Multi-Modal Remote Sensing and Data Assimilation for Crop Type Mapping, Growth, Phenology and Yield Estimation
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 August 2026 | Viewed by 1725
Editors
Interests: remote sensing; agriculture; vegetation monitoring; modelling; statistical analysis
Interests: remote sensing; agriculture; land cover monitoring
Interests: remote sensing; agriculture; phenomics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in remote sensing data collection, analysis, and communication, have significantly broadened its application in agricultural research. These advances have also facilitated the transition from research to operational use, as evidenced by the expanding range of agricultural analytics and products. However, data source selection, often based on revisit frequency, spatial, or spectral characteristics, can limit the outcomes of agricultural studies.
Multi-modal remote sensing and data assimilation offer tailored approaches by integrating diverse data sources (e.g., optical, radar, LiDAR, thermal, field data, and weather) and platforms (satellite, airborne, UAVs, and ground-based) into a single analytical process. While these methods can produce accurate results efficiently, they involve handling complex, large-scale datasets, especially for high-spatial resolution within-field applications.
Despite growing interest, relatively few studies utilising multi-modal approaches for agriculture are being presented. This Special Issue focuses on crop type mapping, growth, phenology, and yield estimation to support informed decision-making and sustainable agricultural practices. It encourages large-scale, multi-season studies with robust statistical validation, showcasing innovative research and methodologies to enhance agricultural outcomes and their operational applications.
Dr. Angélica Suárez
Dr. Andrew Clark
Dr. Michael Gomez Selvaraj
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. 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
- agricultural remote sensing
- precision agriculture
- crop mapping
- crop stress
- crop phenology
- yield prediction
- soil monitoring
- data fusion
- time-series analysis
- irrigation management
- drought monitoring
- unmanned aerial vehicles (UAVs)
- satellite imagery
- hyperspectral and multispectral imaging
- LiDAR
- machine learning
- statistical analysis
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