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:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
For structuring national irrigation policies and determining the exact yield production shares separately generated from irrigated and unirrigated farmland practices fast and simple to employ methods are of great importance. In this work, through utilizing entire satellite image frames, with no masking or cropping any parts out, a local, parcel-based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) means and variance techniques' mapping abilities were investigated. This was the preliminary stage in the delineation of irrigated and non-irrigated parcels. Although, even at this phase we obtained mapping results with reasonably high precision, a further process was performed using Land Surface Temperature (LST) data retrieved from Landsat 8 satellite images. LST tuning up produced irrigated areas to be mapped with accuracy rates escalating above 89%. The results obtained suggest that the NDVI, NDMI means and variance approach coupled up along with LST data holds the capacity to assist in building up trustworthy agrarian statistics for TARBIL project and in formation of a robust Agricultural Geographic Information System on national basis.
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