Special Issue "Global Gridded Soil Information Based on Machine Learning"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Brigitta Szabó (Tóth)
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Guest Editor
Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
Interests: soil science; machine learning; pedotransfer functions; predictive soil mapping; uncertainty assessment
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Prof. Dr. Eyal Ben-Dor
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Guest Editor
The Remote Sensing Laboratory, Tel-Aviv University, Tel-Aviv, Israel
Interests: soil spectroscopy; hyperspectral remote sensing; remote sensing of the environment
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Dr. Yijian Zeng
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Guest Editor
Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Twente, The Netherlands
Interests: the interface of soil–water–plant–energy interactions and Earth observations, including physically-based modelling of soil and subsurface processes
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Prof. Dr. Salvatore Manfreda
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Guest Editor
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Napoli, Italy
Interests: distributed modeling; flood risk; stochastic processes in hydrology and UAS-based monitoring
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Dr. Madlene Nussbaum
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Guest Editor
School of Agricultural, Forest and Food Sciences (BFH-HAFL), Bern University of Applied Sciences, Bern, Switzerland
Interests: regional digital soil mapping; machine learning for spatial prediction; pedotransfer functions; methodological knowledge transfer on soil mapping

Special Issue Information

Dear Colleagues,

Recent technological advances in both remote sensing and soil mapping approaches and progress in establishing harmonized soil profile datasets have opened up the potential to derive global gridded soil information. This has been possible because worldwide researchers have gained a growing experience in building standardized soil profile datasets with measured physical, chemical data and taxonomical information; filling data gaps; using Earth observation data for soil mapping; optimizing soil sampling strategy; processing big data; applying machine learning algorithms; and assessing uncertainty; which support the preparation of global soil maps with increasing accuracy and spatiotemporal resolution.


Data-intensive computing solutions to process and analyze the exploding amount of environmental information are continuously updated. Machine learning algorithms are among the most frequently used tools for data preprocessing and describing the complex relationship between soil properties and environmental covariates with the ability to assess the uncertainty of the predictions. One of the greatest challenges in deriving global gridded soil information is to make the most of the predictive power of machine learning algorithms with the continuously increasing amount of environmental information. This Special Issue is dedicated to machine learning-based methods in:

  • proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);
  • computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;
  • preprocessing Earth observation data to feed into global soil mapping;
  • data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;
  • optimizing temporal resolution to globally track the changes of soil properties,
  • uncertainty assessment of the derived gridded soil information;
  • specifying algorithms to local soil specificities in, e.g., proximal soil mapping;
  • the engagement of remote sensing data with digital soil mapping;
  • downscaling of large-scale soil feature;
  • other related topics.

Review contributions on the abovementioned topics are welcomed as well.


Dr. Brigitta Szabó (Tóth)
Prof.Dr. Eyal Ben-Dor
Dr. Yijian Zeng
Prof.Dr. Salvatore Manfreda
Dr. Madlene Nussbaum
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Global gridded soil information
  • Predictive soil mapping
  • Uncertainty assessment
  • Spectral data
  • Parallel distributive platforms
  • Machine learning
  • Digital soil mapping

Published Papers (1 paper)

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Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative
Remote Sens. 2020, 12(24), 4073; https://doi.org/10.3390/rs12244073 - 12 Dec 2020
Cited by 1 | Viewed by 1339
Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing [...] Read more.
Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier. Full article
(This article belongs to the Special Issue Global Gridded Soil Information Based on Machine Learning)
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