e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

You can access and play with the graphs:

Discover all records
Home page


Crop identification by means of seasonal statistics of RapidEye time series


Crop classification greatly benefits from the analysis of multi-temporal Earth Observation (EO) data within a growing season utilizing the distinct phenological behavior of each crop. RapidEye's high repetition rate increases the chances of providing sufficient high resolution image time series offering new ways of classifying crops. This study introduces a supervised decision tree (DT) classification approach using image objects in combination with seasonal statistics of various vegetation indices (VI) for crop identification. The aim of this study is, first, to show the potential of VI seasonal statistics for crop identification, and secondly, to evaluate the relative contribution of each variable to the overall classification accuracy. The results presented in this paper correspond to an area of 625 km(2) in Saxony-Anhalt, Germany. The cultivated landscape is characterized by large agricultural fields, with winter wheat, canola, corn and winter barley as the main crops. Crop identification accuracies were assessed on the basis of reference fields and the importance of each employed variable is assessed by rule set analysis. The classification accuracy for the test area demonstrates that the proposed approach of multi-temporal image analysis provides spatially detailed and thematically accurate information on the crop type distribution.

  • DE
    Data keywords
      Agriculture keywords
      • agriculture
      Data topic
      • modeling
      Document type

      Inappropriate format for Document type, expected simple value but got array, please use list format

      Institutions 10 co-publis
        Powered by Lodex 8.20.3
        logo commission europeenne
        e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
        Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.