Integrating Advanced Sensor Technologies for Enhanced Agricultural Weather Forecasts and Irrigation Advisories: The MAGDA Project Approach
Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteodrones, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteodrones to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA’s integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management.
Authors
- Martina Lagasio
- Amelia Fernández-Rodríguez (ORCID: https://orcid.org/0009-0000-1704-7145)
- Massimo Milelli (ORCID: https://orcid.org/0000-0003-3078-702X)
- Martijn de Klerk (ORCID: https://orcid.org/0009-0008-6512-8490)
- Elena Oberto
- Alessandro Fumagalli (ORCID: https://orcid.org/0000-0003-0269-1530)
- Fabiola Ramelli (ORCID: https://orcid.org/0000-0003-2051-4263)
- Lukas Hammerschmidt
- Francesco Uboldi (ORCID: https://orcid.org/0000-0002-8559-2839)
- Irina Onţel (ORCID: https://orcid.org/0000-0001-6231-4070)
- Julien Orensanz (ORCID: https://orcid.org/0009-0003-3972-7688)
- Andrea Gatti (ORCID: https://orcid.org/0000-0002-9563-6698)
- Eugenio Realini (ORCID: https://orcid.org/0000-0003-4119-6989)
- Stefano Barindelli (ORCID: https://orcid.org/0000-0002-6416-6099)
- Sergio Contreras (ORCID: https://orcid.org/0000-0003-3991-8241)
- Aso Validi
- Damir Haskovic
- Zenaida Chitu (ORCID: https://orcid.org/0009-0006-8201-3869)
Institutions
- CIMA Research Foundation (IT)
- Administraţia Naţională de Meteorologie (RO)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-05-26
- DOI
- https://doi.org/10.3390/rs17111855
- Citations
- 2
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 3.50