e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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Title

AMSR2 SOIL MOISTURE DOWNSCALING USING MULTISENSOR PRODUCTS THROUGH MACHINE LEARNING APPROACH

en
Abstract

Soil moisture is important to understand the interaction between the land and the atmosphere, and has an influence on hydrological and agricultural processes such as drought and crop yield. In-situ measurements at stations have been used to monitor soil moisture. However, data measured in the field are point-based and difficult to represent spatial distribution of soil moisture. Remote sensing techniques using microwave sensors provide spatially continuous soil moisture. The spatial resolution of remotely sensed soil moisture based on typical passive microwave sensors is coarse (e.g., tens of kilometers), which is inadequate for local or regional scale studies. In this study, AMSR2 soil moisture was downscaled to 1km using MODIS products that are closely related to soil moisture through statistical ordinary least squares (OLS) and random forest (RF) machine learning approaches. RF (r(2) = 0.96, rmse=0.06) outperformed OLS (r(2) = 0.47, rmse=0.16) in modeling soil moisture possibly because RF is much flexible through randomization and adopts an ensemble approach. Both approaches identified T.V (i.e., multiplication between land surface temperature and normalized difference vegetation index) and evapotranspiration. AMSR2 soil moisture produced from the VUA-NASA algorithm appeared overestimated at high elevation areas because the characteristics of ground data for validation and correction used in the algorithm were different from those in our study area. In future study, AMSR2 soil moisture based on the JAXA algorithm will be evaluated with additional input variables including land cover, elevation and precipitation.

en
Year
2015
en
Country
  • KR
Organization
  • Ulsan_Natl_Inst_Sci_&_Technol_UNIST (KR)
Data keywords
  • machine learning
en
Agriculture keywords
  • agriculture
en
Data topic
  • big data
  • modeling
en
SO
2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Document type

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Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.