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Land
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Salinity Monitoring and Modelling at Different Scales: 2nd Edition
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Salinity Monitoring and Modelling at Different Scales: 2nd Edition

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1264

Special Issue Editors

Dr. Maria da Conceição Gonçalves

E-Mail Website
Guest Editor
Instituto Nacional de Investigação Agrária e Veterinária, Lisbon, Portugal
Interests: hydraulic properties; soil water dynamics; soil salinity; pedotransfer functions
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Farzamian

E-Mail Website
Guest Editor
Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Oeiras, Portugal
Interests: hydrogeophysics; environmental geophysics; geophysical monitoring; vadose zone; soil salinity
Special Issues, Collections and Topics in MDPI journals
Dr. Tiago Brito Ramos

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

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of Land entitled “Salinity Monitoring and Modelling at Different Scales: 2nd Edition”.

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

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Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 52399 KiB  
Article
Enhancing Soil Salinity Evaluation Accuracy in Arid Regions: An Integrated Spatiotemporal Data Fusion and AI Model Approach for Arable Lands
by Tong Su, Xinjun Wang, Songrui Ning, Jiandong Sheng, Pingan Jiang, Shenghan Gao, Qiulan Yang, Zhixin Zhou, Hanyu Cui and Zhilin Li
Land 2024, 13(11), 1837; https://doi.org/10.3390/land13111837 - 5 Nov 2024
Viewed by 542
Abstract
Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have [...] Read more.
Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R2) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R2 of 0.767 and an RMSE of 1.677 g·kg−1. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas. Full article
(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales: 2nd Edition)
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11 pages, 1567 KiB  
Article
Leaching Efficiency During Autumn Irrigation in China’s Arid Hetao Plain as Influenced by the Depth of Shallow Saline Groundwater and Irrigation Depth, Using Data from Static Water-Table Lysimeters and the Hydrus-1D and SIMDualKc Models
by Tiago B. Ramos, Meihan Liu, Haibin Shi, Paula Paredes and Luis S. Pereira
Land 2024, 13(11), 1797; https://doi.org/10.3390/land13111797 - 31 Oct 2024
Viewed by 477
Abstract
The need for controlling salinity in arid zones is essential for sustainable agricultural production and irrigation water use. A case study performed for two years in Hetao, Inner Mongolia, China, is used herein to rethink the contradictory issues of arid lands represented by [...] Read more.
The need for controlling salinity in arid zones is essential for sustainable agricultural production and irrigation water use. A case study performed for two years in Hetao, Inner Mongolia, China, is used herein to rethink the contradictory issues of arid lands represented by water saving and controlling soil and water salinity. Two sets of static lysimeters, where water table depths (WTDs) were fixed at 1.25, 150, 2.00, and 2.25 m, were continuously monitored, and soil water and solute data were used to calibrate and validate two models: the soil water balance model SIMDualKc and the deterministic soil water and salt dynamics model HYDRUS-1D. Once accurately calibrated, the models were used to simulate maize water use, percolation, and capillary rise, along with the observed variables for the actual WTD and the autumn irrigation applied. Simulation scenarios also considered agricultural system degradation and dynamic water table behavior. Results have shown that large leaching efficiencies (Lefs) were obtained for large irrigation depths in cases of shallow water tables, but higher Lefs corresponded to high application depths when the water table was deeper. Agricultural system degradation, particularly increased groundwater salinity, lowered Lef, regardless of WTD. Conversely, water savings were minimal and only achievable when considering the dynamic nature of groundwater. These results indicate that there is a need to define different WTDs based on soil characteristics that influence fluxes and root zone storage, as well as the impacts of newly installed drainage systems aimed at salt extraction. Full article
(This article belongs to the Special Issue Salinity Monitoring and Modelling at Different Scales: 2nd Edition)
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