Member States must take the necessary measures to ensure that transactions financed by the EAGF (European Agricultural Guarantee Fund) are implemented correctly. Furthermore, Member States, through the National Paying Agencies, must prevent irregularities and take the appropriate actions if they do occur. For this purpose, the national authorities are required to operate an Integrated Administration and Control System (IACS) in order to ensure that payments are made correctly, irregularities are prevented, revealed by controls, followed up and amounts unduly paid are recovered. Controls are currently operated on the basis of workflows relying on the use of EO data (aerial data, VHR and HR satellite data) integrated with additional geodata and databases. High and very-high resolution satellite imagery (HR and VHR) are currently used to check farmer’s declarations, and increasingly to verify the compliance of their farming practices with agro-environmental rules. In general, National Paying Agencies operate the workflows through service integrators, that process EO data and tune the operational workflow. Service integrators are often local enterprises, that operate with different software solutions based on a common EO data feed. Main stakeholders, external to the National Paying Agencies and involved in the process, are: satellite image providers, aerial image providers, value adders on image data, service integrators. The value of the market for satellite image providers is in the order of 6-7 Milion euros per year, with a strong improvement foreseen for 2015.

For its controls, the National Paying Agencies adopt a national agriculture-oriented land cover reference map (updated in general on a three-years basis with aerial data), and performs detailed and tailored checks by means of satellite data over risk based and random sample zones covering 5% of the farms on a yearly basis.

The controls are aimed at:

  • To check the presence of cultivated land and specific crops in case of coupling
  • To check the diversification of crops according to greening criteria
  • To check the presence and maintenance of permanent pastures
  • To check the presence and maintenance of land lying fallow

All these controls are operated only on the 5% of the farms with fresh VHR and HR EO data. On all the other farms, the control must rely on EO data that could have been collected one or two years before (due to LPIS updating cycle), with a negative impact on farm compliance evaluation. In addition to this situation, it must be noticed that most of the checks could provide more reliable results if based on multi-temporal time series, since adopted data could introduce bias in the interpretation.


Services provided in the pilot will support:

  • Identify parcels (monitoring objects) over which the declared crop is potentially different from the one that extracted from the EO models (outliers). The service is based on Sentinel data and machine learning methods for the description of the crop and analytics methods for the identification of the outliers. The service will allow the performing of big data analytics to various crop indicators on parcel level.
  • Identify different crops present inside a single farm when the global size of declared surface is exceeding a specific threshold. This is due to the fact that CAP requires crops diversification such that farmers should cultivate at least two/three different crops. The service will be based on the management of optical satellite data together with farmer declarations information and limited ground measures if any, and will provide an indication of possible compliance/not compliance of the farmer vs. EU CAP requirements.


The main KPIs of the pilot are:

  • Information correctness: inconsistencies success ratio having an acceptable error rate when tested on historic data.
  • System usage: Number of users of the services, and number of users visiting the website with information about.
  • Satellite on-the-spot checks %: percentage of agricultural parcels covered with satellite on-the-spot checks. This can “significantly increase the efficiency of on-thespot checks necessary for CAP payments”.