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Soil Systems
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Contemporary Applications of Geostatistics to Soil Studies
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Contemporary Applications of Geostatistics to Soil Studies

A special issue of Soil Systems (ISSN 2571-8789).

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 13567

Special Issue Editor

Prof. Dr. Jarosław Zawadzki

E-Mail Website
Guest Editor
Hydro and Environmental Engineering, Faculty of Building Services, Warsaw University of Technology, Nowowiejska Str., 20, 00-653 Warsaw, Poland
Interests: environmental sciences; magnetism and magnetic materials; geophysics and geochemistry; soil sciences, remote sensing; geostatistics; statistics and probability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue is to publish contemporary, outstanding papers presenting cutting-edge research in the field of soil geostatistical studies.

Although geostatistics has its origins mainly in mining and geology, it is nowadays commonly recognized as a forensic method in environmental research. In particular, its application in soil research has been developed intensively. This is due to the exceptional complexity of the soil environment, the description of which requires advanced statistical tools to study spatial and temporal phenomena occurring in the soil. Another reason for the constantly growing interest in the use of geostatistical methods in soil research is the rapid development of the field, laboratory, and remote measurement methods of various soil parameters, including satellite observations (e.g., of soil moisture measurements, field magnetometry, and spectroscopic methods). Conversely, the use of geostatistical methods in soil sciences had, and still has, a substantial influence on the development and shape of these methods. Notably, in this context, and in the International Decade of Soils (2015-2024), this Special Issue of Soil Systems aims to bring together present-day, prominent research on geostatistics applications in soil research.

Prof. Dr. Jarosław Zawadzki
Guest Editor

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. Soil Systems is an international peer-reviewed open access quarterly 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 1800 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

  • soil measurements
  • soil spatial variability
  • soil properties mapping
  • geostatistical methods
  • soil data integration
  • soil sampling
  • spatio-temporal soil modeling
  • soil biodiversity
  • advances in soil geostatistics
  • soil remote sensing

Published Papers (6 papers)

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Research

28 pages, 7247 KiB  
Article
Assessing Soil Prediction Distributions for Forest Management Using Digital Soil Mapping
by Gonzalo Gavilán-Acuna, Nicholas C. Coops, Guillermo F. Olmedo, Piotr Tompalski, Dominik Roeser and Andrés Varhola
Soil Syst. 2024, 8(2), 55; https://doi.org/10.3390/soilsystems8020055 - 16 May 2024
Viewed by 419
Abstract
Texture, soil organic matter (SOM), and soil depth (SoD) are crucial properties in forest management because they can supply spatial information on forest site productivity and guide fertilizer applications. However, soil properties possess an inherent uncertainty that must be mapped to enhance decision [...] Read more.
Texture, soil organic matter (SOM), and soil depth (SoD) are crucial properties in forest management because they can supply spatial information on forest site productivity and guide fertilizer applications. However, soil properties possess an inherent uncertainty that must be mapped to enhance decision making in management applications. Most digital soil mapping predictions primarily concentrate on the mean of the distribution, often neglecting the estimation of local uncertainty in soil properties. Additionally, there is a noticeable scarcity of practical soil examples to demonstrate the prediction uncertainty for the benefit of forest managers. In this study, following a digital soil mapping (DSM) approach, a Quantile Regression Forest (QRF) model was developed to generate high-resolution maps and their uncertainty regarding the texture, SoD, and SOM, which were expressed as standard deviation (Sd) values. The results showed that the SOM (R2 = 0.61, RMSE = 2.03% and with an average Sd = 50%), SoD (R2 = 0.74 and RMSE = 19.4 cm), clay (R2 = 0.63, RMSE = 10.5% and average Sd = 29%), silt (R2 = 0.59, RMSE = 6.26% and average Sd = 33%), and sand content (R2 = 0.55, RMSE = 9.49% and average Sd = 35%) were accurately estimated for forest plantations in central south Chile. A practical demonstration of precision fertilizer application, utilizing the predictive distribution of SOM, effectively showcased how uncertainty in soil attributes can be leveraged to benefit forest managers. This approach holds potential for optimizing resource allocation and maximizing economic benefits. Full article
(This article belongs to the Special Issue Contemporary Applications of Geostatistics to Soil Studies)
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15 pages, 10465 KiB  
Article
Surface Formations Salinity Survey in an Estuarine Area of Northern Morocco, by Crossing Satellite Imagery, Discriminant Analysis, and Machine Learning
by Youssouf El Jarjini, Moad Morarech, Vincent Valles, Abdessamad Touiouine, Meryem Touzani, Youssef Arjdal, Abdoul Azize Barry and Laurent Barbiero
Soil Syst. 2023, 7(2), 33; https://doi.org/10.3390/soilsystems7020033 - 8 Apr 2023
Viewed by 1773
Abstract
The salinity of estuarine areas in arid or semi-arid environments can reach high values, conditioning the distribution of vegetation and soil surface characteristics. While many studies focused on the prediction of soil salinity as a function of numerous parameters, few attempted to explain [...] Read more.
The salinity of estuarine areas in arid or semi-arid environments can reach high values, conditioning the distribution of vegetation and soil surface characteristics. While many studies focused on the prediction of soil salinity as a function of numerous parameters, few attempted to explain the role of salinity and its distribution within the soil profile in the pattern of landscape units. In a wadi estuary in northern Morocco, landscape units derived from satellite imagery and naturalistic environmental analysis are compared with a systematic survey of salinity by means of apparent electrical conductivity (Eca) measurements. The comparison is based on the allocation of measurement points to an area of the estuary from Eca measurements alone, using linear discriminant analysis and four machine learning methods. The results show that between 57 and 66% of the points are well-classified, highlighting that salinity is a major factor in the discrimination of estuary zones. The distribution of salinity is mainly the result of the interaction between capillary rise and flooding by the tides and the wadi. The location of the misclassified points is analysed and discussed, as well as the possible causes of the confusions. Full article
(This article belongs to the Special Issue Contemporary Applications of Geostatistics to Soil Studies)
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17 pages, 2530 KiB  
Article
Spatial Variability of Soil Erodibility at the Rhirane Catchment Using Geostatistical Analysis
by Ouafa Othmani, Kamel Khanchoul, Sana Boubehziz, Hamza Bouguerra, Abderraouf Benslama and Jose Navarro-Pedreño
Soil Syst. 2023, 7(2), 32; https://doi.org/10.3390/soilsystems7020032 - 6 Apr 2023
Cited by 2 | Viewed by 2400
Abstract
Soil erodibility is one of the most crucial factors used to estimate soil erosion by applying modeling techniques. Soil data from soil maps are commonly used to create maps of soil erodibility for soil conservation planning. This study analyzed the spatial variability of [...] Read more.
Soil erodibility is one of the most crucial factors used to estimate soil erosion by applying modeling techniques. Soil data from soil maps are commonly used to create maps of soil erodibility for soil conservation planning. This study analyzed the spatial variability of soil erodibility by using a digital elevation model (DTM) and surface soil sample data at the Rhirane catchment (Algeria). A total of 132 soil samples were collected of up to 20 cm in depth. The spatial distributions of the K-value and soil physical properties (permeability, organic matter, and texture) were used to elaborate ordinary Kriging interpolation maps. Results showed that mean values of soil organic matter content were statistically different between Chromic Cambisols (M = 3.4%) vs. Calcic Cambisols (M = 2.2%). The analysis of variance of the organic matter provided a tool for identifying significant differences when comparing means between the soil types. The soil granulometry is mainly composed of silt and fine sand. The soil erodibility showed values varying between 0.012 and 0.077 with an average of 0.034, which was greater in soils with calcic horizons. Statistical evaluation by using Pearson’s correlation revealed positive correlations between erodibility and silt (0.63%), and negative correlations with sand (−0.16%), clay (−0.56%), organic matter (−0.32%), permeability (−0.41%), soil structure (−0.40%), and the soil stability index (−0.26%). The variability analysis of the K-factor showed moderate spatial dependency with the soil erodibility map indicating moderate to highly erodible risk in cropland and sparse grassland land uses. Overall, the study provides scientific support for soil conservation management and appropriate agricultural food practices for food supply. Full article
(This article belongs to the Special Issue Contemporary Applications of Geostatistics to Soil Studies)
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28 pages, 6058 KiB  
Article
Proximal and Remote Sensing Data Integration to Assess Spatial Soil Heterogeneity in Wild Blueberry Fields
by Allegra Johnston, Viacheslav Adamchuk, Athyna N. Cambouris, Jean Lafond, Isabelle Perron, Julie Lajeunesse, Marc Duchemin and Asim Biswas
Soil Syst. 2022, 6(4), 89; https://doi.org/10.3390/soilsystems6040089 - 29 Nov 2022
Cited by 1 | Viewed by 2193
Abstract
Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (ECa), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil [...] Read more.
Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (ECa), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and ECa at multiple depths were collected from two experimental fields (referred as FieldUnd, FieldFlat) in Normandin, Quebec, Canada. Soil samples were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CVUnd = 54.4%, CVFlat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) ECa sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest ECa depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high ECa/low elevation versus low ECa/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in FieldUnd revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high ECa/low elevation; low ECa/high elevation; bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios. Full article
(This article belongs to the Special Issue Contemporary Applications of Geostatistics to Soil Studies)
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14 pages, 2530 KiB  
Article
Evaluation and Spatial Variability of Cryogenic Soil Properties (Yamal-Nenets Autonomous District, Russia)
by Azamat Suleymanov, Timur Nizamutdinov, Evgeniya Morgun and Evgeny Abakumov
Soil Syst. 2022, 6(3), 65; https://doi.org/10.3390/soilsystems6030065 - 4 Aug 2022
Cited by 9 | Viewed by 2361
Abstract
Agricultural development in northern polar areas has potential as a result of global warming. Such expansion requires modern soil surveys and large-scale maps. In this study, the abandoned arable experimental field founded by I.G. Eichfeld one century ago in Salekhard city (Russian Arctic), [...] Read more.
Agricultural development in northern polar areas has potential as a result of global warming. Such expansion requires modern soil surveys and large-scale maps. In this study, the abandoned arable experimental field founded by I.G. Eichfeld one century ago in Salekhard city (Russian Arctic), located in the polar circle, was investigated. Our aims were to assess the nutritional soil properties and their spatial variability. For spatial assessment and mapping, ordinary kriging (OK) and inverse distance-weighted (IDW) methods were employed. We found that due to long-term agriculture use, the soil cover was represented by a unique Plaggic Podzol (Turbic) that is not typical of the region. The soil was characterized by relatively low soil organic carbon (SOC) content, high acidity and a high content of plant-available forms of phosphorus in the humus-accumulative horizon. The results showed that some properties (pH H2O, pH CaCl2) were characterized by large-scale heterogeneity and showed clear spatial dependence. However, some properties (ammonium and nitrate nitrogen, basal respiration) showed a pure-nugget effect, presumably due to experimentation with fertilizer over a long period of time. Full article
(This article belongs to the Special Issue Contemporary Applications of Geostatistics to Soil Studies)
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20 pages, 2135 KiB  
Article
Tillage Management Impacts on Soil Phosphorus Variability under Maize–Soybean Rotation in Eastern Canada
by Jeff D. Nze Memiaghe, Athyna N. Cambouris, Noura Ziadi and Antoine Karam
Soil Syst. 2022, 6(2), 45; https://doi.org/10.3390/soilsystems6020045 - 5 May 2022
Cited by 4 | Viewed by 3001
Abstract
Conservation tillage, including no-tillage (NT), is being used increasingly with respect to conventional tillage (CT) to mitigate soil erosion, improve water conservation and prevent land degradation. However, NT increases soil phosphorus (P) stratification, causing P runoff and eutrophication. For sustainable P management, fertilization [...] Read more.
Conservation tillage, including no-tillage (NT), is being used increasingly with respect to conventional tillage (CT) to mitigate soil erosion, improve water conservation and prevent land degradation. However, NT increases soil phosphorus (P) stratification, causing P runoff and eutrophication. For sustainable P management, fertilization must be balanced between P sources and actual crop demand. To reduce P losses to the environment, it is important to better understand P spatial variability in NT fields. Little is known about tillage impacts on field-scale P spatial variabi-lity in precision agriculture. This study examines tillage impacts on spatial variability of soil-avai-lable P in a maize–soybean rotation, in two commercial fields, denoted CT (10.8 ha) and NT (9.5 ha), with the aim of improving P fertilizer recommendations in Eastern Canada. NPK fertilizers were applied to the soils (Humic Gleysols) following local recommendations. Soil samples were collected in fall 2014 in regular 35 m by 35 m grids, at 0–5 and 5–20 cm depths, providing 141 and 134 geore-ferenced points for CT and NT fields, respectively. Available P and other elements were analyzed by Mehlich-3 extraction (M3), and the P saturation index (P/Al)M3 was calculated. Variability of soil-available P in both fields ranged from moderate to very high (32% to 60%). A mean (P/Al)M3 of 3% was found in both layers under CT, compared to 8% in the 0–5 cm layer and 6% in the 5–20 cm layer under NT. Relationships between P indices and other elements differed between tillage practices. This study highlights the need to improve P fertilizer recommendations in Eastern Canada. Full article
(This article belongs to the Special Issue Contemporary Applications of Geostatistics to Soil Studies)
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