Crop Yield Prediction Using Remote Sensing Techniques
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: 26 February 2026 | Viewed by 781
Special Issue Editor
Special Issue Information
Dear Colleagues,
Global agricultural systems are increasingly being challenged by climate variability, land-use change, and the growing demand for food security. Accurate, timely, and scalable methods for crop yield prediction are essential to support precision agriculture, optimize resource use, and guide policy decisions. In this context, remote sensing has emerged as a powerful tool for monitoring crop dynamics and estimating yield across spatial and temporal scales. With advances in satellite, UAV, and sensor technologies—as well as machine learning and data fusion approaches—yield modeling has entered a new era of precision and operational feasibility.
This Special Issue aims to explore cutting-edge research and innovative applications of remote sensing techniques for crop yield prediction. It aligns with the journal’s scope by promoting interdisciplinary studies that integrate Earth observation, agronomy, environmental science, and computational methods to address agricultural challenges. Contributions are encouraged from both methodological and applied perspectives, covering a wide range of crops and agroecological regions.
Topics of interest include, but are not limited to:
- Yield estimation using multispectral, hyperspectral, radar, or thermal imagery;
- Machine learning and hybrid modeling approaches (e.g., LUE, RF, CNNs);
- Data fusion (e.g., MODIS–Landsat, Sentinel–UAV);
- Phenological monitoring and crop growth modeling;
- In-season yield forecasting and stress detection;
- Integration of environmental, soil, and climate data;
- Operational systems and platforms for yield prediction;
- Ground truthing, uncertainty, and model validation.
Original research articles, reviews, and case studies are welcome.
Dr. Maninder Singh Dhillon
Guest Editor
Manuscript Submission Information
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Keywords
- crop yield prediction
- remote sensing
- NDVI and vegetation indices
- machine learning
- light use efficiency (LUE)
- data fusion (e.g., MODIS–Landsat)
- phenology and crop growth models
- climate and environmental drivers
- UAV and satellite imagery
- precision agriculture
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