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Sustainable Environmental Analysis of Soil and Water
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Sustainable Environmental Analysis of Soil and Water

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

Deadline for manuscript submissions: 1 November 2024 | Viewed by 3740

Special Issue Editors

Dr. Solmaz Fathololoumi

E-Mail Website
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

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

Published Papers (3 papers)

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Research

21 pages, 3829 KiB  
Article
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 1 | Viewed by 1253
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 [...] Read more.
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  
Article
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 824
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 [...] Read more.
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  
Article
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 5 | Viewed by 1213
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 [...] Read more.
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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