# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 4 Data Acquisition and Integration
### Earth Observation and Modeling Data Sources  
Two main types of sources of data about our environment exist in relevant applications, namely, observation data sources and modeling data sources. Observation data sources are based on different kinds of sensing processes that obtain values of relevant properties over entities of interest. A data model [11] and a related ontology [13] have already been described in Section 3. The Sensor Model Language (SensorML) [56], defined by the OGC, provides a model and an XML encoding of metadata to describe environmental sensor systems (process in OGC notation). The metadata supported for a process includes descriptions of its inputs and outputs. The key element of a simple process is its process method, which provides a description of the methodology used by the process to generate the outputs from the inputs. For example, the process method of an air quality sensor would provide a description of the electrochemical device that generates currents and the algorithms that transform currents in gas concentrations. Aggregate processes are defined as complex interconnections of components that may be in turn simple or aggregate processes. A specific type of process is defined for simple real processing devices, called a physical component. A physical system is an aggregate process that models a complex real device that may be composed of physical and software components. If we restrict to physical components and systems, they may be classified as either in-situ or remote, depending on whether the observed entity (feature of interest) is located in the surroundings of the sensor or at some distance. Alternatively, they may also be classified as either static or mobile, depending on whether they are installed on static or mobile platforms. Examples of observations obtained from physical systems of different types are illustrated in Figure 8. Examples of static platforms with in-situ sensors are the networks of environmental stations. At the top left of the figure, the locations of a collection of stations are shown, together with the time series of values of a given property in one of the stations. An example of a mobile platform with in-situ sensors is a radiosonde, whose values at each location of its trajectory are illustrated at the top right part of the figure. It is noticed that the geospatial data generated by in-situ sensors are vector feature data. On the other hand, remote sensors use to retrieve raster coverages as values of their observations. If the platform is static, usually, all the coverages have the same spatial extent, as it is illustrated in the bottom-left part of the figure. As an example, a meteorological radar generates a time series of precipitation spatial coverages, all of them with the same spatial extent. On the other hand, spatial coverages generated by mobile remote sensors use to provide some kind of mosaic over a specific area called swath, as it is shown in the bottom right part of the figure. Examples of mobile remote sensors are those installed on-board of satellites. Apart from the above physical devices, traditional environmental observation is many times supported by field campaigns undertaken by experts. With the proliferation of the use of smartphones, such human-based sensing started to be supported by mobile applications [57]. One step beyond this practice is to open the use of the data collection mobile applications to the citizens, moving towards a people-centric sensing approach [58], i.e., making use of mobile devices to gather information obtained from both lightweight sensors (either equipped in the device or connected to it) and people senses. Mobile sensing approaches were classified as in [59] as either personal or community. The former use personal data for personal applications such as walking and running support, whereas the latter try to monitor large-scale phenomena measured by many people. Community sensing is further subdivided into participatory and opportunistic sensing.