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
Multitemporal soil pattern analysis with multispectral remote sensing data at the field-scale
This research proposes a new model for the generation of basic soil information maps for precision agriculture based on multitemporal remote sensing data analysis and GIS spatial data modelling. It demonstrates (i) the potential of multitemporal soil pattern analysis (ii) to generate functional soil maps at field scale based on soil reflectance patterns and related soil properties and (iii) how to improve these soil maps based on the identification of static homogenous soil patterns by excluding temporal influences from the developed prediction model. Principal components and per-pixel analyses are used for the separation of static soil pattern from temporal reflectance pattern, influenced by (vital and senescent) vegetation and land management practices. The potential of the proposed algorithm is investigated using multitemporal multispectral RapidEye satellite imagery at a demonstration field "Borrentin" field in Northeast Germany. (C) 2015 Elsevier B.V. All rights reserved.
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