EN中文

GEOGRAPHICAL RESEARCH >

2012 , Vol. 31 >Issue 10: 1793 - 1805

DOI: https://doi.org/10.11821/yj2012100006

论文

Comparison on soil depth prediction among different spatial interpolation methods: A case study in the Three-River Headwaters Region of Qinghai Province

Expand
  • 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Agriculture Resource Monitoring Station, Chinese Academy of Agriculture Engineering, Beijing 100125, China;
    3. School of Geographical sciences, Southwest University, Chongqing 400715, China

Received date: 2011-12-10

  Revised date: 2012-05-23

  Online published: 2012-10-10

Fold

Abstract

Based on the soil depth data from 533 soil profiles in the Three-River Headwaters Region of Qinghai Province, with the help of GIS technology, this paper attempted to predict the spatial distribution of soil depth by using deterministic interpolation methods(Inverse Distance Weighted, Global Polynomial Interpolation, Local Polynomial Interpolation and Radial Basis Function)and geostatistics interpolation methods(Oridnary Kriging, Simple Kriging, Universal Kriging and Co-Kriging).Then it compared the prediction errors, statistical characteristics and interpolation results of different interpolation methods. Some important conclusions were obtained from this research, which mainly contained three aspects as follows.(1)The spatial distribution of soil depth using the first order surface trend of Ordinary Kriging was better than that using the second order surface trend.The spherical model in the Ordinary Kriging(first order)was better than the exponential model and Gaussian model.The Ordinary Kriging was the best of the four geostatistics interpolation methods because of its minimum error and accurately predicted result. (2)The Inverse Distance Weighted(exponent 1)method was the best in deterministic interpolation methods from the prediction errors and comprehensive reflection in general and local trends.(3)In comparison of the prediction errors and spatial distribution in general and local trends, the Ordinary Kriging(first order)of spherical method, which had anisotropy, could be best to reflect the spatial distribution of soil depth in the Three-River Headwaters Region of Qinghai Province.

Key words： soil depth; spatial distribution; spatial interpolation; Three-River Headwaters Region

Cite this article

Yi Xiang-sheng, Li Guo-sheng, Yin Yan-yu, Peng Jing-tao . Comparison on soil depth prediction among different spatial interpolation methods: A case study in the Three-River Headwaters Region of Qinghai Province[J]. GEOGRAPHICAL RESEARCH, 2012 , 31(10) : 1793 -1805 . DOI: 10.11821/yj2012100006

References

[1] 张本家, 高岚. 辽宁土壤土层厚度与抗蚀年限. 水土保持研究, 1997,4(4):57～59.

[2] 王志强, 刘宝元, 海春兴. 土壤厚度对天然草地植被盖度和生物量的影响. 水土保持学报, 2007,21(4):164～167.

[3] 杨喜田, 董惠英, 刘明强, 等. 太行山荒废地土壤厚度与植被类型关系的研究. 河南农业大学学报, 1999,33(增刊):8～11.

[4] 陈佳, 史志华, 李璐, 等. 小流域土层厚度对土壤水分时空格局的影响. 应用生态学报, 2009,20(7):1565～1570.

[5] Liang X,Wood E F,Lettenmaier D P.Surface soil moisture parameterization of the VIC-2Lmodel:Evaluationand modification.Global and Planetary Change,1996,13(1-4):195～206.

[6] 王绍强, 朱松丽, 周成虎. 中国土壤土层厚度的空间变异性特征. 地理研究, 2001,20(2):161～169.

[7] 刘劲松, 陈辉, 杨彬云, 等. 河北省年均降水量插值方法比较. 生态学报, 2009,29(7):3493～3500.

[8] 李璐, 姜小三, 孙永远. 基于地统计学的降雨侵蚀力插值方法研究. 生态与农村环境学报, 2011,27(1):88～92.

[9] Kuriakose S L,Devkota S,Rossiter D,et al.Prediction of soil depth using environmental variables in an anthropogenic landscape,a case study in the Western Ghats of Kerala,India.Catena,2009,79(1):27～38.

[10] Hengl T,Heuvelink G,Stein A.A generic framework for spatial prediction of soil variables based on regressionkriging.Geoderma,2004,120(1-2):75～93.

[11] Peníek V,Bor vka L.Soil depth prediction supported by primary terrain attributes:A comparison of methods.Plant,Soil and Environment,2006,52:424～430.

[12] 青海省农业资源区划办公室. 青海土种志. 北京:中国农业出版社, 1995.1～327.

[13] 青海省农业资源区划办公室. 青海土壤. 北京:中国农业出版社, 1997.55～262.

[14] 全国土壤普查办公室. 中国土种志(第五卷).北京:中国农业出版社, 1995.118～876.

[15] 李炳元. 青海可可西里地区自然环境. 北京:科学出版社, 1996.118～131.

[16] 全国土壤普查办公室. 中国土壤普查技术. 北京:农业出版社, 1992.18.

[17] 汤国安, 杨昕. ArcGIS地理信息系统空间分析实验教程. 北京:科学出版社, 2006.363～428.

[18] Zhao Y C,Shi X Z,Yu D S,et al.Soil organic carbon density in Hebei Province,China:estimates and uncertainty.Pedosphere,2005,15(3):293～300.

[19] 胡克林, 李保国, 吕贻忠, 等. 非平稳型区域土壤汞含量的各种估值方法比较. 环境科学, 2004,25(3):132～137.

[20] 王政权. 地统计学在生态学中的应用. 北京:科学出版社, 1999.65～149.

[21] Samra J S,Gill H S,Anlauf R,et al.Geostatistical evaluation of soil sodicity and growth of Melia azedarachLinn.as simultaneous stochastic processes.Soil Science,1989,148(5):361～369.

[22] 李新, 程国栋, 卢玲. 空间内插方法比较. 地球科学进展, 2000,15(3):260～265.

[23] 朱求安, 张万昌, 余钧辉. 基于GIS的空间插值方法研究. 江西师范大学学报:自然科学版, 2004,28(2):183～188.

[24] 秦俊桃, 冯绍元, 霍再林, 等. 几种地下水位空间插值方法在干旱内陆区的应用比较. 中国农业大学学报, 2010,15(5):124～129.

[25] 林忠辉, 莫兴国, 李宏轩, 等. 中国陆地区域气象要素的空间插值. 地理学报, 2002,57(1):47～56.

[26] 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. 水土保持学报, 2006,20(2):68～72.

[27] Zhang X Y,Sui Y Y,Zhang X D,et al.Spatial variability of nutrient properties in black soil of Northeast China.Pedosphere,2007,17(1):19～29.

[28] 王红, 宫鹏, 刘高焕. 黄河三角洲多尺度土壤盐分的空间分异. 地理研究, 2006,25(4):649～658.

[29] Ziadat F M.Prediction of soil depth from digital terrain data by integrating statistical and visual approaches.Pedosphere,2010,20(3):361～367.
Options
Outlines

/

〈 〉