CLEAR-EO

CLEAR-EO – Climate Change, Extreme Weather and Air Quality Anomalies Resilience Through Novel Earth Observation Advanced Data Analytics aims to develop an analysis and geospatial notification system on top of a federated data space, which integrates EO-based on-demand decision making workflows services for:

a) urban flash flooding,

b) agricultural resilience to Climate Change impacts and

c) air quality forecasting using satellite and in-situ IoT data.

In short

CLEAR-EO focuses on leveraging advanced Earth Observation (EO) data, next-generation satellite missions (such as MTG and EPS-SG), and cloud-based infrastructure (such as the European Weather Cloud) to develop an integrated analysis and geospatial alert system (CLEAR-EO application). This system will contribute to timely and effective management of climate change impacts across three key areas:

  • Management of urban flash flooding
  • Adaptation of agricultural activity to both short- and long-term climate change effects
  • Monitoring and forecasting air pollution

 

At the core of CLEAR-EO lies the Virtual Observatory (VO): a geospatial notification system that interconnects satellite data, in-situ observations, and predictive models to deliver timely, location-specific alerts and forecasts.

Furthermore, CLEAR-EO lays the groundwork for integrating new services into Copernicus and DestinE (Destination Earth) ecosystems.

With long-standing expertise in smart farming technologies, NEUROPUBLIC is leading the development of the agricultural component of the CLEAR-EO analysis system. By combining EO data (e.g., Sentinel, MTG), reanalysis datasets, and its extensive network of IoT agrometeorological stations, NP is developing:

  • A soil anomaly detection service, integrating estimates from multiple models and data sources (e.g. ASCAT, SMOS, in-situ measurements, soil moisture models), and
  • A meteorological early warning system for extreme weather events (such as drought and water/flood stress) that can severely affect crops, using both physical and data-driven models.

 

These services will be tailored to the specific needs of different crops and regions and will be continuously improved based on user feedback collected during pilot demonstrations.

The CLEAR-EO project has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No 101182722.

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