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This article in SSSAJ

  1. Vol. 75 No. 3, p. 1044-1053
    Received: Jan 1, 2010

    * Corresponding author(s): axing@lreis.ac.cn
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Updating Conventional Soil Maps through Digital Soil Mapping

  1. Lin Yanga,
  2. You Jiaobc,
  3. Sherif Fahmyc,
  4. A-Xing Zhu *ade,
  5. Sheldon Hanne,
  6. James E. Burtf and
  7. Feng Qig
  1. a State Key Lab. of Resources and Environment Information System Institute of Geographical Sciences and Resources Research Chinese Academy of Sciences Beijing 100101, China
    b Agrifoods Development Branch Dep. of Natural Resources Corner Brook, NF A2H6J8, Canada
    c Potato Research Centre Agriculture and Agri-Food Canada Fredericton, NB E3B4Z7, Canada
    d Dep. of Geography Univ. of Wisconsin 550 N. Park St. Madison, WI 53706
    e State Key Lab. of Remote Sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101, China
    f Dep. of Geography Univ. of Wisconsin 550 N. Park St. Madison, WI 53706
    g Dep. of Geology and Meteorology Kean Univ. 1000 Morris Ave. Union, NJ 07083


Conventional soil maps, as the major data source for information on the spatial variation of soil, are limited in terms of both the level of spatial detail and the accuracy of soil attributes. These soil maps, however, contain valuable knowledge on soil–environment relationships. Such knowledge can be extracted for updating conventional soil maps through the use of available high-quality data on environmental variables and data analysis techniques. We developed a method to update conventional soil maps using digital soil mapping techniques without additional field work, which can be used in situations where the study area contains no or few soil profile descriptions at points. The basis of the method is that soil polygons on a conventional soil map correspond to landscape units, which can be considered as combinations of environmental factors. Such environmental combinations were approximated through fuzzy clustering on the environmental factors. We extracted the knowledge on soil–environment relationships by relating the environmental combinations to the mapped soil types. The extracted knowledge was then used for soil mapping using the Soil Land Inference Model (SoLIM) framework. This method was demonstrated through a case study for updating a conventional 1:20,000 soil map of Wakefield, NB, Canada. The case study showed that the updated digital soil map contained much greater spatial detail than the conventional soil map. Field validation indicated that the accuracy of the updated soil map was much higher than the conventional soil map at the level of soil associations with drainage classes, indicating that the proposed method is an effective approach to updating conventional soil maps.

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