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
ON THE USE OF TEMPORAL-SPECTRAL DESCRIPTORS FOR CROP MAPPING, MONITORING AND CROP PRACTICES CHARACTERIZATION
Irrespective if remote sensing data are acquired by active or passive sensors, high or medium resolution, key information is the temporal signature. This is particularly true - but not limited to - agriculture, where the spatio-temporal dynamic is significant. Spectral (here meant in frequency and polarimetric terms) information, definitely, complements the temporal one. In this paper, temporal-spectral descriptors are derived from sigma nought time series acquired from various Synthetic Aperture Radar (SAR) systems over different agroecological zones in Senegal, The Gambia, Vietnam. It is shown that: a limited set of temporal descriptors is sufficient to generate a reliable crop map; the selection of the appropriate time period is crucial; the temporal combination of wavelengths and polarizations may enhance the level of detail and product's reliability; the use of temporal descriptors derived from multiannual, annual, and seasonal time series data provides, from an agronomic perspectives, complementary information; temporal-spectral descriptors have an agronomic meaning, hence they should be used in knowledge based classifiers; by sparse time series the adoption of temporal-spectral descriptors is more effective than a dedicated crop detection algorithm.
Inappropriate format for Document type, expected simple value but got array, please use list format