Integrating Remote Sensing, Machine Learning, and Process-Based Modelling for Monitoring Environmental and Agricultural Landscapes Under Climate Change
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 30 January 2026 | Viewed by 684
Special Issue Editors
Interests: agricultural landscapes; machine learning; remote sensing
Interests: remote sensing; GIS; geomorphology; landcape ecology; landscape archaeology; soil erosion; land cover/land use change; natural hazarrds monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue will focus on the adoption and integration of advanced remote sensing technologies, process-based and biophysical models, and cutting-edge machine learning (ML), artificial intelligence (AI), and deep learning (DL) algorithms. The aim is to develop robust frameworks for monitoring, analysing, and managing environmental and agricultural landscapes in the context of land-use changes, evolving impacts of climate change, and feedback from the Intergovernmental Panel on Climate Change (IPCC).
We invite contributions that explore innovative methodologies and their applications, including, but not limited to, the following:
- Development and implementation of remote sensing technologies for multiscale monitoring (local, regional, and global applications);
- Synergistic use of remote sensing with process-based and biophysical models to enhance predictive capabilities;
- Incorporation of ML, AI, and DL for automating and improving the accuracy of environmental assessments;
- Case studies (e.g., ongoing local, regional, and global issues) demonstrating adaptive management practices informed by remote sensing and modelling insights;
- Novel approaches to linking spatial data with actionable management strategies for climate resilience (e.g., agriculture, forestry, and disaster response).
This Special Issue will highlight how the integration of diverse methodologies enables the extraction of actionable insights critical for addressing global challenges, including sustainable land management, carbon sequestration, soil organic carbon, food security, and biodiversity conservation.
Dr. Michael Gbenga Ogungbuyi
Dr. Dimitrios D. Alexakis
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. Remote Sensing 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 2700 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
- remote sensing
- machine learning
- artificial intelligence
- deep learning
- process-based models
- biophysical modelling
- climate change impacts
- environmental monitoring
- agricultural landscapes
- adaptive management
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