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25 pages, 8308 KB

Open AccessArticle

Long-Term Assessment of Soil Carbon Dynamics in Post-Fire Conditions: Evidence from Digital Soil Mapping Approaches

by Yacine Benhalima, Erika S. Santos and Diego Arán

Soil Syst. 2026, 10(1), 17; https://doi.org/10.3390/soilsystems10010017 - 20 Jan 2026

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Abstract

This study examined long-term changes in soil carbon stock dynamics 11 and 19 years after fire under different severities at 0–5 and 0–25 cm depths with a digital soil mapping approach. Linear (MLR) and non-linear models (RF, SVR, XGBoost) combined with feature selection [...] Read more.

This study examined long-term changes in soil carbon stock dynamics 11 and 19 years after fire under different severities at 0–5 and 0–25 cm depths with a digital soil mapping approach. Linear (MLR) and non-linear models (RF, SVR, XGBoost) combined with feature selection methods (r < 0.8, FFS, Boruta) were used to predict bulk density (BD), total C, and C stock. Distributional biases were evaluated with Kolmogorov–Smirnov statistics and corrected by Quantile Mapping (QM). RF-FFS performed best for BD and total C at 0–5, while RF-SVR outperformed for C stock and all properties at 0–25. Total C was 49% higher at 0–5, whereas C stock was 7.57 times greater at 0–25. Both models underestimated variability, especially for C stock. At 0–25, bulk density decreased after fire, particularly under conditions of medium severity, while total C increased following the same tendency. The results showed that fire’s legacy is still present in the ecosystem after one and two decades. This is particularly evident at greater depths, where long-term C stock is lower. Full article

(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)

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22 pages, 3921 KB

Open AccessArticle

Non-Invasive Soil Texture Prediction Using Machine Learning and Multi-Source Environmental Data

by Mohamed Rajhi, Tamas Deak and Endre Dobos

Soil Syst. 2026, 10(1), 8; https://doi.org/10.3390/soilsystems10010008 - 31 Dec 2025

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Abstract

Accurate prediction of soil texture is essential for effective soil management, precision agriculture, and hydrological modeling. This study proposes a novel, data-driven approach for estimating soil texture without the need for laboratory-based analysis. High-frequency in situ soil moisture measurements from EnviroSCAN (Sentek Technologies, [...] Read more.

Accurate prediction of soil texture is essential for effective soil management, precision agriculture, and hydrological modeling. This study proposes a novel, data-driven approach for estimating soil texture without the need for laboratory-based analysis. High-frequency in situ soil moisture measurements from EnviroSCAN (Sentek Technologies, Stepney, Australia) sensors and satellite-derived vegetation indices (NDVI) from Sentinel-2 were collected across 25 sites in Hungary. Temporal soil moisture dynamics were encoded using a Long Short-Term Memory (LSTM) neural network, designed to capture soil-specific hydrological response behavior from time-series data. The resulting latent embeddings were subsequently used within an ordinal regression framework to predict ordered soil texture classes, explicitly enforcing physical consistency between classes. Model performance was evaluated using leave-one-soil-out cross-validation, achieving an overall classification accuracy of 0.54 and a mean absolute error (MAE) of 0.50, indicating predominantly adjacent-class errors. The proposed approach demonstrates that soil texture can be inferred from dynamic environmental responses alone, offering a transferable alternative to fraction-based regression models and supporting scalable sensor calibration and digital soil mapping in data-scarce regions. Full article

(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)

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70 pages, 16474 KB

Open AccessArticle

Assessment of the Accuracy of ISRIC and ESDAC Soil Texture Data Compared to the Soil Map of Greece: A Statistical and Spatial Approach to Identify Sources of Differences

by Stylianos Gerontidis, Konstantinos X. Soulis, Alexandros Stavropoulos, Evangelos Nikitakis, Dionissios P. Kalivas, Orestis Kairis, Dimitrios Kopanelis, Xenofon K. Soulis and Stergia Palli-Gravani

Soil Syst. 2025, 9(4), 133; https://doi.org/10.3390/soilsystems9040133 - 25 Nov 2025

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Abstract

Soil maps are essential for managing Earth’s resources, but the accuracy of widely used global and pan-European digital soil maps in heterogeneous landscapes remains a critical concern. This study provides a comprehensive evaluation of two prominent datasets, ISRIC-SoilGrids and the European Soil Data [...] Read more.

Soil maps are essential for managing Earth’s resources, but the accuracy of widely used global and pan-European digital soil maps in heterogeneous landscapes remains a critical concern. This study provides a comprehensive evaluation of two prominent datasets, ISRIC-SoilGrids and the European Soil Data Centre (ESDAC), by comparing their soil texture predictions against the detailed Greek National Soil Map, which is based on over 10,000 field samples. The results from statistical and spatial analyses reveal significant discrepancies and weak correlations, with a very low overall accuracy for soil texture class prediction (19–21%) and high Root Mean Square Error (RMSE) values ranging from 13% to 19%. The global models failed to capture local variability, showing very low explanatory power (R² < 0.2) and systematically underrepresenting soils with extreme textures. Furthermore, these prediction errors are not entirely random but are significantly clustered in hot spots linked to distinct parent materials and geomorphological features. Our findings demonstrate that while invaluable for large-scale assessments, the direct application of global soil databases for regional policy or precision agriculture in a geologically complex country like Greece is subject to considerable uncertainty, highlighting the critical need for local calibration and the integration of national datasets to improve the reliability of soil information. Full article

(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)

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24 pages, 3279 KB

Open AccessArticle

A Framework Based on Isoparameters for Clustering and Mapping Geophysical Data in Pedogeomorphological Studies

by Gustavo Vieira Veloso, Danilo César de Mello, Heitor Paiva Palma, Murilo Ferre Mello, Lucas Vieira Silva, Elpídio Inácio Fernandes-Filho, Márcio Rocha Francelino, Tiago Osório Ferreira, José Cola Zanuncio, Davi Feital Gjorup, Roney Berti de Oliveira, Marcos Rafael Nanni, Renan Falcioni and José A. M. Demattê

Soil Syst. 2025, 9(4), 124; https://doi.org/10.3390/soilsystems9040124 - 8 Nov 2025

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Abstract

Understanding soil variability supports improved land use and soil security. This study aimed to generate uniform geophysical classes by integrating data from three proximal geophysical sensors with synthetic soil and satellite images using machine learning, proposing a soil survey protocol. Geophysical data—natural gamma-ray [...] Read more.

Understanding soil variability supports improved land use and soil security. This study aimed to generate uniform geophysical classes by integrating data from three proximal geophysical sensors with synthetic soil and satellite images using machine learning, proposing a soil survey protocol. Geophysical data—natural gamma-ray emissions (eU, eTh, K⁴⁰), magnetic susceptibility (κ), and apparent electrical conductivity (ECa)—were collected in Piracicaba, Brazil, and clustered into homogeneous geophysical-isoparameter classes. These classes were modeled alongside Synthetic Soil Images (SYSIs), Sentinel-2 (0.45–2.29 μm), Landsat (0.43–12.51 μm) imagery, and morphometric variables. Empirical validation compared the resulting geophysical-isoparameter map with conventional pedological and lithological maps. The Support Vector Machine (SVM) algorithm exhibited the best classification performance. Results demonstrated that geophysical sensors quantitatively and qualitatively capture soil attributes linked to formation processes and types. The geophysical-isoparameter map correlated well with pedological and lithological patterns. The proposed protocol offers soil scientists a practical tool to delineate soil and lithological units using combined sensor data. Promoting collaboration among pedologists, pedometric mappers, and remote sensing experts, this approach presents a novel framework to enhance soil survey accuracy and efficiency. Full article

(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)

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11 pages, 1167 KB

Open AccessArticle

Towards the Application of Complex-Valued Variograms in Soil Research

by Jarosław Zawadzki

Soil Syst. 2025, 9(4), 122; https://doi.org/10.3390/soilsystems9040122 - 7 Nov 2025

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Abstract

Variograms are a cornerstone of spatial analysis in geostatistics, traditionally applied to real-valued variables under the intrinsic hypothesis. Many soil properties, particularly when integrating magnetic and geochemical measurements, can be expressed as complex-valued variables that capture both magnitude and phase information. In the [...] Read more.

Variograms are a cornerstone of spatial analysis in geostatistics, traditionally applied to real-valued variables under the intrinsic hypothesis. Many soil properties, particularly when integrating magnetic and geochemical measurements, can be expressed as complex-valued variables that capture both magnitude and phase information. In the case of magnetic susceptibility, the imaginary component reflects energy losses associated with viscous magnetization, which in soils can indicate the presence of pedogenic ferrimagnetic minerals, while its relative increase may also reveal anthropogenic magnetite contamination. This study examines the formulation and application of variograms for such complex-valued variables in the context of soil research. Two complementary definitions are considered: an intrinsic-based approach, which directly estimates the variogram from increments and is applicable under the intrinsic hypothesis, and a covariance-based approach, which requires stronger second-order stationarity. Simulated complex-valued soil property data with controlled spatial structures were used to compare the behaviour of these formulations with their real-valued counterparts. The findings indicate that complex-valued variograms preserve additional spatial information, particularly related to local phase shifts, while maintaining compatibility with conventional variographic modelling. Full article

(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)

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22 pages, 2913 KB

Open AccessArticle

Spatial Variability and Temporal Changes of Soil Properties Assessed by Machine Learning in Córdoba, Argentina

by Mariano A. Córdoba, Susana B. Hang, Catalina Bozzer, Carolina Alvarez, Lautaro Faule, Esteban Kowaljow, María V. Vaieretti, Marcos D. Bongiovanni and Mónica G. Balzarini

Soil Syst. 2025, 9(4), 109; https://doi.org/10.3390/soilsystems9040109 - 10 Oct 2025

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Understanding the temporal dynamics and spatial distribution of key soil properties is essential for sustainable land management and informed decision-making. This study assessed the spatial variability and decadal changes (2013–2023) of topsoil properties in Córdoba, central Argentina, using digital soil mapping (DSM) and [...] Read more.

Understanding the temporal dynamics and spatial distribution of key soil properties is essential for sustainable land management and informed decision-making. This study assessed the spatial variability and decadal changes (2013–2023) of topsoil properties in Córdoba, central Argentina, using digital soil mapping (DSM) and machine learning (ML) algorithms. Three ML methods—Quantile Regression Forest (QRF), Cubist, and Support Vector Machine (SVM)—were compared to predict soil organic matter (SOM), extractable phosphorus (P), and pH at 0–20 cm depth, based on environmental covariates related to site climate, vegetation, and topography. QRF consistently outperformed the other models in prediction accuracy and uncertainty, confirming its suitability for DSM in heterogeneous landscapes. Prediction uncertainty was higher in marginal mountainous areas than in intensively managed plains. Over ten years, SOM, P, and pH exhibited changes across land-use classes (cropland, pasture, and forest). Extractable P declined by 15–35%, with the sharpest reduction in croplands (−35.4%). SOM decreased in croplands (−6.7%) and pastures (−3.1%) but remained stable in forests. pH trends varied, with slight decreases in croplands and forests and a small increase in pastures. By integrating high-resolution mapping and temporal assessment, this study advances DSM applications and supports regional soil monitoring and sustainable land-use planning. Full article

(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)

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14 pages, 2833 KB

Open AccessArticle

Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions

by Francesca Antonucci, Simona Violino, Loredana Canfora, Małgorzata Tartanus, Ewa M. Furmanczyk, Sara Turci, Maria G. Tommasini, Nika Cvelbar Weber, Jaka Razinger, Morgane Ourry, Samuel Bickel, Thomas A. J. Passey, Anne Bohr, Heinrich Maisel, Massimo Pugliese, Francesco Vitali, Stefano Mocali, Federico Pallottino, Simone Figorilli, Anne D. Jungblut, Hester J. van Schalkwyk, Corrado Costa and Eligio Malusà Show full author list Hide full author list

Soil Syst. 2025, 9(1), 10; https://doi.org/10.3390/soilsystems9010010 - 26 Jan 2025

Cited by 4 | Viewed by 2199 | Correction

Abstract

Background: Self-organizing maps (SOMs) are a class of neural network algorithms able to visually describe a high-dimensional dataset onto a two-dimensional grid. SOMs were explored to classify soils based on an array of physical, chemical, and biological parameters. Methods: The SOM analysis was [...] Read more.

Background: Self-organizing maps (SOMs) are a class of neural network algorithms able to visually describe a high-dimensional dataset onto a two-dimensional grid. SOMs were explored to classify soils based on an array of physical, chemical, and biological parameters. Methods: The SOM analysis was performed considering soil physical, chemical, and microbial data gathered from an array of apple orchards and strawberry plantations managed by organic or conventional methods and located in different European climatic zones. Results: The SOM analysis considering the “climatic zone” categorical variables was able to discriminate the samples from the three zones for both crops. The zones were associated with different soil textures and chemical characteristics, and for both crops, the Continental zone was associated with microbial parameters—including biodiversity indices derived from the NGS data analysis. However, the SOM analysis based on the “management method” categorical variables was not able to discriminate the soils between organic and integrated management. Conclusions: This study allowed for the discrimination of soils of medium- and long-term fruit crops based on their pedo-climatic characteristics and associating these characteristics to some indicators of the soil biome, pointing to the possibility of better understanding the interactions among diverse variables, which could support unraveling the intricate web of relationships that define soil quality. Full article

(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)

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Open AccessCorrection

Correction: Antonucci et al. Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions. Soil Syst. 2025, 9, 10

by Francesca Antonucci, Simona Violino, Loredana Canfora, Małgorzata Tartanus, Ewa M. Furmanczyk, Sara Turci, Maria G. Tommasini, Nika Cvelbar Weber, Jaka Razinger, Morgane Ourry, Samuel Bickel, Thomas A. J. Passey, Anne Bohr, Heinrich Maisel, Massimo Pugliese, Francesco Vitali, Stefano Mocali, Federico Pallottino, Simone Figorilli, Anne D. Jungblut, Hester J. van Schalkwyk, Corrado Costa and Eligio Malusà Show full author list Hide full author list

Soil Syst. 2025, 9(3), 76; https://doi.org/10.3390/soilsystems9030076 - 14 Jul 2025

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Anne D [...] Full article

(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)

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