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Land
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Integrating Remote Sensing, Geospatial Technologies, and AI for Sustainable Land...
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Integrating Remote Sensing, Geospatial Technologies, and AI for Sustainable Land and Soil System Management

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1794

Special Issue Editor

Dr. Carmine Massarelli

E-Mail Website
Guest Editor
Italian National Research Council, Rome, Italy
Interests: GIS analysis; environmental pollution; machine learning; prototypes for environmental monitoring; smart technology; pesticide residue analysis; open-source scientific software
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil and land systems are fundamental components of environmental sustainability, yet they are increasingly threatened by climate change, urbanization, and intensive land use. The integration of emerging technologies such as deep learning and digital twin modelling opens up new opportunities for understanding and managing these complex socio-ecological systems. Advancing scientific research in this domain is crucial for supporting data-driven environmental planning and resilient development strategies.

This Special Issue aims to present innovative contributions to the application of advanced computational methods for land and soil system monitoring and analysis. This topic directly aligns with the journal's interdisciplinary focus on sustainable land use, environmental engineering, and digital innovation for natural resource management. We welcome the submission of studies that combine geospatial analysis, machine learning, and system modelling to inform evidence-based decision-making. Topics of interest include the following:

  • Emerging data-processing technologies (deep learning/machine-based learning) for soil monitoring: studies focusing on the use of AI and machine learning to detect, classify, and predict soil degradation, contamination, and health indicators at multiple scales.
  • The application of digital twin technology for the evaluation of land/soil/water systems: contributions that explore digital twin frameworks that can simulate dynamic interactions in complex land/water/soil systems, supporting scenario analysis and decision-support systems.
  • Land/land use/land-cover change: articles investigating the drivers, patterns, and impacts of land cover transformation using remote sensing, geospatial intelligence, and spatio-temporal analysis.
  • Land system science and social–ecological system research: research integrating land system dynamics with socio-economic and ecological feedback, fostering transdisciplinary approaches to landscape governance.
  • Urban contexts, urban–rural interactions, and urban planning and development: papers that analyze land and soil management in urban settings, the impacts of urban expansion, and the integration of green infrastructure in planning frameworks.

Dr. Carmine Massarelli
Guest Editor

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

  • AI and machine learning
  • digital twin
  • soil monitoring
  • land use change
  • urban planning
  • social–ecological systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 6903 KB  
Article
Application of GIS Technology in Soil Quality Management and Agricultural Development Orientation in Vietnam
by Nguyen Thi Hong Hanh, Doan Thanh Thuy, Nguyen Dinh Trung, Nguyen Hai Nui and Cao Truong Son
Land 2026, 15(3), 445; https://doi.org/10.3390/land15030445 - 11 Mar 2026
Viewed by 405
Abstract
Land is the fundamental basis for maintaining agricultural production and ensuring food security. The task of managing and sustainably utilizing land resources has always been a priority for every country in the world. The study used GIS-MEC technology to integrate data from seven [...] Read more.
Land is the fundamental basis for maintaining agricultural production and ensuring food security. The task of managing and sustainably utilizing land resources has always been a priority for every country in the world. The study used GIS-MEC technology to integrate data from seven types of single-factor maps to construct a soil quality map with 47 land units (including eight land units with an area >100 ha, 29 land units with an area from 10 to 100 ha, and 10 land units with an area <10 ha). In addition, by combining soil quality maps and the nutritional needs of different crops, an assessment of land suitability for six major crops was conducted, and three key crops were selected for focused development: rice, vegetables, and flowers. The application of GIS in soil quality management is in line with the current trends of digital transformation and integrated data management in Vietnam and around the world. However, this method has several limitations that need to be considered when applying it, such as dependence on expert expertise, high demands on input data and verification of output results, and limitations in analyzing trends and analyzing social, non-linear factors. Full article
(This article belongs to the Special Issue Integrating Remote Sensing, Geospatial Technologies, and AI for Sustainable Land and Soil System Management)
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25 pages, 6643 KB  
Article
From Analytical Detection to Spatial Prediction: LC–MS and Machine Learning Approaches for Glyphosate Monitoring in Interconnected Land–Soil–Water Systems
by Annamaria Ragonese and Carmine Massarelli
Land 2026, 15(2), 303; https://doi.org/10.3390/land15020303 - 11 Feb 2026
Viewed by 947
Abstract
The widespread application of glyphosate—the world’s most used herbicide—presents a significant environmental challenge due to its persistence and mobility within interconnected land–soil–water systems. This study addresses the limitations of traditional, discrete water monitoring by developing a predictive framework for glyphosate and its primary [...] Read more.
The widespread application of glyphosate—the world’s most used herbicide—presents a significant environmental challenge due to its persistence and mobility within interconnected land–soil–water systems. This study addresses the limitations of traditional, discrete water monitoring by developing a predictive framework for glyphosate and its primary metabolite, aminomethylphosphonic acid (AMPA), in the agricultural context of Apulia, Southern Italy. The methodology integrates high-sensitivity analytical chemistry with advanced spatial intelligence. Water samples were analyzed using an optimized UHPLC–MS/MS framework with pre-column derivatization (FMOC-Cl), achieving an ultra-trace Limit of Quantification (LOQ) of 0.025 μg/L. To transition from point data to continuous spatial profiles, a hybrid Machine Learning (ML) architecture was implemented. The model utilized a suite of geospatial predictors, including land use (Corine Land Cover), Digital Elevation Models (DEMs), and slope characteristics extracted from river offset lines. A dual-modeling strategy was employed: Global Models (Random Forest, Gradient Boosting, and KNN) for regional trends and Individual Models for river segments exhibiting sufficient internal variability. Analytical findings (2018–2024) revealed that AMPA consistently exhibited higher mean concentrations than glyphosate, reaching peaks of 9.27 μg/L. This trend is primarily attributed to its superior environmental persistence and a half-life of up to 240 days, compared to the parent compound. Spatiotemporal analysis identified critical peaks in the second quarter for glyphosate and extreme surges in the fourth quarter for AMPA, particularly in the Cervaro basin. The Random Forest Regressor emerged as the most robust predictive tool, achieving a coefficient of determination (R2) of approximately 0.68 at the global scale and up to 0.75 for localized models where data density was sufficient. The integration of ML frameworks allows for the identification of contamination “micro-hotspots” and the mapping of probabilistic pollutant distribution along entire river reaches without additional sampling costs. This high-fidelity diagnostic tool provides a cost-effective strategy for environmental agencies to implement targeted mitigation and proactive water resource protection in Mediterranean agroecosystems. Full article
(This article belongs to the Special Issue Integrating Remote Sensing, Geospatial Technologies, and AI for Sustainable Land and Soil System Management)
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