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  • Within the TERENO initiative, four terrestrial observatories, collecting huge amounts of environmental data, are being set up since 2008. To manage, describe, exchange and publish these data, the distributed Spatial Data Infra- structure TEODOOR (http://www.tereno.net) was created. Each institution responsible for an individual observatory sets up its own local data infrastructure, which may communicate with each other to exchange data and metadata internally or to the public by OGC compliant Web services. The TEODOOR data portal serves as a database node to provide scientists and decision makers with reliable and well-accessible data and data products. Various tools like hierarchical search or Web-GIS functions allow a deeper insight into the different observato- ries, test sites and sensor networks. Sensor data can be queried and selected for measured parameters, stations and/or time periods, and can be visualized and downloaded according to a shared TERENO data policy. Currently, TEODOOR provides free access to data from more than 500 monitoring stations.

  • Soil moisture is measured continuously with the wireless sensor network SoilNet (Bogena et al., 2010). The sensor network in the Wuestebach test site was installed in 2008 and consists of 150 end device units. Soil moisture data are interpolated into a 5x5 m grid for each depth by Ordinary Kriging using the daily mean of the maximum value of the two sensors measurements at each depth. Obviously wrong data or data, which have been assigned as being incorrect are not used. Semivariograms are estimated for each time step and depth using a spherical model. Validity of the Kriging estimation is checked by cross validation. For each time step a reporting document is created and stored to allow for further analyses

  • The worldwide Sensor Web comprises observation data from diverse sources. Each data provider may process and assess datasets differently before making them available online. This information is often invisible to end users. Therefore, publishing observation data with quality descriptions is vital as it helps users to assess the suitability of data for their applications. It is also important to capture contextual information concerning data quality such as provenance to trace back incorrect data to its origins. In the Open Geospatial Consortium (OGC)’s Sensor Web Enablement (SWE) framework, there is no sufficiently and practically applicable approach how these aspects can be systematically represented and made accessible. This paper presents Q-SOS—an extension of the OGC’s Sensor Observation Service (SOS) that supports retrieval of observation data together with quality descriptions. These descriptions are represented in an observation data model covering various aspects of data quality assessment. The service and the data model have been developed based on open standards and open source tools, and are productively being used to share observation data from the TERENO observatory infrastructure. We discuss the advantages of deploying the presented solutions from data provider and consumer viewpoints. Enhancements applied to the related open-source developments are also introduced.

  • The target of the Open Geospatial Consortium (OGC) is interoperability of geographic information, which means creating opportunities to access geodata in a consistent, standardized way. In the domain of sensor data, the target will be picked up within the OGC Sensor Web Enablement Initiative and especially reached through the Sensor Observation Service (SOS) standard. This one defines a service for a standardized access to time series data and is usually used for in situ sensors (like discharge gauges and climate stations). Although the standard considers raster data, no implementation of the standard for raster data exists presently. In this paper an OGC-compliant Sensor Observation Service for a standardized access to raster data is described. A data model was developed that enables effective storage of the raster data with the corresponding metadata in a database, reading this data in an efficient way, and encoding it with result formats that the SOS-standard provides.

  • abstract