Remote Sensing and AI for Agricultural Land Mapping, Monitoring, and Management
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 2987
Special Issue Editors
Interests: remote sensing; land cover mapping; semantic segmentation
Interests: remote sensing; machine learning; deep learning; UAV; crop monitoring
Interests: tidal flat mapping; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: irrigation and drainage engineering; agricultural drought and water resource management; drought monitoring, mitigation, planning, and policy; risk and vulnerability management; remote sensing for drought monitoring and management; soil moisture and hydrologic/watershed modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Agricultural land plays a vital role in ensuring global food security, sustaining rural livelihoods, and maintaining ecological balance. It encompasses a wide range of land use types, including croplands, fallow lands, irrigated and rainfed systems, agricultural facilities, and aquaculture areas. Accurate and timely mapping and monitoring of agricultural land are essential for supporting precision agriculture, resource management, and evidence-based policy-making.
With the rapid development of remote sensing technologies—including high-resolution satellite imagery, UAV platforms, hyperspectral sensing, and SAR data—combined with advances in artificial intelligence (AI), new opportunities have emerged for large-scale, high-precision agricultural land monitoring. In particular, the integration of deep learning models, foundation models, and multi-source data fusion techniques enables more robust analysis of agricultural land dynamics, productivity, and sustainability.
This Special Issue aims to provide a platform for AI technologies to address key challenges in agricultural land mapping, monitoring, analysis, and management. We welcome contributions addressing both specific land subtypes and broader agricultural land systems, including their spatial distribution, temporal dynamics, environmental impacts, and policy implications.
Topics of Interest:
- Agricultural Land Mapping and Classification
- Large-scale mapping of croplands and agricultural systems using remote sensing
- AI-based classification of diverse agricultural land types and patterns
- Transfer learning and foundation models for global agricultural mapping
- Agricultural Land Monitoring and Dynamics
- Time-series analysis of agricultural land changes (e.g., cropping patterns, land abandonment, expansion)
- Monitoring agricultural intensification, rotation, and phenology
- Detection of land use/land cover changes in agricultural regions
- Multi-Source Data Fusion and AI Methods
- Integration of optical, SAR, UAV, and hyperspectral data
- Deep learning models (CNNs, Transformers, diffusion models) for agricultural applications
- Self-supervised, weakly supervised, and few-shot learning in agricultural remote sensing
- Precision Agriculture and Smart Farming
- Remote sensing-based crop monitoring, yield estimation, and stress detection
- AI-driven decision support systems for irrigation, fertilization, and pest control
- Field-scale monitoring using UAV and high-resolution imagery
- Agricultural Land and Environmental Sustainability
- Assessment of soil health, water use, and land degradation
- Monitoring agricultural impacts on ecosystems (e.g., pollution, biodiversity loss)
- Carbon accounting and greenhouse gas emissions from agricultural lands
- Agricultural Infrastructure and Facilities
- Mapping and monitoring of greenhouses, irrigation systems, and agricultural engineering structures
- Detection of plastic mulch, controlled-environment agriculture (CEA), etc.
- Aquaculture and Water-Based Agriculture
- Monitoring aquaculture systems in inland and coastal regions
- AI-based detection and mapping of aquaculture ponds and cages
- Policy, Governance, and Socioeconomic Applications
- Remote sensing for agricultural policy evaluation and compliance monitoring
- Agricultural subsidy verification and land-use regulation
- Linking remote sensing data with socioeconomic datasets
- Big Data Platforms and Open Science
- Cloud computing (e.g., GEE) for agricultural monitoring
- Open datasets and benchmarking for AI in agriculture
- Scalable systems for near real-time monitoring
Dr. Peng Zhang
Dr. Quanlong Feng
Dr. Pengfei Tang
Dr. Won-Ho Nam
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. 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
- artificial intelligence (AI)
- agricultural mapping
- agricultural monitoring
- deep learning
- multi-source data fusion
- precision agriculture
- big data platforms
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