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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Soil Conservation and Sustainability".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 12234

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Special Issue Editors

Dr. Solmaz Fathololoumi

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Guest Editor

School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: soil physics; digital soil mapping; soil moisture; remote sensing

Prof. Dr. Asim Biswas

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Guest Editor

School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: soil physics; vadose zone hydrology; precision agriculture; proximal soil sensing; digital soil mapping; soil spatial variability
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Special Issue Information

Dear Colleagues,

The increase in human activities in the world is a serious threat to maintaining the health and stability of various components of the environment, including soil and water. Soil and water are two crucial materials for the continuation of life on Earth, so it is necessary to focus effort and attention on preserving these valuable resources. To protect soil and water and support sustainable development, soil/water maps that provide the larger community of soil and water users access to the related knowledge and data flows they need are essential. In recent years, thanks to the huge advances in different soil and water data-collection methods—including the development of satellite and proximal sensors, and data-analysis methods based on artificial intelligence algorithms, machine learning and deep learning—the field of understanding the different dimensions of soil and water and providing a solution to maintain their stability has been developed. Accurately mapping the spatial and temporal distribution of the static and dynamic parameters of soil and water at different scales based on new technologies is of great importance in maintaining the sustainability of the environment, agriculture, food security, climate, natural resources, etc., which can improve the quality of life of all living beings.

The goal of this Special Issue is to identify ways to better manage and use natural resources (soil and water). To this end, our objective is to determine how we can gather various information to generate precise and accurate data of soil and water without damaging them, in order to achieve sustainability.

This Special Issue invites research and review articles, including new and creative ideas in the fields of:

Smart soil and water data management.
Conservation of soil and water resources.
Sustainability of agricultural and urban soils.
Digital soil mapping.
Remote sensing in soil and water studies.
Spatial and temporal changes of soil and water properties.
Soil and water erosion.
Soil moisture retrieval.
Modeling and management of soil and water pollution.
Land use/cover mapping.

Dr. Solmaz Fathololoumi
Prof. Dr. Asim Biswas
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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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 water
sustainability agricultural
erosion environment
remote sensing

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Published Papers (8 papers)

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Research

16 pages, 6011 KiB

Open AccessArticle

Sedimentation Pattern as a Response to Hydrodynamics in a Near-Symmetric River Confluence

by João Nuno Fernandes and Leila Alizadeh

Sustainability 2025, 17(9), 3790; https://doi.org/10.3390/su17093790 - 23 Apr 2025

Viewed by 348

Abstract

River confluences are dynamic zones where hydrodynamic interactions between tributary flows—varying in velocity, direction, and sediment concentration—can significantly alter hydro morphology. These changes feature substantial consequences for the stability of riverbanks, nearby hydraulic structures, and the surrounding environment. This paper investigates flow mechanisms and sediment dynamics in a symmetric 50° confluence through laboratory experiments on a scaled physical model of a real confluence located on Madeira Island, Portugal. Acoustic Doppler velocity measurements were used to analyze the hydrodynamic characteristics, while bathymetry was surveyed using an RGB sensor and the Structure from Motion technique. Sedimentation patterns were correlated with key flow zones within the confluence. This study highlights how variations in discharge and momentum ratios influence sediment distribution and morphology, potentially destabilizing riverbanks and contributing to sediment deposition and erosion patterns. Understanding these mechanisms is critical for improving the sustainable management of water resources and minimizing anthropogenic impacts on fluvial systems. The findings provide valuable insights for enhancing river resilience, protecting natural watercourses, and supporting sustainable development by promoting informed planning of hydraulic structures and sediment management strategies. Full article

(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)

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27 pages, 11781 KiB

Open AccessArticle

Exploring the Interaction Between Landslides and Carbon Stocks in Italy

by Jibran Qadri and Francesca Ceccato

Sustainability 2024, 16(24), 11273; https://doi.org/10.3390/su162411273 - 23 Dec 2024

Cited by 1 | Viewed by 967

Abstract

Landslides, as natural hazards, have far-reaching impacts beyond their immediate effects on human lives and infrastructure; landslides disrupt both carbon storage and ecosystem stability, and their role in the global carbon cycle cannot be underestimated. This study delves into the complex relationship between landslides and carbon stocks such as, in particular, soil organic carbon (SOC) and above-ground biomass (AGB), and outlines the spatial relationship between different types of landslides, soil organic carbon (SOC), and the carbon cycle, underscoring the importance of understanding these interconnections for environmental sustainability and climate change mitigation efforts. By employing machine learning algorithms on the Google Earth Engine platform, landslide susceptibility maps were created for different landslide types across Italy, and their spatial patterns with SOC accumulation were analyzed using the Python environment. The findings reveal a nuanced relationship between landslide hazard levels and SOC dynamics, with varying trends observed for different landslide types. In addition, this study investigates the potential impact of large-scale landslide events on carbon sequestration in the short term via a case study of the May 2023 landslide event in the Emilia Romagna region of Italy. The analysis reveals a substantial reduction in above-ground biomass by 35%, which approximately accounts for the loss of 0.133 MtC, and a decrease in SOC accumulation in 72% of the affected areas, indicating that landslides can transform carbon sinks into carbon sources, at least in the short term, and suggested that carbon released from extreme landslide events at a larger scale needs to be accounted for in regional or national carbon emissions. This research underscores the importance of considering landslides in carbon cycle assessments and emphasizes the need for sustainable land management strategies to protect and enhance carbon sinks, such as forests and healthy soils, in the face of increasing natural hazards and climate change impacts. Full article

(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)

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21 pages, 4044 KiB

Open AccessArticle

The Effect of Soil Tillage Systems on the Soil Microbial and Enzymatic Properties Under Soybean (Glycine max L. Merrill) Cultivation—Implications for Sustainable Soil Management

by Jacek Długosz, Bożena Dębska and Anna Piotrowska-Długosz

Sustainability 2024, 16(24), 11140; https://doi.org/10.3390/su162411140 - 19 Dec 2024

Cited by 3 | Viewed by 1065

Abstract

Reducing soil tillage with the application of catch-crop green mass as a mulch is a conservation practice that is used in agriculture to improve soil ecosystem functioning. Such a cultivation method enhances soil organic matter quantity and quality through the improvement of soil biological activity and nutrient availability, while reducing soil disturbance. Therefore, a three-year field experiment was conducted in the years 2017–2019 to evaluate the effect of three tillage methods (TMs) (conventional, CT; reduced, RT; and strip tillage, ST) on soil microbial and specific enzyme properties (microbial C and N content, the activity of dehydrogenases—DHA, the rate of fluorescein sodium salt hydrolysis—FDAH, CMC-cellulase—Cel and β-glucosidase—Glu) and certain basic soil properties. The study was performed in a field; it was a one-factor experiment that was carried out in a randomized block design. The soil samples were collected from the upper soil layer five times a year: in April (before the sowing of soybean), May, June, August and September (before soybean harvesting). The tillage methods or sampling dates used had no significant effect on the organic carbon and total nitrogen levels. Most of the C-related properties (the content of microbial biomass and the C-cycling enzymatic activity such as Cel and Glu) and microbial activity bioindicators (DHA activity, FDAH rate) revealed significant seasonal changes, whereby each variable was affected in a different order (e.g., the Cel activity was significantly higher in April and September than in other months—22%, while the DHA activity was significantly higher in June and August compared to other months—18%). RT significantly increased the enzymatic activity as compared to CT and ST, and the difference was between 8 and 33% (with a mean of 18%). The exception was the β-glucosidase activity as determined in 2019, which was significantly higher in the case of CT (1.02 mg pNP kg⁻¹ h⁻¹) and ST than in RT (0.705 mg pNP kg⁻¹ h⁻¹). However, the explanation for such phenomenon could not possibly be based on the available data. Our results suggested that the response of the enzyme activities toward the same factor may be due to the inherent variability in enzyme response associated with the spatial variability in soil properties as well as the properties of the enzyme itself and changes in the periodic occurrence of its substrates in the soil. Generally, the reduced tillage combined with plant residues return could be recommended for enhancing soil health and quality by improving its microbial and enzymatic features. The findings above suggest that a reduced tillage system is an important component of soil management in sustainable agriculture. Full article

(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)

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15 pages, 6502 KiB

Open AccessArticle

Investigation and Simulation Study on the Impact of Vegetation Cover Evolution on Watershed Soil Erosion

by Dandan Shen, Yuangang Guo, Bo Qu, Sisi Cao, Yaer Wu, Yu Bai, Yiting Shao and Jinglin Qian

Sustainability 2024, 16(22), 9633; https://doi.org/10.3390/su16229633 - 5 Nov 2024

Cited by 1 | Viewed by 1264

Abstract

Soil erosion has always been a critical issue confronting watershed environments, impacting the progress of sustainable development. As an increasing number of countries turn their attention to this problem, numerous policies have been enacted to halt the progression of soil erosion. However, policy-driven interventions often lead to significant changes in watershed vegetation coverage, under which circumstances, the original sediment erosion models may fall short in terms of simulation accuracy. Taking the Kuye River watershed as the research subject, this study investigates soil erosion data spanning from 1981 to 2015 and utilizes the Revised Universal Soil Loss Equation (RUSLE) model to simulate soil erosion. It is found that the extensive planting of vegetation after 2000 has led to a rapid reduction in soil erosion within the Kuye River watershed. The original vegetation cover and management factor (C) proves inadequate in predicting the abrupt changes in vegetation coverage. Consequently, this study adopts two improved plant cover and management factor equations. We propose two new methods for calculating the vegetation cover and management factor, one using machine learning techniques and the other employing a segmented calculation approach. The machine learning approach utilizes the Eureqa software (version11.0, Cornell University, New York, American) to search for the relationship between Normalized Difference Vegetation Index (NDVI) and C, ultimately establishing an equation that describes this relationship. On the other hand, the piecewise method determines critical values based on data trends and provides separate formulas for C above and below these critical values. Both methods have achieved superior calculation accuracy. Specifically, the overall data calculation using the machine learning method achieved an determined coefficient (R²) of 0.5959, while the segmented calculation method achieved an R² of 0.6649. Compared to the R² calculated by the traditional RULSE method, these two new methods can more accurately predict soil erosion. The findings of this study can provide valuable theoretical reference for water and soil prediction in watersheds. Full article

(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)

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21 pages, 12105 KiB

Open AccessArticle

Assessment of Total Mercury Levels Emitted from ASGM into Soil and Groundwater in Chami Town, Mauritania

by Mohamed Mamoune Maha, Akito Matsuyama, Takahiko Arima and Atsushi Sainoki

Sustainability 2024, 16(18), 7992; https://doi.org/10.3390/su16187992 - 12 Sep 2024

Cited by 2 | Viewed by 1300

Abstract

Artisanal and small-scale gold mining (ASGM) is a serious growing concern in Sub-Saharan Africa. In Mauritania, recent gold discoveries in the north and northwest have led to an increase in ASGM centers, reflecting trends across the region and posing considerable risks of mercury (Hg) contamination. Notwithstanding this fact, the extent of mercury contamination in the region remains unclear due to insufficient knowledge on the mechanisms of Hg dispersion in hyper-arid regions. In light of this, the present study aimed to acquire fundamental knowledge to elucidate the dispersion mechanism of mercury through conducting soil and groundwater sampling in and around Chami town, Mauritania, where ASGM activities have intensified. We analyzed 180 soil samples and 5 groundwater samples for total mercury (total Hg) using cold vapor atomic absorption spectrometry (CVAAS) and atomic fluorescence spectrometry (AFS) methods. The total Hg levels in soil samples ranged from 0.002 to 9.3 ppm, with the highest concentrations found at ASGM sites. Groundwater samples exhibited low total Hg levels (0.25–1.25 ng/L). The total Hg content in soil and groundwater samples was below Japanese standards, yet soil samples from hotspot points exceeded other international standards. Our study emphasizes the Hg dispersion patterns around Chami town, suggesting a gradual decrease in total Hg with increasing distance from ASGM sites and a potential influence of wind dynamics. The knowledge accumulated in this study provides essential insights into the Hg dispersion mechanisms in Chami town, laying the foundation for establishing a predictive model of Hg contamination in hyper-arid regions. Full article

(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)

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21 pages, 3829 KiB

Open AccessArticle

A Scenario-Based Multi-Criteria Decision-Making Approach for Allocation of Pistachio Processing Facilities: A Case Study of Zarand, Iran

by Mohammad Ebrahimi Sirizi, Esmaeil Taghavi Zirvani, Abdulsalam Esmailzadeh, Jafar Khosravian, Reyhaneh Ahmadi, Naeim Mijani, Reyhaneh Soltannia and Jamal Jokar Arsanjani

Sustainability 2023, 15(20), 15054; https://doi.org/10.3390/su152015054 - 19 Oct 2023

Cited by 8 | Viewed by 2440

Abstract

Site selection and allocation of manufacturing and processing facilities are essential to sustainable economic productivity of a given product while preserving soil, the environment, and biodiversity. An essential criterion when evaluating various approaches to model land suitability for pistachio processing facilities is their adaptability to accommodate diverse perspectives and circumstances of managers and decision makers. Incorporating the concept of risk into the decision-making process stands as a significant research gap in modeling land suitability for pistachio processing facilities. This study presents a scenario-based multi-criteria decision-making system for modeling the land suitability of pistachio processing facilities. The model was implemented based on a stakeholder analysis as well as inclusion of a set of influential criteria and restrictions for an Iranian case study, which is among the top three producers. The weight of each criterion was determined based on the best-worst method (BWM) after the stakeholder analysis. Then, the ordered weighted averaging (OWA) model was used to prepare maps of spatial potential for building a pistachio processing factory in different decision-making scenarios, including very pessimistic, pessimistic, intermediate, optimistic, and very optimistic attitudes. Finally, the sensitivity analysis of very-high- and high-potential regions to changes in the weight of the effective criteria was evaluated and proved that the most important criteria were proximity to pistachio orchards, proximity to residential areas, proximity to the road network, and proximity to industrial areas. Overall, 327 km² of the study area was classified as restricted, meaning that they are not suitable locations for pistachio processing. The average estimated potential values based on the proposed model for very pessimistic, pessimistic, intermediate, optimistic, and very optimistic scenarios were 0.19, 0.47, 0.63, 0.78, and 0.97, respectively. The very-high-potential class covered 0, 0.41, 8.25, 39.64, and 99.78 percent of the study area based on these scenarios, respectively. The area of suitable regions for investment decreased by increasing risk aversion in decision making. The model was more sensitive to changes in the weights of proximity to residential areas, proximity to pistachio orchards, and proximity to transportation hubs. The proposed approach and the achieved findings could be of broader use to respective stakeholders and investors. Given the suitability of arid regions for planting pistachio and its relatively high profitability, the local authorities and decision makers can promote further expansion of the orchards, which can lead to better welfare of farmers and reducing rural-urban migration in the region. Full article

(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)

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18 pages, 5345 KiB

Open AccessArticle

Improving Runoff Prediction Accuracy in a Mountainous Watershed Using a Remote Sensing-Based Approach

by Solmaz Fathololoumi, Ali Reza Vaezi, Seyed Kazem Alavipanah, Ardavan Ghorbani, Mohammad Karimi Firozjaei and Asim Biswas

Sustainability 2023, 15(17), 12754; https://doi.org/10.3390/su151712754 - 23 Aug 2023

Cited by 2 | Viewed by 1565

Abstract

Due to the limited number and sparse distribution of meteorological and hydrometric stations in most watersheds, the runoff estimation based on these stations may not be accurate. However, the accurate determination of the Antecedent Soil Moisture (ASM) in watersheds can improve the accuracy of runoff forecasting. The objective of this study is to utilize the ASM derived from satellite imagery to enhance the accuracy of runoff estimation in a mountainous watershed. In this study, a range of Remote Sensing (RS) data, including surface biophysical and topographic features, climate data, hydrometric station flow data, and a ground-based measured SM database for the Balikhli-Chay watershed in Iran, were utilized. The Soil Conservation Service Curve Number (SCS-CN) method was employed to estimate runoff. Two approaches were used for estimating the ASM: (1) using the precipitation data recorded in ground stations, and (2) using the SM data obtained from satellite images. The accuracy of runoff estimation was then calculated for these two scenarios and compared. The mean Nash–Sutcliffe statistic was found to be 0.63 in the first scenario and 0.74 in the second scenario. The inclusion of ASM derived from the satellite imagery in the precipitation–runoff model resulted in a 51% increase in the accuracy of runoff estimation compared to using precipitation data recorded in ground stations. These findings have significant implications for improving the accuracy of ASM and runoff modeling in various applications. Full article

(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)

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20 pages, 6887 KiB

Open AccessArticle

A Generalized Regression Neural Network Model for Accuracy Improvement of Global Precipitation Products: A Climate Zone-Based Local Optimization

by Saeid Mohammadpouri, Mostafa Sadeghnejad, Hamid Rezaei, Ronak Ghanbari, Safiyeh Tayebi, Neda Mohammadzadeh, Naeim Mijani, Ahmad Raeisi, Solmaz Fathololoumi and Asim Biswas

Sustainability 2023, 15(11), 8740; https://doi.org/10.3390/su15118740 - 29 May 2023

Cited by 8 | Viewed by 2063

Abstract

The ability to obtain accurate precipitation data from various geographic locations is crucial for many applications. Various global products have been released in recent decades for estimating precipitation spatially and temporally. Nevertheless, it is extremely important to provide reliable and accurate products for estimating precipitation in a variety of environments. This is due to the complexity of topographic, climatic, and other factors. This study proposes a multi-product information combination for improving precipitation data accuracy based on a generalized regression neural network model using global and local optimization strategies. Firstly, the accuracy of ten global precipitation products from four different categories (satellite-based, gauge-corrected satellites, gauge-based, and reanalysis) was assessed using monthly precipitation data collected from 1896 gauge stations in Iran during 2003–2021. Secondly, to enhance the accuracy of the modeled precipitation products, the importance score of effective and auxiliary variables—such as elevation, the Enhanced Vegetation Index (EVI), the Land Surface Temperature (LST), the Soil Water Index (SWI), and interpolated precipitation maps—was assessed. Finally, a generalized regression neural network (GRNN) model with global and local optimization strategies was used to combine precipitation information from several products and auxiliary characteristics to produce precipitation data with high accuracy. Global precipitation products scored higher than interpolated precipitation products and surface characteristics. Furthermore, the importance score of the interpolated precipitation products was considerably higher than that of the surface characteristics. SWI, elevation, EVI, and LST scored 53%, 20%, 15%, and 12%, respectively, in terms of importance. The lowest RMSE values were associated with IMERGFinal, TRMM3B43, PERSIANN-CDR, ERA5, and GSMaP-Gauge. For precipitation estimation, these products had Kling–Gupta efficiency (KGE) values of 0.89, 0.86, 0.77, 0.78, and 0.60, respectively. The proposed GRNN-based precipitation product with a global (local) strategy showed RMSE and KGE values of 9.6 (8.5 mm/mo) and 0.92 (0.94), respectively, indicating higher accuracy. Generally, the accuracy of global precipitation products varies depending on climatic conditions. It was found that the proposed GRNN-derived precipitation product is more efficient under different climatic conditions than global precipitation products. Moreover, the local optimization strategy based on climatic classes outperformed the global optimization strategy. Full article

(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)

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