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Salinity Monitoring and Modelling at Different Scales

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A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Soil-Sediment-Water Systems".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 8288

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Special Issue Editors

Dr. Maria da Conceição Gonçalves

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Guest Editor

Instituto Nacional de Investigação Agrária e Veterinária, Avenida da República, Quinta do Marquês, 2780-157 Oeiras, Lisbon, Portugal
Interests: hydraulic properties; soil waterdynamics; soil salinity; pedotransfer functions

Dr. Mohammad Farzamian

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Guest Editor

Instituto Nacional de Investigação Agrária e Veterinária, Avenida da República, Quinta do Marquês, 2780-157 Oeiras, Lisbon, Portugal
Interests: proximal soil sensing; near-surface geophysics; electromagnetic induction; ground-penetrating radar; precision agriculture

Dr. Tiago Brito Ramos

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Guest Editor

Centro de Ciência e Tecnologia do Ambiente e do Mar (MARETEC-LARSyS), Instituto Superior Técnico, Universidade de Lisboa, 1, 1049-001 Lisboa, Portugal
Interests: modeling soil water dynamics and solute transport in the vadose zone
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Special Issue Information

Dear Colleagues,

Under the current changing climate and agricultural intensification, agricultural management practices need to adapt to changing conditions and to increasing water scarcity issues. Soil salinization, which already widely affects many regions of the world with arid and semiarid climates, becomes a top priority of research as it not only leads to the degradation of soil functions but also to yield losses, farmer's income, eventual migration of populations, and ultimately social unrest.

Strategies to better tackle soil salinization problems are thus critical for supporting soil management and agricultural production. These strategies should be based on efficient monitoring programs capable of continuously evaluating the performance of the implemented management strategies.

Numerical modeling, as well as remote sensing, are two widely used examples that have served as the basis for the development of monitoring tools aimed at supporting soil management and mitigating soil salinization problems. Numerical modeling, either through simple water balance models or more complex transient-state, Richards-based models, is fundamental for data integration and processes interpretation to improve agricultural practices and the protection of soil resources. Non-invasive and inexpensive proximal and remote sensing data can also be used to rapidly monitor, model, and predict the spatial and temporal variations of soil physical, chemical and hydrological properties at different scales.

In this Special Issue, we are soliciting research or manuscripts advancing on soil salinity measurement, modeling of soil salinization processes through the use of numerical tools at different scales, modeling and mapping using proximal soil sensing and remote sensing sensors, and other upscaling procedures used for soil salinity assessment and management. This Special Issue aims to bring together researchers from around the world on the advances in soil salinity measurement, mapping and modeling using various proximal and remote sensing sensors and vadose zone modeling to help connect researchers working in a similar area to tackle the globally critical issue and enhance soil security.

Dr. Maria da Conceição Gonçalves
Dr. Mohammad Farzamian
Dr. Tiago Brito Ramos
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. Land is an international peer-reviewed open access monthly 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 2600 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 salinity
soil hydraulic properties
pedotransfer functions
proximal soil sensing
remote sensing
electromagnetic induction
digital soil mapping
machine learning
arid and semi-arid climate
agricultural water management

Published Papers (7 papers)

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Research

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18 pages, 15446 KiB

Open AccessArticle

An Ecological Overview of Halophytes and Salt-Affected Soils at El Hito Saline Pond (Central Spain): Baseline Study for Future Conservation–Rehabilitation Measures

by Raimundo Jiménez-Ballesta, Santos Cirujano-Bracamonte, Eduardo Palencia-Mayordomo and Mario Álvarez-Soto

Land 2024, 13(4), 449; https://0-doi-org.brum.beds.ac.uk/10.3390/land13040449 - 31 Mar 2024

Viewed by 598

Abstract

In an attempt to boost the potential ecological viability of wetlands, this study aimed to discover the relationship between soil salinity and vegetation composition in a quasi-pristine saline pond, “El Hito Lagoon”. This wetland is situated in the largest continuous natural semi-arid steppe land of western Europe (specifically in Castilla La Mancha, Central Spain). Several soil profiles and a series of surface samples (0–10 cm) extracted from a systematic network throughout the saline pond were described, sampled, and analyzed. The most significant results included the detection of elevated levels of soil salinity, with distinctive sub-areas of extreme elevated surface salinity where the pH reading peaked at 9.89 and the electrical conductivity was higher than 40 (dS/m). The very high content of total available P displayed quite an irregular scatter within the soil profile. Specifically, the range oscillated between 8.57 mg/kg and 388.1 mg/kg, several samples having values greater than 100 mg/kg. An aspect that the abundant presence of Salsola soda, a plant frequently found growing in nutrient-rich wetlands, was able to confirm. Full article

(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales)

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17 pages, 9861 KiB

Open AccessArticle

Comparison of Electromagnetic Induction and Electrical Resistivity Tomography in Assessing Soil Salinity: Insights from Four Plots with Distinct Soil Salinity Levels

by Maria Catarina Paz, Nádia Luísa Castanheira, Ana Marta Paz, Maria Conceição Gonçalves, Fernando Monteiro Santos and Mohammad Farzamian

Land 2024, 13(3), 295; https://0-doi-org.brum.beds.ac.uk/10.3390/land13030295 - 27 Feb 2024

Viewed by 923

Abstract

Electromagnetic induction (EMI) and electrical resistivity tomography (ERT) are geophysical techniques measuring soil electrical conductivity and providing insights into properties correlated with it to depths of several meters. EMI measures the apparent electrical conductivity (EC_a, dS m⁻¹) without physical contact, while ERT acquires apparent electrical resistivity (ER_a, ohm m) using electrodes. Both involve mathematical inversion to obtain models of spatial distribution for soil electrical conductivity (σ, mS m⁻¹) and electrical resistivity (ρ, ohm m), respectively, where ρ is the reciprocal of σ. Soil salinity can be assessed from σ over large areas using a calibration process consisting of a regression between σ and the electrical conductivity of the saturated soil paste extract (EC_e, dS m⁻¹), used as a proxy for soil salinity. This research aims to compare the prediction abilities of the faster EMI to the more reliable ERT for estimating σ and predicting soil salinity. The study conducted surveys and sampling at four locations with distinct salinity levels in Portugal, analysing the agreement between the techniques, and obtained 2D vertical soil salinity maps. In our case study, the agreement between EMI and ERT models was fairly good in three locations, with σ varying between 50 and 500 mS m⁻¹. However, this was not the case at location 4, where σ exceeded 1000 mS m⁻¹ and EMI significantly underestimated σ when compared to ERT. As for soil salinity prediction, both techniques generally provided satisfactory and comparable regional-level predictions of EC_e, and the observed underestimation in EMI models did not significantly affect the overall estimation of soil salinity. Consequently, EMI demonstrated an acceptable level of accuracy in comparison to ERT in our case studies, supporting confidence in utilizing this faster and more practical technique for measuring soil salinity over large areas. Full article

(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales)

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17 pages, 2951 KiB

Open AccessArticle

Comparing Two Saline-Gypseous Wetland Soils in NE Spain

by Juan Herrero and Carmen Castañeda

Land 2023, 12(11), 1990; https://0-doi-org.brum.beds.ac.uk/10.3390/land12111990 - 30 Oct 2023

Cited by 1 | Viewed by 669

Abstract

Small (<1 km²) saline wetlands scattered across the landscape often go unnoticed or are threatened by urbanization or other interventions, despite their role as biodiversity shelters. This study is needed to show methods for monitoring this specific kind of wetland, and to guide the selection of analytical techniques. We provide data and comparisons for salient soil traits of two quasi-pristine gypsiferous and saline wetlands named Farrachuela (FA) and Agustín (AG). The soil characteristics presented in this article are a more sensitive indicator of their ecological status than some of the most used indicators, such as birds and plants. We found significant differences between the two saladas in percent water saturation, equivalent calcium carbonate, gypsum content, and soil salinity expressed as electrical conductivity both of 1:5 soil-to-water ratio and of saturation extracts. The differences were also significant in the concentrations of Mg²⁺, Na⁺, and Cl⁻, while they were non-significant for Ca²⁺, HCO₃²⁻, and SO₄²⁻. The mean contents of the six ions were lower in FA than in AG. Both pH and sodium adsorption ratios were significantly different between the two wetlands. The data are mainly examined and plotted by displaying their non-parametric statistics, a synoptic approach that will allow us to monitor the evolution of the wetlands against both traditional agricultural pressures and emerging green energy infrastructures. Last but not least, we discuss the shortcomings of some standard laboratory methods when applied to gypsum-rich soils. Full article

(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales)

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29 pages, 6891 KiB

Open AccessFeature PaperArticle

Optimization of Irrigation of Wine Grapes with Brackish Water for Managing Soil Salinization

by Vinod Phogat, Tim Pitt, Paul Petrie, Jirka Šimůnek and Michael Cutting

Land 2023, 12(10), 1947; https://0-doi-org.brum.beds.ac.uk/10.3390/land12101947 - 20 Oct 2023

Cited by 1 | Viewed by 999

Abstract

Water scarcity and quality are critical impediments to sustainable crop production. In this study, HYDRUS-2D was calibrated using field measurements of water contents and salinities in the soil under wine grapes irrigated with river water (Rw, 0.32 dS/m). The calibrated model was then used to evaluate the impact of (a) four different water qualities ranging from 0.32 (Rw) to 3.2 dS/m (brackish water, Gw) including blended (Mix) and monthly alternating (Alt) irrigation modes; (b) two rainfall conditions (normal and 20% below normal); and (c) two leaching options (with and without 30 mm spring leaching irrigation) during the 2017–2022 growing seasons. Irrigation water quality greatly impacted root water uptake (RWU) by wine grapes and other water balance components. Irrigation with brackish water reduced average RWU by 18.7% compared to river water. Irrigation with blended water or from alternating water sources reduced RWU by 8.8 and 7%, respectively. Relatively small (2.8–8.2%) average annual drainage (Dr) in different scenarios produced a very low (0.05–0.16) leaching fraction. Modeling scenarios showed a tremendous impact of water quality on the salts build-up in the soil. The average electrical conductivity of the saturated soil extract (EC_e) increased three times with Gw irrigation compared to Rw (current practices). Blended and alternate irrigation scenarios showed a 21 and 28% reduction in EC_e, respectively, compared to Gw. Irrigation water quality substantially impacted site-specific actual basal (K_{cb act}) and single (K_{c act}) crop coefficients of grapevine. Threshold leaching efficiency estimated in terms of the salt mass leached vs. added (LE_s; kg/kg) for salinity control (LE_s > 1) was achieved with LFs of 0.07, 0.12, 0.12, and 0.15 for the Rw, Mix, Alt, and Gw irrigations, respectively. Applying annual leaching irrigation (30 mm) before bud burst (spring) in the Mix and Alt with Rw and Gw scenarios was found to be the best strategy for managing irrigation-induced salinity in the root zone, lowering the EC_e to levels comparable to irrigation with Rw. Modeling scenarios suggested that judicious use of water resources and continuous root zone monitoring could be key for salinity management under adverse climate and low water allocation conditions. Full article

(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales)

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22 pages, 12975 KiB

Open AccessArticle

Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

by Magboul M. Sulieman, Fuat Kaya, Mohammed A. Elsheikh, Levent Başayiğit and Rosa Francaviglia

Land 2023, 12(9), 1680; https://0-doi-org.brum.beds.ac.uk/10.3390/land12091680 - 28 Aug 2023

Cited by 2 | Viewed by 1330

Abstract

A comprehensive understanding of soil salinity distribution in arid regions is essential for making informed decisions regarding agricultural suitability, water resource management, and land use planning. A methodology was developed to identify soil salinity in Sudan by utilizing optical and radar-based satellite data as well as variables obtained from digital elevation models that are known to indicate variations in soil salinity. The methodology includes the transfer of models to areas where similar conditions prevail. A geographically coordinated database was established, incorporating a variety of environmental variables based on Google Earth Engine (GEE) and Electrical Conductivity (EC) measurements from the saturation extract of soil samples collected at three different depths (0–30, 30–60, and 60–90 cm). Thereafter, Multinomial Logistic Regression (MNLR) and Gradient Boosting Algorithm (GBM), were utilized to spatially classify the salinity levels in the region. To determine the applicability of the model trained at the reference site to the target area, a Multivariate Environmental Similarity Surface (MESS) analysis was conducted. The producer’s accuracy, user’s accuracy, and Tau index parameters were used to evaluate the model’s accuracy, and spatial confusion indices were computed to assess uncertainty. At different soil depths, Tau index values for the reference area ranged from 0.38 to 0.77, whereas values for target area samples ranged from 0.66 to 0.88, decreasing as the depth increased. Clay normalized ratio (CLNR), Salinity Index 1, and SAR data were important variables in the modeling. It was found that the subsoils in the middle and northwest regions of both the reference and target areas had a higher salinity level compared to the topsoil. This study highlighted the effectiveness of model transfer as a means of identifying and evaluating the management of regions facing significant salinity-related challenges. This approach can be instrumental in identifying alternative areas suitable for agricultural activities at a regional level. Full article

(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales)

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23 pages, 5491 KiB

Open AccessArticle

Indirect Prediction of Salt Affected Soil Indicator Properties through Habitat Types of a Natural Saline Grassland Using Unmanned Aerial Vehicle Imagery

by László Pásztor, Katalin Takács, János Mészáros, Gábor Szatmári, Mátyás Árvai, Tibor Tóth, Gyöngyi Barna, Sándor Koós, Zsófia Adrienn Kovács, Péter László and Kitti Balog

Land 2023, 12(8), 1516; https://0-doi-org.brum.beds.ac.uk/10.3390/land12081516 - 30 Jul 2023

Cited by 2 | Viewed by 1053

Abstract

Salt meadows, protected within National Parks, cannot be directly surveyed, yet understanding their soil condition is crucial. Our study indirectly estimates soil parameters (Total Salt Content (TSC), Na, and pH) related to salinization/sodification/alkalinization using spectral indices and UAV survey-derived elevation model, focusing on continental lowland salt meadows. A vegetation map was created using 16 spectral indices and a Digital Elevation Model calculated from RGB orthophotos using photogrammetry. Field observations helped define habitat types based on the General National Habitat Classification System (Hungary), and quadrats with complete coverage of specific plant species were identified. Machine learning was employed on 84 training quadrats to develop a prediction algorithm for vegetation patterns. Five saline habitat types, representing variations in soil properties and topography, were identified. Spectral and topomorphometric indices derived from UAV were key to the spatial prediction of soil properties, employing random forest and co-kriging methods. TSC, Na, and pH data served as indicators of salt-affected soils (SAS), and thematic maps were generated for each indicator (57 samples). Overlapping with the vegetation map, the probability range of estimated SAS indicator values was determined. Consequently, a model-based estimation of soil pH, TSC, and Na conditions is provided for habitat types without disturbing protected areas. Full article

(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales)

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Review

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27 pages, 1205 KiB

Open AccessFeature PaperReview

Assessment and Mapping of Soil Salinity Using the EM38 and EM38MK2 Sensors: A Focus on the Modeling Approaches

by Panagiota Antonia Petsetidi and George Kargas

Land 2023, 12(10), 1932; https://0-doi-org.brum.beds.ac.uk/10.3390/land12101932 - 17 Oct 2023

Cited by 1 | Viewed by 1583

Abstract

Soil salinization and its detrimental agricultural, environmental, and socioeconomic impact over extended regions represent a major global concern that needs to be addressed. The sustainability of agricultural lands and the development of proper mitigation strategies require effective monitoring and mapping of the saline areas of the world. Therefore, robust modeling techniques and efficient sensors that assess and monitor the spatial and temporal variations in soil salinity within an area, promptly and accurately, are essential. The aim of this paper is to provide a comprehensive and up-to-date review of the modeling approaches for the assessment and mapping of saline soils using data collected by the EM38 and EM38MK2 (MK2) sensors at different scales. By examining the current and latest approaches and highlighting the most noteworthy considerations related to their accuracy and reliability, the intention of this review is to elucidate and underline the role of the EM38 and the MK2 type in the recent needs of detecting and interpreting soil salinity. Another aim is to assist researchers and users in selecting the optimal approach for future surveys and making well-informed decisions for the implementation of precise management practices. The study’s findings revealed that the integration of the EM38 and MK2 sensors with remote sensing data and advanced methods like machine learning and inversion is a promising approach to the accurate prediction and mapping of the spatiotemporal variations in soil salinity. Therefore, future research focused on validating and expanding such sophisticated modeling applications to regional and global scales should be increased. Full article

(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales)

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