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Soil Systems
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Digital Soil Mapping of Soil Functions
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Digital Soil Mapping of Soil Functions

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

Deadline for manuscript submissions: closed (15 April 2019) | Viewed by 30721

Special Issue Editors

Dr. Dominique Arrouays

E-Mail Website
Guest Editor
INRAE, InfoSol Unit, 45075 Orléans, France
Interests: digital soil mapping of soil properties and classes; global soil mapping; soil organic carbon
Special Issues, Collections and Topics in MDPI journals
Dr. Ir. Vera Leatitia (Titia) Mulder

E-Mail Website
Guest Editor
Wageningen University & Research, The Netherlands
Interests: soil mapping; soil monitoring; digital soil mapping; soil properties; soil carbon; soil quality assessment; soil functions in space and time; soil ecosystem services; planetary health; soil security

Special Issue Information

Dear Colleagues,

Soils ensure many essential functions and services, such as the provision of food, fiber and energy, regulating the water cycle and extreme events (droughts, floods), recycling waste, and purifying water. Moreover, soils are essential for mitigating climate change, maintaining biodiversity, sustainable land use and ecosystem functioning, among others. Spatial and temporal modelling and mapping of these functions and services has become a contemporary research topic. This can be attributed on the one hand to the increased demand for such information from various stakeholders, from the national to global scale. On the other hand, it can be attributed to methodological and technological advances and increased data availability. Here, digital soil mapping has great potential for providing such information on various spatial and temporal scales. Digital soil mapping is a growing sub-discipline of soil science, aiming to provide soil information for a wide range of domains. It has been widely used since the 2000s for predicting soil classes or soil attributes in a numerical way, using soil data and environmental co-variates. Digital soil mapping of soil functions is less common and may constitute a step forward for end users, stakeholders and decision makers’ needs. This Special Issue on “Soil Systems” welcomes review and original research papers showing fundamental or applied advances on this topic. Papers should be of high scientific relevance and quality. They should be formatted according to the instructions for authors. All papers will be subjected to a peer-review process. Therefore, submitting a paper does not guarantee that it will be accepted and published. The deadline for submitting a paper is 15 December, 2018.

Dr. Ir. Dominique Arrouays
Dr. Ir. Vera Leatitia (Titia) Mulder
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. 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.

Published Papers (6 papers)

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Research

18 pages, 5359 KiB  
Article
The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran
by Elham Mehrabi-Gohari, Hamid Reza Matinfar, Azam Jafari, Ruhollah Taghizadeh-Mehrjardi and John Triantafilis
Soil Syst. 2019, 3(4), 65; https://doi.org/10.3390/soilsystems3040065 - 26 Sep 2019
Cited by 18 | Viewed by 4017
Abstract
To predict the soil texture fractions, 115 profiles were identified based on the Latin hypercube sampling technique, the horizons were sampled, and the clay, sand, and silt contents (in percentages) of soil samples were measured. Then equal-area quadratic spline depth functions were used [...] Read more.
To predict the soil texture fractions, 115 profiles were identified based on the Latin hypercube sampling technique, the horizons were sampled, and the clay, sand, and silt contents (in percentages) of soil samples were measured. Then equal-area quadratic spline depth functions were used to derive clay, sand, and silt contents at five standard soil depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm). Auxiliary variables used in this study include the terrain attributes (derived from a digital elevation model), Landsat 8 image data (acquired in 2015), geomorphological map, and spectrometric data (laboratory data). Artificial neural network (ANN), regression tree (RT), and neuro-fuzzy (ANFIS) models were used to make a correlation between soil data (clay, sand, and silt) and auxiliary variables. The results of this study showed that the ANFIS model was more accurate in the prediction of the three parameters of clay, silt, and sand than ANN and RT. Moreover, the ability of ANFIS model to estimate the soil texture fractions in the surface layers was higher than the lower layers. The mean coefficient of determination (R2) values calculated by 10-fold cross validation suggested the higher prediction performance in the upper depth intervals and higher prediction error in the lower depth intervals (e.g., R2 = 0.91, concordance correlation coefficient (CCC) = 0.90, RMSE = 4.00 g kg−1 for sand of 0–5 cm depth, and R2 = 0.68, CCC = 0.60, RMSE = 8.03 g kg−1 for 60–100 cm depth). The results also showed that the most important auxiliary variables are spectrometric data, multi-resolution, valley-bottom flatness index and wetness index. Overall, it is recommended to use ANFIS models for the digital mapping of soil texture fractions in other arid regions of Iran. Full article
(This article belongs to the Special Issue Digital Soil Mapping of Soil Functions)
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17 pages, 2852 KiB  
Article
Mapping Soil Biodiversity in Europe and the Netherlands
by Michiel Rutgers, Jeroen P. van Leeuwen, Dirk Vrebos, Harm J. van Wijnen, Ton Schouten and Ron G. M. de Goede
Soil Syst. 2019, 3(2), 39; https://doi.org/10.3390/soilsystems3020039 - 12 Jun 2019
Cited by 16 | Viewed by 9894
Abstract
Soil is fundamental for the functioning of terrestrial ecosystems, but our knowledge about soil organisms and the habitat they provide (shortly: Soil biodiversity) is poorly developed. For instance, the European Atlas of Soil Biodiversity and the Global Soil Biodiversity Atlas contain maps with [...] Read more.
Soil is fundamental for the functioning of terrestrial ecosystems, but our knowledge about soil organisms and the habitat they provide (shortly: Soil biodiversity) is poorly developed. For instance, the European Atlas of Soil Biodiversity and the Global Soil Biodiversity Atlas contain maps with rather coarse information on soil biodiversity. This paper presents a methodology to map soil biodiversity with limited data and models. Two issues were addressed. First, the lack of consensus to quantify the soil biodiversity function and second, the limited data to represent large areas. For the later issue, we applied a digital soil mapping (DSM) approach at the scale of the Netherlands and Europe. Data of five groups of soil organisms (earthworms, enchytraeids, micro-arthropods, nematodes, and micro-organisms) in the Netherlands were linked to soil habitat predictors (chemical soil attributes) in a regression analysis. High-resolution maps with soil characteristics were then used together with a model for the soil biodiversity function with equal weights for each group of organisms. To predict soil biodiversity at the scale of Europe, data for soil biological (earthworms and bacteria) and chemical (pH, soil organic matter, and nutrient content) attributes were used in a soil biodiversity model. Differential weights were assigned to the soil attributes after consulting a group of scientists. The issue of reducing uncertainty in soil biodiversity modelling and mapping by the use of data from biological soil attributes is discussed. Considering the importance of soil biodiversity to support the delivery of ecosystem services, the ability to create maps illustrating an aggregate measure of soil biodiversity is a key to future environmental policymaking, optimizing land use, and land management decision support taking into account the loss and gains on soil biodiversity. Full article
(This article belongs to the Special Issue Digital Soil Mapping of Soil Functions)
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21 pages, 4038 KiB  
Article
Digital Mapping of Soil Classes Using Ensemble of Models in Isfahan Region, Iran
by Ruhollah Taghizadeh-Mehrjardi, Budiman Minasny, Norair Toomanian, Mojtaba Zeraatpisheh, Alireza Amirian-Chakan and John Triantafilis
Soil Syst. 2019, 3(2), 37; https://doi.org/10.3390/soilsystems3020037 - 28 May 2019
Cited by 35 | Viewed by 4783
Abstract
Digital soil maps can be used to depict the ability of soil to fulfill certain functions. Digital maps offer reliable information that can be used in spatial planning programs. Several broad types of data mining approaches through Digital Soil Mapping (DSM) have been [...] Read more.
Digital soil maps can be used to depict the ability of soil to fulfill certain functions. Digital maps offer reliable information that can be used in spatial planning programs. Several broad types of data mining approaches through Digital Soil Mapping (DSM) have been tested. The usual approach is to select a model that produces the best validation statistics. However, instead of choosing the best model, it is possible to combine all models realizing their strengths and weaknesses. We applied seven different techniques for the prediction of soil classes based on 194 sites located in Isfahan region. The mapping exercise aims to produce a soil class map that can be used for better understanding and management of soil resources. The models used in this study include Multinomial Logistic Regression (MnLR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Bayesian Networks (BN), and Sparse Multinomial Logistic Regression (SMnLR). Two ensemble models based on majority votes (Ensemble.1) and MnLR (Ensemble.2) were implemented for integrating the optimal aspects of the individual techniques. The overall accuracy (OA), Cohen's kappa coefficient index (κ) and the area under the curve (AUC) were calculated based on 10-fold-cross validation with 100 repeats at four soil taxonomic levels. The Ensemble.2 model was able to achieve larger OA, κ coefficient and AUC compared to the best performing individual model (i.e., RF). Results of the ensemble model showed a decreasing trend in OA from Order (0.90) to Subgroup (0.53). This was also the case for the κ statistic, which was the largest for the Order (0.66) and smallest for the Subgroup (0.43). Same decrease was observed for AUC from Order (0.81) to Subgroup (0.67). The improvement in κ was substantial (43 to 60%) at all soil taxonomic levels, except the Order level. We conclude that the application of the ensemble model using the MnLR was optimal, as it provided a highly accurate prediction for all soil taxonomic levels over and above the individual models. It also used information from all models, and thus this method can be recommended for improved soil class modelling. Soil maps created by this DSM approach showed soils that are prone to degradation and need to be carefully managed and conserved to avoid further land degradation. Full article
(This article belongs to the Special Issue Digital Soil Mapping of Soil Functions)
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17 pages, 1556 KiB  
Article
What is the Best Inference Trajectory for Mapping Soil Functions: An Example of Mapping Soil Available Water Capacity over Languedoc Roussillon (France)
by Quentin Styc and Philippe Lagacherie
Soil Syst. 2019, 3(2), 34; https://doi.org/10.3390/soilsystems3020034 - 7 May 2019
Cited by 16 | Viewed by 3416
Abstract
Extending digital soil mapping to the mapping of soil functions that can support end-user decisions comes to coupling a digital soil mapping procedure and a soil function assessment method. This can be done following various possible inference trajectories following the order with which [...] Read more.
Extending digital soil mapping to the mapping of soil functions that can support end-user decisions comes to coupling a digital soil mapping procedure and a soil function assessment method. This can be done following various possible inference trajectories following the order with which “combining primary soil properties”, “aggregating soil layers across depths” and “mapping” are executed to provide the targeted output. Eighteen inference trajectories, designed for computing soil available water capacity maps in the Languedoc–Roussillon region (France), were compared with regard to their mapping performances. The best performance (SSMSE = 0.42) was obtained by a trajectory that, before mapping, combined the three first GlobalSoilMap soil layers and computed the available water capacity of each layer. The worst (SSMSE = 0.07) was observed when all the soil layers and soil properties were combined prior to mapping. We explain the observed differences between trajectories by examining the differences in mapping errors and in error propagation between the compared trajectories, which involve both the correlations between the soil properties and between their mapping errors. This paves the way to spatial soil inference systems that could perform an ex ante selection of the best possible inference trajectory for mapping a soil function. Full article
(This article belongs to the Special Issue Digital Soil Mapping of Soil Functions)
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12 pages, 6161 KiB  
Article
Digital Mapping of Habitat for Plant Communities Based on Soil Functions: A Case Study in the Virgin Forest-Steppe of Russia
by Nikolai Lozbenev, Maria Smirnova, Maxim Bocharnikov and Daniil Kozlov
Soil Syst. 2019, 3(1), 19; https://doi.org/10.3390/soilsystems3010019 - 9 Mar 2019
Cited by 7 | Viewed by 3879
Abstract
The spatial structure of the habitat for plant communities based on soil functions in virgin forest-steppe of the Central Russian Upland is the focus of this study. The objectives include the identification of the leading factors of soil function variety and to determine [...] Read more.
The spatial structure of the habitat for plant communities based on soil functions in virgin forest-steppe of the Central Russian Upland is the focus of this study. The objectives include the identification of the leading factors of soil function variety and to determine the spatial heterogeneity of the soil function. A detailed topographic survey was carried out on a key site (35 hectares), 157 soil, and 34 geobotanical descriptions were made. The main factor of soil and plant cover differentiation is the redistribution of soil moisture along the microrelief. Redistributed runoff value was modelled in SIMWE and used as a tool for spatial prediction of soils due to their role in a habitat for plant communities’ functional context. The main methods of the study are the multidimensional scaling and discriminant analysis. We model the composition of plant communities (accuracy is 95%) and Reference Soil Group (accuracy is 88%) due to different soil moisture conditions. There are two stable soil habitat types: mesophytic communities on the Phaeozems (with additional water runoff more than 80 mm) and xerophytic communities on Chernozems (additional runoff less than 55 mm). A transitional type corresponded to xero- mesophytic communities on the Phaeozems with 55–80 mm additional redistributed runoff value. With acceptable accuracy, the habitat for natural plant communities based on soil function model predicts the position of contrastingly different components of biota in relation to their soil moisture requirements within the virgin forest-steppe of the Central Russian Upland. Full article
(This article belongs to the Special Issue Digital Soil Mapping of Soil Functions)
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27 pages, 8942 KiB  
Article
How and to What Extent Does Topography Control the Results of Soil Function Assessment: A Case Study From the Alps in South Tyrol (Italy)
by Fabian Ernst Gruber, Elisabeth Schaber, Jasmin Baruck and Clemens Geitner
Soil Syst. 2019, 3(1), 18; https://doi.org/10.3390/soilsystems3010018 - 5 Mar 2019
Cited by 2 | Viewed by 3527
Abstract
Soil function assessments (SFA) are becoming increasingly important as a tool to integrate soil-related issues in decision-making processes in order to maintain soil quality. We present the SEPP (Soil Evaluation for Planning Procedures) tool, which calculates a level of fulfillment for 14 soil [...] Read more.
Soil function assessments (SFA) are becoming increasingly important as a tool to integrate soil-related issues in decision-making processes in order to maintain soil quality. We present the SEPP (Soil Evaluation for Planning Procedures) tool, which calculates a level of fulfillment for 14 soil functions based on the information generally collected in soil pit descriptions. By using a statistical modeling approach based on support vector machine classification, we investigate how and to what extent topography, as representated by local terrain parameters and landform classes computed with the GRASS GIS tool r.geomorphon algorithm, controls soil parameters and hence the output of the SEPP tool. A feature selection procedure is applied which highlights those topographic attributes best suited for modeling the various soil function fulfillment levels. By evaluating the model for each soil function using cross-validation we show that the prediction accuracy varies from function to function. While some terrain attributes are directly implemented in the SFA algorithms of SEPP, others are implemented indirectly due to the link between topography and land use. Minimal curvature and slope were found to be first indicators of function fulfillment level for a number of soil functions. Full article
(This article belongs to the Special Issue Digital Soil Mapping of Soil Functions)
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