Advances in High-Resolution Crop Mapping at Large Spatial Scales
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: 28 August 2025 | Viewed by 72
Special Issue Editors
Interests: remote sensing; deep learning; crop mapping; optical image; machine learning
Special Issue Information
Dear Colleagues,
This Special Issue of Remote Sensing is dedicated to the latest advancements in high-resolution crop mapping at large spatial scales. This issue aims to provide a comprehensive overview of the progress made in mapping agricultural landscapes using remote sensing technologies, highlighting the methodologies, applications, and challenges faced in the field. As global agriculture continues to evolve in response to climate change, shifting agricultural practices and increasing demands for food security, the need for accurate, large-scale crop monitoring has never been more critical. Remote sensing has proven to be an invaluable tool in addressing these challenges, with significant progress made in crop classification, monitoring, and analysis through the integration of multi-source satellite data, machine learning techniques, and advanced modeling approaches.
The goal of this Special Issue is to showcase the latest innovations in high-resolution crop mapping, focusing on improving the accuracy, scalability, and applicability of remote sensing methods for large-scale agricultural monitoring. We seek to enhance the methodological rigor in crop mapping by fostering consistent, transparent, and comparable approaches for monitoring crop dynamics, land use change, and agricultural productivity at both regional and global scales. By combining remote sensing with machine learning and spatial analysis, this issue aims to provide a clearer understanding of the spatiotemporal dynamics of crop systems and their relationship to broader environmental and socio-economic factors.
We welcome articles that focus on new methodologies for high-resolution crop mapping, change detection, and crop classification using remote sensing. Topics of interest include, but are not limited to, the following:
- The development and application of advanced algorithms for large-scale crop identification and mapping.
- The integration of multi-sensor data (e.g., Sentinel-1, Sentinel-2, Landsat) and data fusion techniques for crop monitoring.
- Machine learning and deep learning applications in crop classification, yield prediction, and change detection.
- The temporal analysis and seasonal dynamics of crops for regional and global monitoring.
- Case studies demonstrating operational applications in precision agriculture, climate adaptation, and food security.
We encourage the submission of papers based on novel methodological approaches and successful applications that contribute to the growing field of remote sensing-based crop monitoring.
Dr. Lingbo Yang
Dr. Pengliang Wei
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
- high-resolution crop mapping
- remote sensing for agriculture
- large-scale crop monitoring
- multi-source satellite data
- crop classification
- machine learning in agriculture
- temporal dynamics of crops
- agricultural change detection
- precision agriculture
- remote sensing applications in food security
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