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Marie Skłodowska-Curie Research Fellow (Ca' Foscari University and Euro-Mediterranean Center on Climate Change, Venice).

Curriculum Vitae
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Project scheduled for 2018.

Prompt access to climate services at a high-resolution level is crucial to assist stakeholders with the decision-making process. In the case of agriculture, decisions on the provision of water or fertilisers do heavily depend on the health status and water availability at the plot level. This project aims to facilitate access to a platform where early warning indicators of drought at the plot level are supplied for the Po river basin region.

The surge and popularity of climate services as tools to mitigate and adapt to climate change has peaked in Europe with the launch of the Copernicus programme coupled with the Sentinel family of satellites. Through satellite and in situ observations, new value-added climate services can be created to deliver near-real-time data that can be used for local and regional needs. In particular, our prior knowledge of the agricultural specificities (use of land, crops grown, phenology, etc) of the Po river basin area enables us with the potential to create a tailor-made climate service to warn stakeholders about the drought stress experienced at the local level over the growing season.

Features of the Service

  • Agricultural-oriented climate service addressing specifically the incidence of droughts on the Po river agricultural sector.

  • Multi-regional assessment, covering Valle d’Aosta, Lombardia, Emilia-Romagna and Veneto regions.

  • Declaration of the Regione Agraria (RA) as the basic unit level of analysis. RAs are a grouping of municipalities showing homogeneous natural (altimetry) and agricultural characteristics. Warnings at the RA level will be issued, as shown in the figure below.

  • Having geo-referenced yield and surface data from the RICA dataset, makes us able to exactly identify pixel-level activity and compare it directly with remote sensing and climatic observations. Besides, missing pixels or pixels not covered by the survey can be classified using dedicated crop masks, as the LCLU Riparian Zone Copernicus product.

  • Ex-ante identification of water limited ecosystems, improving the detail of the Macro-Region classification provided by the Agenzia regionale per la prevenzione, l´ambiente e l´energia dell´Emilia-Romagna (ARPAE) to improve drought risk and emergency management.

  • Wide use of remote sensing meteorological and vegetation indicators, such as Evapotranspiration, Soil moisture, fAPAR, Land Surface Temperature and, particularly, NDVI anomalies at a moderate resolution (300 metres), improving the 1-kilometer resolution of the ARPAE’s current analysis.

  • Near-real-time analysis, exploiting dekadal updates of remote sensing observations, improving on the monthly frequency of the ARPAE’s Bolletino della Siccità. Timely early warnings and open access, interactive web interface.

Drought warnings could be aggregated at the Regione Agraria (RA) level. Illustration of the RA classification in the Emilia Romagna Region. Source: ARPAE.


The provision of climate information in a way that assists decision making by individuals and organisations must be accompanied by appropriate engagement along with an effective access mechanism and must respond to user needs. This service will be featured via an interactive Google Earth Engine app and included as part of the CLARA project.


  • Yield forecasts. The above service may be complemented feeding crop-specific models calibrated to the study area, such as WOFOST, with actual field-level yield data to produce seasonal yield forecasts and, thus, accompany early warnings to potential drought impacts on yields.

  • Automatic learning. The choice of indicators to identify pixel-level environmental stress could be trained to identify triggers of actual drought onset, adapting Jean et al. (2016).

Materials and Methods

Remote sensing and Meteorological data will be extracted from different sources, such as Copernicus, MODIS, ESA or ECMWF. Yield and surface data comes from the RICA dataset. Methodology will be adapted from Meroni et al. (2017).