## 4 Discussion
### Limitations and opportunities for sensor web based approach  
Within this study we have developed an automated processing facility to derive products relevant for environmental resource management from multi-source sensor data using a sensor web based approach. Interoperability between sensor data streams and connection with the information system was achieved by using open-source implementations of international standards for SWE and WMS. Use of common standards is an important requirement for upscaling of the developed facility, both in terms of number of sensors and inclusion of new sensor types. The interoperability of the presented processing facility could be further improved by using Web Processing Services (WPS) and Web Coverage Services (WCS) for interpolation of point data and access to gridded surfaces (e.g., satellite images) respectively [36]. Furthermore, additional functionality (e.g., temporal zonal statistics) in the WMS would be helpful to query and visualize the dynamic dimension of the products derived from the automated processing chain. Unfortunately, only a limited number of sensor web based meteorological stations was available. This is mainly related to the fact that the OGC SWE standards have only recently become available and most organizations responsible for _in situ_ sensor networks haven't yet introduced this in their actual processing facility. The expectation is that in the coming five to 10 years this will change and a large number of sensor nodes in the currently running geo-sensor networks (meteorology, groundwater etc.) will be operated within a standardized SWE based concept. In addition, dedicated networks (e.g., protection of dikes) which are newly developed will more quickly be adapted to newly available technology. However, in order to discover available sensors and sensor data, which take into account the dynamic behavior of the sensors, new approaches for sensor discovery need to be developed [37]. At this moment, actual processing and visualization of vegetation productivity within the developed facility is not real time yet but has a delay up to 10 days. Main reason is the time delay due processing and archiving of the MODIS images from the USGS Land Processes DAAC ftp download facility. This could be reduced by downloading raw unprocessed MODIS data (available within one day). However this would require development of a hardware and software set-up for complete (automated) processing of satellite data from raw data to surface reflectance. Another important limitation in the use of optical remote sensing data for daily monitoring is the problem of cloud coverage. For the Dutch situation, on average between 40 and 60 days per year have a cloud coverage smaller than 30% for the Netherlands during the MODIS overpass. As a result it will not be possible to produce daily maps of vegetation productivity with a complete coverage and also the frequency of coverage per pixel will be variable. For the Dutch situation with its relatively heterogeneous landscapes and short-scale variability, the medium resolution of 250 m to 500 m for MODIS data will often be to coarse. However, higher resolution satellite based remote sensing sources (10-30 m) are only limited available (Landsat, SPOT). In that respect, recent developments in the field of remote sensing data fusion [38, 39] could be of interest to improve the spatial resolution to relevant management units (\(<30\) m). Recent developments within NASA and ESA are aiming at provision of real-time earth observation products to the end-users, so the expectation is that within the coming years, real-time availability of earth observation data and products will improve. Within ESA, several projects [40] are dealing with the use of (OGC) SWE technology to connect _in situ_ sensor webs with remote sensing sensors which is brought together in the ESA Service Support Environment.