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DINOSAR

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Diagnostic tool that integrates optical infrared and SAR data DINOSAR

DINOSAR aims to develop Copernicus based algorithms to support smart farming applications that can be used worldwide, clouds, or no clouds. At the moment, most EO based crop monitoring tools are based on optical satellite inputs.... ver más

31/12/2026

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1M€

Presupuesto del proyecto: 1M€

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-12-01

HORIZON-EUSPA-2022-SPACE-02-51: EGNSS applications for Smart mobili... Expected Outcome:Projects are expected to contribute to the following outcomes:

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Duración del proyecto: 36 meses Fecha Inicio: 2023-12-01
Fecha Fin: 2026-12-31

Líder del proyecto Líder desconocido

Presupuesto del proyecto 1M€

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Descripción del proyecto DINOSAR aims to develop Copernicus based algorithms to support smart farming applications that can be used worldwide, clouds, or no clouds. At the moment, most EO based crop monitoring tools are based on optical satellite inputs. In areas with substantial cloud cover the use of these applications is extremely limited. To be able to introduce more sustainable crop management practices, reliable and continuous time series on crop phenology and health throughout the growing season are needed. This will support farmers to match agricultural inputs (fertilisers, pesticides, water) with what the crop actually needs, decreasing their environmental footprint. DINOSAR will do this by integrating the diagnostic power of optical, infrared and Synthetic Aperture Radar (SAR) signals. With the DINOSAR project we intend to kickstart a revolution in EO-based solutions that tackle challenges in agriculture (under clouds) by making full use of the Copernicus infrastructure. We intend to take the existing methodology a step further by designing a multi-sensor operational monitoring method for a single crop (sugarcane) capable of operating on large data volumes, and then extrapolating this approach to practical field cases and to other crops (and geographies) for which the application of EO-based applications has been underexplored. Rather than looking at optical and SAR based data as two parallel signals, we will focus on integrating the two early on in the processing chain. This has not been done before. Sugarcane in Colombia is our initial test-case, but we will not stop there. DINOSAR will also develop a methodology integrating the combined observations from optical, infrared and SAR EO satellites to monitor other crops in other geographies.

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