High-Resolution Soil Moisture Remote Sensing for Precision Agriculture and Crop Monitoring
A Special Issue of Agriculture (ISSN 2077-0472) belonging to the section "Artificial Intelligence and Digital Agriculture".
Deadline for manuscript submissions: 15 December 2026 | Viewed by 293
Editors
Interests: remote sensing; geospatial AI; soil moisture retrieval; crop monitoring; LAI estimation; data fusion; precision agriculture; irrigation management; time-series analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: geographic information science (GIS); remote sensing; agro-geoinformatics; crop mapping; geospatial cyberinfrastructure; digital twin; land use and land cover change; machine learning; image processing; geospatial data interoperability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Soil moisture is a fundamental variable controlling land–atmosphere interactions, crop growth, and water resource management. Over the past decades, satellite missions and in situ sensing networks have enabled large-scale soil moisture monitoring; however, their coarse spatial resolution limits direct application in field-scale agricultural management. Recent advances in high-resolution remote sensing, data fusion, and machine learning have opened new opportunities to derive fine-scale soil moisture information tailored to precision agriculture and crop monitoring needs.
This Special Issue aims to advance the development and application of high-resolution soil moisture products to support irrigation management, drought assessment, crop yield prediction, and sustainable agricultural practices. It seeks to bridge the gap between satellite observations and actionable, field-level decision-making by integrating multi-source data, including optical, thermal, microwave, and UAV-based observations.
Cutting-edge research topics include downscaling methodologies, data assimilation, synergistic use of multi-platform observations (e.g., Sentinel, SMAP, UAV), AI-driven retrieval algorithms, and uncertainty quantification. We also welcome studies linking soil moisture dynamics with crop phenology, evapotranspiration, and agroecosystem resilience.
We invite original research articles, reviews, and case studies that address methodological innovations, validation strategies, and real-world applications across diverse agricultural systems and climatic regions.
Dr. Haoteng Zhao
Dr. Chen Zhang
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture 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 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 moisture
- remote sensing
- precision agriculture
- data fusion
- downscaling
- UAV
- machine learning
- irrigation management
- crop monitoring
- SMAP
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