Sampling design optimization for multivariate soil mapping

被引:63
|
作者
Vasat, R. [1 ]
Heuvelink, G. B. M. [2 ]
Boruvka, L. [1 ]
机构
[1] Czech Univ Life Sci Prague, Dept Soil Sci & Soil Protect, Prague 16521 6, Suchdol, Czech Republic
[2] Univ Wageningen & Res Ctr, Environm Sci Grp, NL-6700 AA Wageningen, Netherlands
关键词
Geostatistics; Sampling design; Linear Model of Coregionalization; Ordinary (co)kriging; Optimization; CONSTRAINED OPTIMIZATION; REGIONALIZED VARIABLES; LOCAL ESTIMATION; SCHEMES;
D O I
10.1016/j.geoderma.2009.07.005
中图分类号
S15 [土壤学];
学科分类号
0903 ; 090301 ;
摘要
Much attention has been paid to sampling design optimization over the past decades. Many methods have been developed and applied, but only a few of these deal with simultaneous optimization of the sampling design for multiple soil variables. In this paper we present a method implemented as R-code that minimizes the average kriging variance (AKV) for multiple soil variables simultaneously. The method is illustrated with real soil data from an experimental field in central Czech Republic. The goal of the method is to minimize the sample size while keeping the AKV values of all tested soil variables below given thresholds. We defined and tested two different objective functions, critical AKV optimization and weighted sum of AKV optimization, both based on the AKV minimization with annealing algorithm. The crucial moment for such an optimization is defining the mutual spatial relationship between all soil variables with the Linear Model of Coregionalization and proper modelling of all (cross)variograms which are used in the optimization process. In addition, a separate optimization was made for each of the tested soil characteristics to evaluate a possible gain of the simultaneous approach. The results showed that the final design for multivariate sampling is "fully-optimal" for one soil variable optimal number of observations and optimal structure of sampling pattern, and "sub-optimal" for the others, while no clear difference between the two optimization criteria was found. We can recommend using the method in situations where periodical soil surveys are planned and where multivariate soil characteristics are determined from the same soil samples at once. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:147 / 153
页数:7
相关论文
共 50 条
  • [31] Multivariate hermite approximation for design optimization
    Wang, LP
    Grandhi, RV
    Canfield, RA
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 1996, 39 (05) : 787 - 803
  • [32] A novel sampling design considering the local heterogeneity of soil for farm field-level mapping with multiple soil properties
    Wang, Yongji
    Qi, Qingwen
    Bao, Zhengyi
    Wu, Lili
    Geng, Qingling
    Wang, Jun
    PRECISION AGRICULTURE, 2023, 24 (01) : 1 - 22
  • [33] Multivariate Hermite approximation for design optimization
    Wright State Univ, Dayton, United States
    Int J Numer Methods Eng, 5 (787-803):
  • [34] Using targeted sampling to process multivariate soil sensing data
    Adamchuk, Viacheslav I.
    Rossel, Raphael A. Viscarra
    Marx, David B.
    Samal, Ashok K.
    GEODERMA, 2011, 163 (1-2) : 63 - 73
  • [35] Model-Based Sampling Design for Multivariate Geostatistics
    Li, Jie
    Zimmerman, Dale L.
    TECHNOMETRICS, 2015, 57 (01) : 75 - 86
  • [36] An optimum multivariate-multiobjective stratified sampling design
    Ansari A.H.
    Varshney R.
    Najmussehar
    Ahsan M.J.
    METRON, 2011, 69 (3) : 227 - 250
  • [37] Optimal design of multivariate acceptance sampling plans by variables
    Duarte, Belmiro P. M.
    Singh, Satya P.
    Moura, Maria J.
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2022, 92 (15) : 3129 - 3149
  • [38] Sampling strategies in soil mapping with real-time soil spectrophotometer
    Wijaya, IMAS
    Shibusawa, S
    Sasao, A
    Sakai, K
    Hache, C
    Hirako, S
    INTELLIGENT CONTROL FOR AGRICULTURAL APPLICATIONS 2001, 2002, : 39 - 42
  • [39] Exploring the Sensitivity of Sampling Density in Digital Mapping of Soil Organic Carbon and Its Application in Soil Sampling
    Guo, Long
    Linderman, Marc
    Shi, Tiezhu
    Chen, Yiyun
    Duan, Lijun
    Zhang, Haitao
    REMOTE SENSING, 2018, 10 (06)
  • [40] Mapping the design optimization landscape
    Balázs, ME
    Clarkson, PJ
    Parks, GT
    DESIGN METHODS FOR PERFORMANCE AND SUSTAINABILITY, 2001, : 211 - 218