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Land
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Predictive Soil Mapping Contributing to Sustainable Soil Management
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Predictive Soil Mapping Contributing to Sustainable Soil Management

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (23 January 2025) | Viewed by 6289

Special Issue Editors

Dr. Preston T. Sorenson

E-Mail Website
Guest Editor
Department of Soil Science, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
Interests: predictive soil mapping; pedology; reflectance spectroscopy
Dr. Kwabena Abrefa Nketia

E-Mail Website
Guest Editor
Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
Interests: GIS; remote sensing; GIS web services; GPS; land evaluation; land quality assessment and agronomy

Special Issue Information

Dear Colleagues,

(1) Introduction, including scientific background and highlighting the importance of this research area.

Renewed interest in soil mapping and the development of predictive soil mapping tools has greatly increased available soil information. This increased soil information presents the opportunity to improve our understanding of soil-landscape dynamics and make better land use decisions.

Pressing land use decisions, such as where to optimize carbon sequestration efforts and how to more efficiently apply nutrients and optimize land for food production, all depend on detailed soil information. A number of ecological goods and services are also closely tied to soil properties, and more soil information can help ensure that these ecological goods and services are maintained or enhanced.

Predictive soil mapping has an instrumental role to play in ensuring that the necessary soil information is available to achieve these goals.

(2) Aim of the Special Issue and how the subject relates to the journal scope.

The aim of this Special Issue will be to highlight advances in predictive soil mapping, and applications of soil mapping tools and datasets to improve understanding of soil, agronomic, ecological, and land use dynamics.

(3) Suggested themes and article types for submissions.

  • Predictive soil mapping methodology development;
  • Applications of predictive soil mapping to better understand soil-landscape dynamics;
  • Use of predictive soil mapping datasets for agronomic decision making;
  • Predictive soil mapping for ecological goods and services mapping;
  • Use of predictive soil mapping for land use decision making.

Dr. Preston T. Sorenson
Dr. Kwabena Abrefa Nketia
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 submissions that pass pre-check are 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. Land is an international peer-reviewed open access monthly 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 2600 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

23 pages, 3060 KiB  
Article
Integrating Environmental Variables into Geostatistical Interpolation: Enhancing Soil Mapping for the MEDALUS Model in Montenegro
by Stefan Miletić, Jelena Beloica and Predrag Miljković
Land 2025, 14(4), 702; https://doi.org/10.3390/land14040702 - 26 Mar 2025
Viewed by 361
Abstract
Geostatistical methods are important in analyzing natural resources providing input data for complex mathematical models that address environmental processes and their spatial distribution. Ten interpolation methods and one empirical-based classification grounded in empirical knowledge, with a total of 929 soil samples, were used [...] Read more.
Geostatistical methods are important in analyzing natural resources providing input data for complex mathematical models that address environmental processes and their spatial distribution. Ten interpolation methods and one empirical-based classification grounded in empirical knowledge, with a total of 929 soil samples, were used to create the most accurate spatial prediction maps for clay, sand, humus, and soil depth in Montenegro. These analyses serve as a preparatory phase and prioritize the practical application of the obtained results for the implementation and improvement of the MEDALUS model. This model, used to assess sensitivity to land degradation, effectively integrates into broader current and future research. The study emphasizes the importance of incorporating auxiliary variables, such as topography, climate, and vegetation data, enhancing explanatory power and accuracy in delineating the environmental characteristics, ensuring better adaptability to the studied area. The results were validated by the coefficient of determination (R2) and root mean square error (RMSE). For the clay, EBKRP (empirical Bayesian kriging regression prediction) achieved R2 = 0.35 and RMSE = 6.95%, for the sand, it achieved R2 = 0.34 and RMSE = 17.38%, for the humus, it achieved R2 = 0.50 and RMSE = 3.80%, and for the soil depth, it achieved R2 = 0.76 and RMSE = 5.36 cm. These results indicate that EBKRP is the optimal method for accurately mapping soil characteristics in future research in Montenegro. Full article
(This article belongs to the Special Issue Predictive Soil Mapping Contributing to Sustainable Soil Management)
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36 pages, 48400 KiB  
Article
autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil
by Hugo Rodrigues, Marcos Bacis Ceddia, Gustavo Mattos Vasques, Sabine Grunwald, Ebrahim Babaeian and André Luis Oliveira Villela
Land 2025, 14(3), 604; https://doi.org/10.3390/land14030604 - 13 Mar 2025
Viewed by 433
Abstract
The reference area (RA) approach has been frequently used in soil surveying and mapping projects, since it allows for reduced costs. However, a crucial point in using this approach is the choice or delineation of an RA, which can compromise the accuracy of [...] Read more.
The reference area (RA) approach has been frequently used in soil surveying and mapping projects, since it allows for reduced costs. However, a crucial point in using this approach is the choice or delineation of an RA, which can compromise the accuracy of prediction models. In this study, an innovative algorithm that delineates RA (autoRA—automatic reference areas) is presented, and its efficiency is evaluated in Sátiro Dias, Bahia, Brazil. autoRA integrates multiple environmental covariates (e.g., geomorphology, geology, digital elevation models, temperature, precipitation, etc.) using the Gower’s Dissimilarity Index to capture landscape variability more comprehensively. One hundred and two soil profiles were collected under a specialist’s manual delineation to establish baseline mapping soil taxonomy. We tested autoRA coverages ranging from 10% to 50%, comparing them to RA manual delineation and a conventional “Total Area” (TA) approach. Environmental heterogeneity was insufficiently sampled at lower coverages (autoRA at 10–20%), resulting in poor classification accuracy (0.11–0.14). In contrast, larger coverages significantly improved performance: 30% yielded an accuracy of 0.85, while 40% and 50% reached 0.96. Notably, 40% struck the best balance between high accuracy (kappa = 0.65) and minimal redundancy, outperforming RA manual delineation (accuracy = 0.75) and closely matching the best TA outcomes. These findings underscore the advantage of applying an automated, diversity-driven strategy like autoRA before field campaigns, ensuring the representative sampling of critical environmental gradients to improve DSM workflows. Full article
(This article belongs to the Special Issue Predictive Soil Mapping Contributing to Sustainable Soil Management)
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22 pages, 13639 KiB  
Article
Post-hoc Evaluation of Sample Size in a Regional Digital Soil Mapping Project
by Daniel D. Saurette, Richard J. Heck, Adam W. Gillespie, Aaron A. Berg and Asim Biswas
Land 2025, 14(3), 545; https://doi.org/10.3390/land14030545 - 5 Mar 2025
Viewed by 451
Abstract
The transition from conventional soil mapping (CSM) to digital soil mapping (DSM) not only affects the final map products, but it also affects the concepts of scale, resolution, and sampling intensity. This is critical because in the CSM approach, sampling intensity is intricately [...] Read more.
The transition from conventional soil mapping (CSM) to digital soil mapping (DSM) not only affects the final map products, but it also affects the concepts of scale, resolution, and sampling intensity. This is critical because in the CSM approach, sampling intensity is intricately linked to the desired scale of soil map publication, which provided standardization of sampling. This is not the case for DSM where sample size varies widely by project, and sampling design studies have largely focused on where to sample without due consideration for sample size. Using a regional soil survey dataset with 1791 sampled and described soil profiles, we first extracted an external validation dataset using the conditioned Latin hypercube sampling (cLHS) algorithm and then created repeated (n = 10) sample plans of increasing size from the remaining calibration sites using the cLHS, feature space coverage sampling (FSCS), and simple random sampling (SRS). We then trained random forest (RF) models for four soil properties: pH, CEC, clay content, and SOC at five different depths. We identified the effective sample size based on the model learning curves and compared it to the optimal sample size determined from the Jensen–Shannon divergence (DJS) applied to the environmental covariates. Maps were then generated from models that used all the calibration points (reference maps) and from models that used the optimal sample size (optimal maps) for comparison. Our findings revealed that the optimal sample sizes based on the DJS analysis were closely aligned with the effective sample sizes from the model learning curves (815 for cLHS, 832 for FSCS, and 847 for SRS). Furthermore, the comparison of the optimal maps to the reference maps showed little difference in the global statistics (concordance correlation coefficient and root mean square error) and spatial trends of the data, confirming that the optimal sample size was sufficient for creating predictions of similar accuracy to the full calibration dataset. Finally, we conclude that the Ottawa soil survey project could have saved between CAD 330,500 and CAD 374,000 (CAD = Canadian dollars) if the determination of optimal sample size tools presented herein existed during the project planning phase. This clearly illustrates the need for additional research in determining an optimal sample size for DSM and demonstrates that operationalization of DSM in public institutions requires a sound scientific basis for determining sample size. Full article
(This article belongs to the Special Issue Predictive Soil Mapping Contributing to Sustainable Soil Management)
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17 pages, 4300 KiB  
Article
Soil Geochemical Mapping of the Sal Island (Cape Verde): Ecological and Human Health Risk Assessment
by Luísa Diniz, Gelson Carlos, Carmelita Miranda, Pedro Dinis, Rosa Marques, Fernando Tavares Rocha, Eduardo Ferreira da Silva, Agostinho Almeida and Marina Cabral Pinto
Land 2024, 13(8), 1139; https://doi.org/10.3390/land13081139 - 25 Jul 2024
Cited by 2 | Viewed by 1279
Abstract
Geochemical mapping is the base of knowledge needed to determine the critical contents of potential toxic elements and the potentially hazardous regions on the planet. This work presents maps of baseline values of chemical elements in the soils of Sal Island (Cape Verde) [...] Read more.
Geochemical mapping is the base of knowledge needed to determine the critical contents of potential toxic elements and the potentially hazardous regions on the planet. This work presents maps of baseline values of chemical elements in the soils of Sal Island (Cape Verde) and the assessment of their ecological and human health risks. According to the results, Ba, Co, Ni, and V baseline values are above the international guidelines for agricultural and residential proposed uses. Arsenic in the soil overlying the Ancient Eruptive Complex shows a high potential ecological risk factor. It is not clear if high As contents in soils have a geogenic or anthropogenic source. Hazard indexes (HI) were calculated for children and adults. For children, HI is higher than 1 for Co, Cr, and Mn, indicating potential non-carcinogenic risk. These elements are present in high content in soils covering Quaternary sediments, the Monte Grande-Pedra Lume Formation, and the Ancient Eruptive Complex, inducing belief in a geogenic source. For the other elements and for adults, there is no potential non-carcinogenic risk. Cancer risk (CR) was calculated for As, Cd, Cr, and Ni exposures for adults and children, and the results are mainly lower than the carcinogenic target risk value, indicating no cancer risk. Only in a few soil samples are CR results slightly higher than the carcinogenic target risk of 1 × 10−4 2 × 10−6 for adults exposed to Cr by inhalation. It is important to emphasize that these results of the health risk associated with exposure are likely to overestimate the bioavailable fractions of the elements in the soil once it is used as aqua regia instead of physiological fluids to digest the soil. However, since measured concentrations of potential toxic elements in soil reveal that they can be harmful to both the environment and human health, regional activities such as agriculture or water exploitation must be controlled by competent authorities. These conclusions highlight the insights and the applicability of soil geochemistry surveys for future policy progress, which are particularly relevant in developing countries like the Cape Verde archipelago. Full article
(This article belongs to the Special Issue Predictive Soil Mapping Contributing to Sustainable Soil Management)
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21 pages, 11576 KiB  
Article
Sample Size Optimization for Digital Soil Mapping: An Empirical Example
by Daniel D. Saurette, Richard J. Heck, Adam W. Gillespie, Aaron A. Berg and Asim Biswas
Land 2024, 13(3), 365; https://doi.org/10.3390/land13030365 - 14 Mar 2024
Cited by 6 | Viewed by 2701
Abstract
In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes [...] Read more.
In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal DJS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of DJS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation. Full article
(This article belongs to the Special Issue Predictive Soil Mapping Contributing to Sustainable Soil Management)
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