Acta Geographica Sinica ›› 2005, Vol. 60 ›› Issue (3): 511-518.doi: 10.11821/xb200503018

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Use of Cokriging to Improve Estimates of Soil Salt Solute Spatial Distribution in the Yellow River Delta

WANG Hong1,2, LIU Gaohuan3, GONG Peng2,4   

  1. 1. Dept. of Urban and Resources Sciences, Nanjing University, Nanjing 210093, China;
    2. International Institute for Earth System, Nanjing University, Nanjing 210093, China;
    3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    4. Dept. of Environment Science, Policy and Management, University of California, Berkeley, USA
  • Received:2005-01-17 Revised:2005-03-19 Online:2005-05-25 Published:2005-05-25
  • Supported by:

    CAS Fund for Outstanding Overseas Scholars, No.2001-1-13; National Natural Science Foundation of China, No.40371093; National 973 Project, No.2001CB309404


Estimation of the quantity and distribution of soil chemicals is a major component in the study of chemical transportation in the vadose zone and groundwater system. Such information is also essential in undertaking any proper measures to meliorate soil salinization. However, it is a time-consuming, laborious, and expensive process to carry out detailed sampling in the field, especially when it is large. Accurate variability of soil solute can be determined from a limited number of the available samples through geostatistical analysis. In this study two interpolation methods (ordinary kriging and cokriging) were compared with each other in terms of their accuracy. It is found that cokriging of half of the observations (239) resulted in more accurate results than ordinary kriging of all the samples. Cokriging is able to reduce relative root mean square error (RMSE) by 130.83% in comparison with ordinary kriging. Using the same number of samples (239) for the secondary variable (total salt), cokriging attained a higher accuracy with half of the samples than it did with all the samples, the relative reduction of RMSE being 20.10%. Furthermore, the relationship between the secondary and the primary variables governs the estimation accuracy. As the correlation coefficient between them increases from 77% to 99%, the relative RMSE of estimation is reduced by 48.30%.

Key words: ordinary kriging, cokriging, soil salt concentration, root mean square error, spatial interpolation, the Yellow River Delta