• DocumentCode
    255125
  • Title

    The observation capability reason of optical satellite sensor for soil moisture monitoring

  • Author

    Xiaolei Wang ; Nengcheng Chen ; Xunliang Yang ; Zeqiang Chen

  • Author_Institution
    State Key Lab. of Inf. Eng. in Surveying, Wuhan Univ., Wuhan, China
  • fYear
    2014
  • fDate
    11-14 Aug. 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The accuracy of soil moisture depends on these sensors´ observation capability. Current methods have simple inference rules to provide obvious results for users, which have not clear connection between the sensors and specific application. This paper proposes an ontology and the reason rules of observation capability about optical satellite sensor, in order to mine deeper observation capability of sensors for soil moisture monitoring. The data in inversion model of soil moisture come from the information about band types in satellite sensors´ images. So we define some rules of band type to make it available. We build and manipulate the ontology of sensors based on the SSN for inference. The reasoning was executed using the Jena rule engine. They were applied to some sensors and inversion models to reason the observation capabilities. We execute this method in the application for better selection based on available sensors.
  • Keywords
    hydrological equipment; hydrological techniques; moisture; remote sensing; soil; Jena rule engine; inference rules; optical satellite sensor; satellite sensor images; sensor observation capability; soil moisture monitoring; Cognition; Data models; Monitoring; Ontologies; Optical sensors; Satellites; Soil moisture; observation capability reason; optical satellite sensor; semantic web; sensor web; soil moisture;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Agro-geoinformatics (Agro-geoinformatics 2014), Third International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/Agro-Geoinformatics.2014.6910582
  • Filename
    6910582