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Land
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Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in...
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Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in Agriculture

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 8998

Special Issue Editors

Dr. Anastasia Angelaki

E-Mail Website
Guest Editor
Laboratory of Agricultural Hydraulics, Department of Agriculture, Crop Production and Rural Environment, School of Agricultural Sciences, University of Thessaly, Fytokou St., N. Ionia Magnisias, 38446 Volos, Greece
Interests: soil–water–plant relations; soil hydraulic properties; precise irrigation scheduling; soil–water infiltration; solute transport in porous media; simulation and prediction models; neural networks; irrigation water quality; water saving; rational and sus-tainable irrigation water management.
Special Issues, Collections and Topics in MDPI journals
Dr. Parveen Sihag

E-Mail Website
Guest Editor
Department of Civil Engineering, Chandigarh University, Mohali 140413, India
Interests: water resources; climate change; infiltration; vortex tube; silt ejectors; streamflow; road accident analysis; mechanical properties of concrete-based materials; artificial neural network; fuzzy logic; adaptive neurofuzzy inference system; random forest; M5P; random tree; bagging; stochastic; support vector machine; Gaussian process; regression; generalized neural network; multivariate adaptive regression splines; group method of data handling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water shortage is a serious worldwide issue, and the demand–supply balance for water is approaching a tipping point in the majority of regions, particularly where natural resources are limited. As water shortages intensify to critical levels in most regions, the demand outpaces the supply. We still do not completely understand the natural water–soil system; therefore, adopting an eco-friendly strategy to utilize the irrigation water is most crucial, ensuring that the maximum water efficiency and crop yield are attained. Over the past few decades, in anticipation of the incoming climate change, the management of water has become a high-priority area. Timely irrigation water management, soil pollution monitoring, and physical and chemical soil status examination are fundamental for the future. Future generations require sustainable soil–water management, and other aspects such as the rising climatic crisis, soil and water pollution, soil fertility loss, etc., must be considered. As artificial intelligence and machine learning are increasingly part of everyday life, we expect their further integration into agriculture. Thus, research associated with efficient water management in agriculture, intelligent irrigation planning, soil health, efficient land use planning, improved yield through intelligent approaches, water quality, physical, and chemical and hydraulic properties of soils in agriculture may provide key solutions for the future.

The main goal of this Special Issue is to collect papers (original research articles and review papers) that provide insights into the following areas: water saving in agriculture; efficient land use planning; agricultural hydraulics; smart irrigation planning; improved yield using intelligent techniques; water quality; the physical, chemical, and hydraulic properties of agricultural soils; and the application of machine learning and artificial intelligence techniques in the soil–water complex.

This Special Issue will welcome manuscripts that link the following themes:

  • Agricultural hydraulics; hydraulic parameters of agricultural soils; comparison with urban soils; water movement into the vadose zone; water movement in saturated and unsaturated soils; canals; flow control; and drainage systems.
  • Irrigation (surface irrigation, sprinkler irrigation, drip irrigation, individual and collective irrigation systems); smart irrigation systems; and automation and AI in irrigation.
  • Water saving in agriculture using smart irrigation scheduling, mulching, precision agriculture, and smart sensors.
  • Effects of oil pollution on soil structure, water holding capacity, leaching, and runoff; the impact of soil remediation on agricultural water use and sustainable irrigation.
  • Drift analysis, remote sensing, real-time decision making, predictive models for drift, and water saving through AI and drift control.
  • Land uses, land cover, soil degradation and improvement, soil health, efficient land use planning, crop rotation, and intercropping for reducing stress on water resources.

We look forward to receiving your original research articles and reviews.

Dr. Anastasia Angelaki
Dr. Parveen Sihag
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 250 words) can be sent to the Editorial Office for assessment.

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

  • agricultural hydraulics
  • irrigation planning
  • water saving
  • soil pollution and remediation
  • efficient land uses
  • soil degradation
  • drift analysis
  • machine learning
  • artificial intelligence
  • smart sensors

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Published Papers (5 papers)

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Research

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24 pages, 5160 KB  
Article
A Simple Platform for Emulating Irrigation Scenarios and Its Applicability for Big Data Collection Toward Water Preservation via In Situ Experiments
by Dimitrios Loukatos, Athanasios Fragkos, Paraskevi Londra, Leonidas Mindrinos, Georgios Kargas and Konstantinos G. Arvanitis
Land 2026, 15(3), 464; https://doi.org/10.3390/land15030464 - 13 Mar 2026
Viewed by 505
Abstract
Modern agriculture has to alleviate extremes in water demand and/or water waste. In this regard, this work showcases how soil moisture instruments can be combined with low-end microcontrollers, energy-efficient communication protocols, single-board computers, flow and pressure sensors, and purpose-built actuators to form a [...] Read more.
Modern agriculture has to alleviate extremes in water demand and/or water waste. In this regard, this work showcases how soil moisture instruments can be combined with low-end microcontrollers, energy-efficient communication protocols, single-board computers, flow and pressure sensors, and purpose-built actuators to form a synergistic platform able to generate and study realistic irrigation scenarios. These scenarios, potentially emulating anomalies such as clogged emitters or pipe leaks with a satisfactory time granularity of a few minutes, provide valuable data that pave the way for the creation of intelligent models intercepting water misuse events and/or irrigation failures. The proposed system utilizes widely available, well-documented, low-cost components to form a functioning whole which is optimized for outdoor, low-power, low-maintenance and long-term operation and is accessible remotely via typical end-user devices. Two drip irrigation points were set up, each having a TEROS 12 and a TEROS 10 instrument placed at different depths, while a prototype water flow/pressure control and report system was developed. All modules sent data in real time, via LoRa, to a central node implemented using a Raspberry Pi for further processing and to make them widely available via common network infrastructures, also provisioning for remote scenario invocation. The system does not claim to achieve specific irrigation water savings, but it contributes to maintaining/increasing the benefits of modern irrigation practices (such as drip irrigation). This goal is served by emulating a wide variety of irrigation events and by gathering and studying the corresponding data. These multimodal data are collected at a frequency of a few minutes, reflecting key irrigation-specific parameters with an accuracy better than or equal to 3%. The exact steps for specific hardware and software component interoperation are clearly explained, allowing other teams of researchers and/or university educators worldwide to be inspired and benefit from platform replication. Full article
(This article belongs to the Special Issue Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in Agriculture)
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26 pages, 174853 KB  
Article
Understanding Flash Droughts in Greece: Implications for Sustainable Water and Agricultural Management
by Evangelos Leivadiotis, Evangelia Farsirotou, Ourania Tzoraki, Silvia Kohnová and Aris Psilovikos
Land 2025, 14(11), 2290; https://doi.org/10.3390/land14112290 - 20 Nov 2025
Viewed by 1011
Abstract
Flash droughts—characterized by their sudden development, severity, and short duration—impose considerable challenges on the soil–water complex of agricultural systems, especially under the Mediterranean climate. Though gaining increasing global significance, Mediterranean flash droughts are still understudied. This study examines the spatiotemporal variability of flash [...] Read more.
Flash droughts—characterized by their sudden development, severity, and short duration—impose considerable challenges on the soil–water complex of agricultural systems, especially under the Mediterranean climate. Though gaining increasing global significance, Mediterranean flash droughts are still understudied. This study examines the spatiotemporal variability of flash droughts in Greece for the period 1990–2024 using 5-day (pentad) ERA5-Land root-zone soil moisture (0–100 cm) at 0.25° resolution. A percentile-threshold approach detected flash drought events, and their main features—including frequency, duration, magnitude, intensity, decline rate, recovery rate, and recovery duration—were evaluated at the annual and seasonal levels. Findings indicate that Central Greece and Thessaly face the highest frequency and longevity of flash droughts, while Western Greece and Peloponnese and Western Macedonia are characterized by rapid development but intense recovery. An innovative empirical classification framework founded on decline and recovery rates indicated that Mild Fast Recovery events prevail in northern and central Greece, while Intense but Recovering events dominate in western and southern Greece. These results offer new perspectives on how flash droughts impact soil–water availability and agricultural resilience, providing a data-driven platform to aid sustainable water management, early warning systems, and adaptation strategies for Mediterranean agriculture in conditions of climate variability. Full article
(This article belongs to the Special Issue Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in Agriculture)
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23 pages, 5274 KB  
Article
Assessing an Optical Tool for Identifying Tidal and Associated Mangrove Swamp Rice Fields in Guinea-Bissau, West Africa
by Jesus Céspedes, Jaime Garbanzo-León, Marina Temudo and Gabriel Garbanzo
Land 2025, 14(11), 2144; https://doi.org/10.3390/land14112144 - 28 Oct 2025
Viewed by 1653
Abstract
An optical remote sensing approach was developed to identify areas with high and low salinity within the mangrove swamp rice system in West Africa. Conducted between 2019 and 2024 in Guinea-Bissau, this study examined two contrasting rice-growing environments, tidal mangrove (TM) and associated [...] Read more.
An optical remote sensing approach was developed to identify areas with high and low salinity within the mangrove swamp rice system in West Africa. Conducted between 2019 and 2024 in Guinea-Bissau, this study examined two contrasting rice-growing environments, tidal mangrove (TM) and associated mangrove (AM), to assess changes in vegetation dynamics, soil salinity concentration, and soil chemical properties. Field sampling was conducted during the dry season to avoid waterlogging, and soil analyses included texture, cation exchange capacity, micronutrients, and electrical conductivity (ECe). Meteorological stations recorded rainfall and environmental conditions over the period. Moreover, orthorectified and atmospherically corrected surface reflectance satellite imagery from PlanetScope and Sentinel-2 was selected due to their high spatial resolution and revisit frequency. From this data, vegetation dynamics were monitored using the Normalized Difference Vegetation Index (NDVI), with change detection calculated as the difference in NDVI between sequential images (ΔNDVI). Thresholds of 0.15 ≤ NDVI ≤ 0.5 and ΔNDVI > 0.1 were tested to identify significant vegetation growth, with smaller polygons (<1000 m2) removed to reduce noise. In this process, at least three temporal images per season were analyzed, and multi-year intersections were done to enhance accuracy. Our parameter optimization tests found that a locally calibrated NDVI threshold of 0.26 improved site classification. Thus, this integrated field–remote sensing approach proved to be a reproducible and cost-effective tool for detecting AM and TM environments and assessing vegetation responses to seasonal changes, contributing to improved land and water management in the salinity-affected mangrove swamp rice system. Full article
(This article belongs to the Special Issue Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in Agriculture)
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22 pages, 959 KB  
Article
Predictive Modeling of Zinc Fractions in Zinc Chloride-Contaminated Soils Using Soil Properties
by Edyta Nartowska, Anna Podlasek, Magdalena Daria Vaverková, L’ubica Kozáková and Eugeniusz Koda
Land 2025, 14(9), 1825; https://doi.org/10.3390/land14091825 - 7 Sep 2025
Cited by 1 | Viewed by 1882
Abstract
The combined effects of soil properties, zinc (Zn), and chloride ion (Cl−) concentrations on Zn distribution across soil fractions are poorly understood, even though zinc chloride (ZnCl2) contamination in industrial soils is a major source of mobile Zn and [...] Read more.
The combined effects of soil properties, zinc (Zn), and chloride ion (Cl−) concentrations on Zn distribution across soil fractions are poorly understood, even though zinc chloride (ZnCl2) contamination in industrial soils is a major source of mobile Zn and poses significant environmental risks. This study aimed to (1) assess how the soil type, physicochemical properties, and Zn concentration affect Zn distribution in Community Bureau of Reference (BCR)-extracted fractions; (2) evaluate the impact of Cl− on Zn mobility; and (3) develop predictive models for mobile and stable Zn fractions based on soil characteristics. Zn mobility was analyzed in 18 soils differing in Zn and Cl−, pH, specific surface area (SSA), organic matter (OM), and texture (sand, silt, clay (CLY)), using a modified BCR method. Zn fractions were measured by atomic absorption spectroscopy (AAS). Analysis of Covariance was used to assess Zn distribution across soil types, while Zn fractions were modeled using non-linear regression (NLR). The results showed that mobile Zn increased with the total Zn, and that the soil type and Zn levels influenced Zn distribution in soils contaminated with ZnCl2 (Zn 304–2136 mg·kg−1 d.m.; Cl− 567–2552 mg·kg−1; pH 3.5–7.5; CLY 11–22%; SSA 96–196 m2·g−1; OM 0–4.8%). Although Cl− enhanced Zn mobility, its effect was weaker than that of Zn. Predictive models based on the total Zn, SSA, and CLY accurately estimated Zn in mobile and stable fractions (R > 0.92), whereas the effects of the pH and OM, although noticeable, were not statistically significant. Full article
(This article belongs to the Special Issue Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in Agriculture)
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Review

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28 pages, 5737 KB  
Review
Benefits and Challenges of Artificial Intelligence in Soil Science—A Review
by Christos Kikis and Vasileios Antoniadis
Land 2026, 15(2), 331; https://doi.org/10.3390/land15020331 - 15 Feb 2026
Viewed by 1417
Abstract
Artificial intelligence (AI) is rapidly affecting soil science by allowing the analysis of large, complex, and heterogeneous datasets that were previously difficult to exploit. The current review synthesizes the recent advances of AI and highlights how these tools are applied in key soil [...] Read more.
Artificial intelligence (AI) is rapidly affecting soil science by allowing the analysis of large, complex, and heterogeneous datasets that were previously difficult to exploit. The current review synthesizes the recent advances of AI and highlights how these tools are applied in key soil science domains, such as digital soil mapping, soil fertility management, soil moisture prediction, contamination monitoring, soil carbon assessment, and precision agriculture. This study evaluates the performance of different AI methods, showing that techniques such as random forests, neural networks, and convolutional neural networks often outperform traditional methods in capturing non-linear soil-environment. At the same time, it identifies major limitations such as data scarcity, reproducibility, lack of large datasets, uncertainty, and the “black-box” nature of many models. This review concludes that AI has strong potential to support sustainable soil management, but its real-world impact will depend on better data integration, explainability, standardization, and closer collaboration with scientists, technologists, and end-users. Full article
(This article belongs to the Special Issue Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in Agriculture)
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