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
Downscaling and forecasting of evapotranspiration using a synthetic model of wavelets and support vector machines
Providing reliable forecasts of evapotranspiration (ET) at farm level is a key element toward efficient water management in irrigated basins. This paper presents an algorithm that provides a means to downscale and forecast dependent variables such as ET images. Using the discrete wavelet transform (DWT) and support vector machines (SVMs), the algorithm finds multiple relationships between inputs and outputs at all different spatial scales and uses these relationships to predict the output at the finest resolution. Decomposing and reconstructing processes are done by using 2-D DNVT with basis functions that suit the physics of the property in question. Two-dimensional DWT for one level will result in one datum image (low-low-pass filter image) and three detail images (low-high, high-low, and high-high). The underlying relationship between the input variables and the output are learned by training an SVM on the datum images at the resolution of the output. The SVM is then applied on the detailed images to produce the detailed images of the output, which are needed to help downscale the output image to a higher resolution. In addition to being downscaled, the output image can be shifted ahead in time, providing a means for the algorithm to be used for forecasting. The algorithm has been applied on two case studies, one in Bondville, IL, where the results have been validated against AmeriFlux observations, and mother in the Sevier River Basin, UT.
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