GeoAI, Climate Data Harmonization, and Multi-Source Remote Sensing for Smart Agriculture

Dear Colleagues,

The integration of multi-source Earth Observation data, gridded climate datasets, and Artificial Intelligence-based methodologies has led Smart Agriculture to evolve into a data-driven approach at different scales and spatial resolutions, for monitoring crop dynamics, water resources, and climate–agriculture interactions.

This heterogeneity in the available datasets introduces critical methodological challenges related to spatial resolution, scale consistency, dataset harmonization, bias propagation, uncertainty quantification, and reproducibility. Indeed, differences in modeling assumptions, physical parameterizations, and spatial scales across datasets can significantly affect agricultural monitoring outputs and the data-driven approach used to support smart agricultural decision-making.

This Special Issue will collect high-quality methodological contributions that explore novel geospatial frameworks, algorithms, and validation methodologies supporting climate-resilient agricultural systems and precision farming applications.

Suggested topics include, but are not limited to, the following:

1. Benchmarking and bias adjustment of gridded climate datasets;
2. Scale-aware modeling in crop monitoring;
3. Multi-source data integration (satellite, UAV, SAR, and climate grids);
4. Deep learning approaches for NDVI/GAI forecasting and vegetation stress detection;
5. GeoAI-based digital twins;
6. Uncertainty quantification in geospatial agricultural models;
7. Near-real-time crop monitoring;
8. Satellite-based phenotyping;
9. SAR-based soil moisture retrieval;
10. Scalable geospatial workflows in cloud-computing environments.

Dr. Alessandro Vitale
Prof. Dr. Francesco Lamonaca
Guest Editors

Manuscript Submission Information

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• geospatial metrology
• GeoAI
• smart agriculture
• gridded climate datasets
• uncertainty quantification
• remote sensing
• SAR–optical fusion
• digital twin
• NDVI forecasting
• multi-source data integration
• cloud computing platforms