Journal of Integrative Agriculture ›› 2017, Vol. 16 ›› Issue (12): 2871-2885.DOI: 10.1016/S2095-3119(17)61762-3

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  • 收稿日期:2017-05-14 出版日期:2017-12-20 发布日期:2017-12-08

Recent progress and future prospect of digital soil mapping: A review

ZHANG Gan-lin1, 2, LIU Feng1, SONG Xiao-dong1   

  1. 1 State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, P.R.China
    2 University of Chinese Academy of Sciences, Beijing 100049, P.R.China
  • Received:2017-05-14 Online:2017-12-20 Published:2017-12-08
  • Contact: Correspondence ZHANG Gan-lin, E-mail: glzhang@issas.ac.cn
  • Supported by:

    The study is supported by the National Natural Science Foundation of China (91325301, 41571130051).

Abstract: To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is urgently needed.  This drives the worldwide development of digital soil mapping.  In recent years, significant progresses have been made in different aspects of digital soil mapping.  The main purpose of this paper is to provide a review for the major progresses of digital soil mapping in the last decade.  First, we briefly described the rise of digital soil mapping and outlined important milestones and their influence, and main paradigms in digital soil mapping.  Then, we reviewed the progresses in legacy soil data, environmental covariates, soil sampling, predictive models and the applications of digital soil mapping products.  Finally, we summarized the main trends and future prospect as revealed by studies up to now.  We concluded that although the digital soil mapping is now moving towards mature to meet various demands of soil information, challenges including new theories, methodologies and applications of digital soil mapping, especially for highly heterogeneous and human-affected environments, still exist and need to be addressed in the future.

Key words: digital soil mapping ,  soil-landscape model ,  predictive models ,  soil functions ,  spatial variation