Multi-Source Remote Sensing Data Fusion for Crop Yield Prediction Through AI and Agro-Hydrological Advances
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 46
Special Issue Editors
2. Lab for Applied Science (LAS), The University of Alabama in Huntsville, Huntsville, AL 35805, USA
Interests: agriculture and food security; remote sensing; crop type mapping; crop yield estimation and prediction; impact of support policies on agricultural area
2. NASA Marshall Space Flight Center (MSFC), Huntsville, AL 35812, USA
Interests: agro-hydrology; evapotranspiration (ET); irrigation; water resources; environmental modeling; remote sensing applications
Interests: computer vision; machine learning; multimodal data fusion; spatiotemporal
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; agriculture; disaster risk reduction; climate resilience; geospatial analysis; decision-support systems; nature-based solutions
Special Issues, Collections and Topics in MDPI journals
Interests: land system science; land use and land cover change; earth system modeling; agricultural systems; environmental change; geospatial analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in Earth observation (EO) technologies and the growing availability of multi-sensor satellite data (optical, SAR, thermal and hyperspectral) have significantly enhanced our ability to monitor agricultural systems. In parallel, growing challenges related to water availability, irrigation demand and climate variability have highlighted the critical role of agro-hydrological processes—such as evapotranspiration (ET), soil moisture dynamics and crop water use—in determining crop productivity.
At the same time, rapid progress in machine learning, deep learning and computer vision is enabling new approaches to extract meaningful information from large-scale, multi-modal EO datasets. The integration of multi-source EO data with ET modeling, irrigation monitoring and AI-driven analytics offers powerful capabilities to quantify crop water stress, improve yield estimation and support scalable agricultural monitoring across diverse agroecosystems.
This Special Issue aims to advance interdisciplinary research at the intersection of multi-source remote sensing, agro-hydrological modeling and AI-driven data analytics for crop monitoring and yield prediction. It aligns with the scope of Remote Sensing by emphasizing methodological innovation, the integration of heterogeneous datasets and operational applications.
The Special Issue particularly encourages contributions that combine EO data with evapotranspiration estimation, irrigation assessment, soil moisture and crop water modeling, alongside machine learning and computer vision approaches, to develop scalable and transferable solutions for agricultural monitoring and decision support. Submissions addressing uncertainty quantification, model generalization, cloud-based processing platforms and applications for food security and decision support are particularly encouraged.
We welcome contributions that advance both methodological development and applied agricultural monitoring within the following interconnected themes:
- Multi-Source Earth Observation and Data Fusion
- Integration of optical, SAR, thermal, hyperspectral and UAV data
- Spatiotemporal data fusion and harmonization techniques
- Multi-resolution and cross-platform EO integration strategies
- AI-Driven Modeling and Spatiotemporal Analysis
- Machine learning and deep learning for agricultural remote sensing
- Transformer-based and foundation models for EO data
- Spatiotemporal prediction and representation learning
- Computer vision for crop mapping and classification
- Agro-Hydrological Processes and Water–Crop Interactions
- Evapotranspiration (ET) estimation and modeling
- Soil moisture dynamics and water stress detection
- Irrigation mapping and agricultural water management
- Coupling EO with hydrological and crop growth models (e.g., DSSAT)
- Crop Yield Prediction and Agricultural Forecasting
- Data-driven and hybrid yield prediction models
- Transfer learning and domain adaptation across regions
- Early-season yield forecasting and uncertainty quantification
- Phenology-based and time-series modeling approaches
- Scalable EO Workflows and Decision Support Systems
- Cloud-based geospatial analytics
- Near-real-time agricultural monitoring systems
- Operational decision support for farm and policy applications
- Model generalization and deployment in data-scarce regions
- Applications for Food Security and Climate Resilience
- Drought monitoring and crop stress assessment
- Climate variability impacts on agricultural productivity
- Regional to global food security analytics
- Sustainable irrigation and resource optimization strategies
Dr. Aparna Ravindra Phalke
Dr. Chinmay G. Deval
Dr. Thangarajah Akilan
Dr. Rishiraj Dutta
Dr. Lachezar Filchev
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
- multi-source data fusion
- crop monitoring
- crop yield prediction
- evapotranspiration (ET)
- irrigation mapping
- agro-hydrological modeling
- machine learning
- computer vision
- time-series analysis
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