Environmental Monitoring and Assessment

, Volume 185, Issue 6, pp 5151–5164 | Cite as

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

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Abstract

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (ECe 4–8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.

Keywords

Uncertainty assessment Geostatistical simulation Soil salinity Coastal farmland Apparent electrical conductivity 
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Notes

Acknowledgments

The authors are grateful for the financial support of the National Natural Science Foundation of China (41101199), the Special Fund for Public Welfare Industrial (Agriculture) Research of China (200903001), the Natural Science Foundation of Jiangsu Province (BK2011423), the Key Technology R&D Program of Jiangsu Province (BE2010313), the Prospective Project of production education research cooperation of Jiangsu Province (BY2011195), and the Fund Project for Transformation of Scientific and Technological Achievements of Jiangsu Province (BA2010116). Particular thanks are due to Li Dongshun, Zou Ping, and Li Xiaoming for assistance with soil sampling and lab analysis. We also acknowledge the valuable comments of the anonymous reviewers.

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil ScienceChinese Academy of Sciences (CAS)NanjingChina
  2. 2.Dongtai Institute of Tidal Flat ResearchNanjing Branch of the Chinese Academy of SciencesDongtaiChina
  3. 3.Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS); Shandong Provincial Key Laboratory of Coastal Zone Environmental Processes, YICCASYantaiChina
  4. 4.Institute of Life ScienceQingdao University of Science & TechnologyQingdaoChina

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