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Special Issue in Big Earth Data

Conference Special Issue

In cooperation with the Big Earth Data, a special issue is initiated for several topics of the ISMC conference. Submissions to this conference may be published as extended articles by this journal after peer review.


Spatial and Spatio-temporal Modeling of Soil Systems

Soil models have long played an important role in quantifying and predicting soil processes and related ecosystem services. In recent years, a new generation of soil models based on novel observation methods have been developed. These new models have allowed a greater number of physical, mechanical, chemical, and biological processes to be included and have raised critical questions regarding how the critical zone at the Earth’s crust is shaped by geo-, bio-, and chemical dynamics. Soil models that focus on the critical zone by addressing critical knowledge gaps and thus contribute to the quantification of ecosystem services have improved our understanding of climate change feedback processes and bridged the gap between basic soil-science research and management.


Therefore, this special issue focuses on recent advances in spatial and spatio-temporal soil modeling, especially from the perspective of big soil data, including soil- related data products, the development of soil-modeling tools that use process-based and/or data-driven methods, statistics, and machine learning (deep learning) approaches to analysis and prediction in soil science. We invite original research articles, review articles, data articles, and technical notes related to (but not limited to) the following.

  • Descriptions of innovative datasets relevant to soil modeling
  • The use of advanced machine learning techniques for making spatial and spatial-temporal predictions of soil variables
  • Modeling of the relationships between climate change and soil systems using historic, current, and future scenario data
  • Modeling of the effects of soil carbon sequestration on climate change mitigation and adaptation
  • Modeling of the effects of dynamic land use on soil systems
  • Modeling of the biological and economic potential of soil for soil carbon sequestration
  • Modeling of dynamic soil properties such as soil moisture and water runoff
  • Spatial and spatio-temporal modeling of the distribution of soil biota (macro- and micro-fauna)

This special issue is an initiative of the journal Big Earth Data and the 3rd ISMC Conference “Advances in modeling soil systems” hosted by the International Soil Modeling Consortium ( Contributions from both conference participants and the wider soil modeling community are welcome.


Big Earth Data is the world's first big data journal on Earth sciences, and aims to provide an efficient and high-quality platform for promoting 'big data' sharing, processing and analyses, thereby revolutionizing the cognition of the Earth's systems. The journal publishes original research articles, review articles, data papers and technical notes on 'big data' studies across the entire spectrum of Earth sciences, including soil science. The journal has been indexed by EI Compendex, Scopus and DOAJ. For other journal information, please visit


Submission Instructions

Important Dates

  • 30 September 2021: Paper submission online
  • 30 November 2021: Decision to authors
  • 31 January 2022: Revised paper submission
  • 31 March 2022: Publication


Manuscript Submission Information

Please visit the Instructions for Authors page before submitting your manuscript. Once you have finished preparing your manuscript, please submit it through the Taylor & Francis Submission Portal, ensuring that you select the appropriate Special Issue.

Publication charges (APCs) will be waived for invited manuscripts submitted to Big Earth Data. Authors who require a waiver code should contact the Editorial Office ( before papers are submitted.


Special Issue Editor(s)

Wei Shangguan, Sun Yat-sen University, China

Tomislav Hengl, OpenGeoHub Foundation, The Netherlands

Roland Baatz, Forschungszentrum Jülich, Germany

Sagar Gautam, Sandia National Laboratory, USA

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