects on the spatial variation of soil organic carbon:A case study in a tipical small watershed of the black soil region in Northeast China[J]. Euras Soil Sci., 2008,41(1):39-47.

Liu Dianwei, Wang Zongming, Zhang Bai, et al. Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China[J]. Agriculture, Ecosystems and Environment, 2006,113(1/4):73-81.

Zhang Xingyi, Sui Yueyu, Zhang Xudong, et al. Spatial variability of nutrient properties in black soil of Northeast China[J]. Pedosphere, 2007,17(1):19-29.

Yang Yuanhe, Mohammat A, Feng Jianming, et al. Storage, patterns and environmental controls of soil organic carbon in China[J]. Biogeochemistry, 2007,84(2):131-141.

Kravchenko A, Bullock D G. A comparative study of interpolation methods for mapping soil properties[J]. Agronomy Journal, 1999,91(3):393-400.

Panagopoulos T, Jesus J, Antunes M D C, et al. Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce[J]. European Journal of Agronomy, 2006,24(1):1-10.

Hengl T, Heuvelink G, Alfred S. A generic framework for spatial prediction of soil variables based on regression-Kriging[J]. Geoderma, 2004,120(1/2):75-93.

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