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Land
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Digital Soil Mapping for Soil Health Monitoring in Agricultural Lands
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Digital Soil Mapping for Soil Health Monitoring in Agricultural Lands

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land, Soil and Water".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3776

Special Issue Editors

Dr. João Augusto Coblinski

E-Mail Website
Guest Editor
Institute of Soil Science and Plant Cultivation, Puławy, Poland
Interests: digital soil mapping and modeling; pedometrics; soil modeling; soil threats; soil health; land degradation
Dr. Sylwia Pindral

E-Mail Website
Guest Editor
Institute of Soil Science and Plant Cultivation—State Research Institute, Czartoryskich 8, 24-100 Puławy, Poland
Interests: urban soil mapping; digital soil mapping; pedodiversity; soil transformation; SUITMAs; spatial analyzes
Dr. Gabriele Buttafuoco

E-Mail Website
Guest Editor
National Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean, 87036 Rende, Italy
Interests: soil science; pedometrics; geostatistics; precision agriculture; hydrology and water resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil health assessment and monitoring are essential for evaluating the capacity of soils to support productivity and delivery ecosystem services through different soil functions. Soils are also threatened because of changes in climate, landuse, and management. As a result, millions of hectares of agricultural land are degraded annually worldwide, affecting soil health and its capacity to deliver essential ecosystem services. Moreover, farmers, consultants, and decision makers for different purposes need detailed knowledge of soils. Therefore, there is an increasing interest in measuring and mapping soil properties as indicators of soil health at different spatial scales for soil management and for preventing, controlling, and monitoring degradation processes. In this context, digital soil mapping (DSM) is a valuable tool to enhance the understanding of spatial soil variability at a feasible cost and reproducibility. Particularly, DSM is suitable for benefiting from many measuring technology advances, such as proximal and remote sensing, as well as statistical, geostatistical, and machine learning approaches for modelling and mapping soil variation.

This Special Issue welcome the submission of original research articles and review articles focusing on DSM for soil properties, threats, degradation, function and ecosystem service estimations, long-term projections under global change’s influence, new DSM techniques, and mapping and modelling using proximal and remote sensing.

We look forward to receiving your original research articles and reviews.

Dr. João Augusto Coblinski
Dr. Sylwia Pindral
Dr. Gabriele Buttafuoco
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • digital soil mapping
  • pedometrics
  • remote sensing
  • proximal sensing
  • soil functions
  • soil threats
  • soil ecosystem services
  • soil modelling

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Published Papers (3 papers)

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Research

21 pages, 3864 KB  
Article
Comparison of National and Regional Assessments of Soil Loss Rates by Water Erosion and Soil Erosion Control: An Application to the Tuscany Region (Italy)
by Eduardo Medina-Roldán, Gabriele Buttafuoco, Lorenzo Gardin, Romina Lorenzetti and Fabrizio Ungaro
Land 2026, 15(3), 417; https://doi.org/10.3390/land15030417 - 4 Mar 2026
Viewed by 412
Abstract
Soil erosion assessments for policy are often derived from continental-scale datasets, but their suitability for regional planning remains unclear. This study compares two Revised Universal Soil Loss Equation (RUSLE) applications for Tuscany, Italy: one using high-resolution regional data (TuscReg) and another using European-scale [...] Read more.
Soil erosion assessments for policy are often derived from continental-scale datasets, but their suitability for regional planning remains unclear. This study compares two Revised Universal Soil Loss Equation (RUSLE) applications for Tuscany, Italy: one using high-resolution regional data (TuscReg) and another using European-scale data from the European Soil Data Centre (TuscNat). We found the mean estimated actual soil erosion rate was 58% higher in the regional assessment (10.7 vs. 6.8 Mg ha−1 yr−1). Remarkably, the spatial patterns diverged significantly in the complex landscapes characterizing some Tuscan soil regions. In mountainous areas like the Apuan Alps, TuscReg estimated soil erosion control (potential minus actual erosion) to be over 500 Mg ha−1 yr−1 greater than TuscNat for 30% of the area. Correlation analysis revealed these major differences were primarily driven by disparities in the rainfall erosivity (R) and soil erodibility (K) factors. Our results demonstrate that while EU-scale models provide a consistent, broad-scale overview, they can substantially underestimate erosion and the ecosystem service of erosion control in specific, high-risk environments. To implement policies like the EU Soil Monitoring Law (Directive (EU) 2025/2360), regional-scale data are essential to accurately identify priority areas for soil conservation and set meaningful local thresholds. Full article
(This article belongs to the Special Issue Digital Soil Mapping for Soil Health Monitoring in Agricultural Lands)
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17 pages, 7402 KB  
Article
Digital Mapping of Soil pH Using Tree-Based Models Coupled with Residual Kriging
by Yanyan Tian, Suyang Cao, Pei Sun, Quanguo Kang, Shaohua Liu, Xinao Zheng, Lifei Wei and Qikai Lu
Land 2026, 15(3), 365; https://doi.org/10.3390/land15030365 - 25 Feb 2026
Viewed by 401
Abstract
Soil pH is a critical soil property governing nutrient availability and ecosystem functioning. Digital mapping of its spatial distribution is essential for precision agriculture and sustainable land management. This study performs a comparative analysis of six tree-based models coupled with residual kriging (RK) [...] Read more.
Soil pH is a critical soil property governing nutrient availability and ecosystem functioning. Digital mapping of its spatial distribution is essential for precision agriculture and sustainable land management. This study performs a comparative analysis of six tree-based models coupled with residual kriging (RK) for 30 m resolution mapping of soil pH in Shayang County, China. Specifically, random forest (RF), extremely randomized trees (ERT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) were used. Based on 1343 soil samples and 32 environmental variables, experimental results demonstrate that the integration of RK enhanced the prediction accuracy of all standalone models by taking the spatial dependence of residuals into account. Among the models, CatBoost-RK achieved the best performance with an R2 of 0.7265, RMSE of 0.5072, and RPD of 1.9122, closely followed by ERT-RK and RF-RK. The analysis of variable importance identified soil type (ST) and mean annual precipitation (MAP) as the most critical factors affecting soil pH distribution. The generated 30 m resolution soil pH map reveals distinct patterns across different land use types, with croplands showing lower soil pH and grasslands exhibiting higher pH with greater variability. These findings confirm the effectiveness of the hybrid ML-RK framework and provide valuable insights for selecting optimal modeling strategies in digital soil mapping. Full article
(This article belongs to the Special Issue Digital Soil Mapping for Soil Health Monitoring in Agricultural Lands)
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16 pages, 11425 KB  
Article
Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation
by Azamat Suleymanov, Mikhail Komissarov, Mikhail Aivazyan, Ruslan Suleymanov, Ilnur Bikbaev, Arseniy Garipov, Raphak Giniyatullin, Olesia Ishkinina, Iren Tuktarova and Larisa Belan
Land 2025, 14(5), 931; https://doi.org/10.3390/land14050931 - 25 Apr 2025
Viewed by 1670
Abstract
Unmanned aerial vehicles (UAVs) are rapidly becoming a popular tool for digital soil mapping at a large-scale. However, their applicability in areas with homogeneous vegetation (i.e., not bare soil) has not been fully investigated. In this study, we aimed to predict soil organic [...] Read more.
Unmanned aerial vehicles (UAVs) are rapidly becoming a popular tool for digital soil mapping at a large-scale. However, their applicability in areas with homogeneous vegetation (i.e., not bare soil) has not been fully investigated. In this study, we aimed to predict soil organic carbon, soil texture at several depths, as well as the thickness of the AB soil horizon and penetration resistance using a machine learning algorithm in combination with UAV images. We used an area in the Eurasian steppe zone (Republic of Bashkortostan, Russia) covered with the Stipa vegetation type as a test plot, and collected 192 soil samples from it. We estimated the models using a cross-validation approach and spatial prediction uncertainties. To improve the prediction performance, we also tested the inclusion of oblique geographic coordinates (OGCs) as covariates that reflect spatial position. The following results were achieved: (i) the predictive models demonstrated poor performance using only UAV images as predictors; (ii) the incorporation of OGCs slightly improved the predictions, whereas their uncertainties remained high. We conclude that the inability to accurately predict soil properties using these predictor variables (UAV and OGC) is likely due to the limited access to soil spectral signatures and the high variability of soil properties within what appears to be a homogeneous site, particularly in relation to soil-forming factors. Our results demonstrated the limitations of UAVs’ application for modeling soil properties on a site with homogeneous vegetation, whereas including spatial autocorrelation information can benefit and should be not ignored in further studies. Full article
(This article belongs to the Special Issue Digital Soil Mapping for Soil Health Monitoring in Agricultural Lands)
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