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
CROP MAPPING APPLICATIONS AT SCALE: USING GOOGLE EARTH ENGINE TO ENABLE GLOBAL CROP AREA AND STATUS MONITORING USING FREE AND OPEN DATA SOURCES
The confluence of rapidly growing streams of "free and open" satellite imagery at 10-30 m spatial resolution, expending libraries of sophisticated open source software components for geospatial data processing and the increase in publicly available open data sets is driving major changes in agricultural monitoring activities. In the next years, we can expect a scale step in derived crop area and status information at parcel level from the combined use of global sensors such as Landsat-8, Sentinel 1 and 2. In order to handle the unprecedented flow of such data into value adding agricultural mapping and monitoring applications, novel approaches need to be developed to ensure a globally consistent use in a "knowledge inference" context in support of, for instance, food security analysis. We demonstrate the use of Google Earth Engine (GEE) as a prototype environment that could possibly support such a context with 3 different examples.
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