# Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security
## 4 Earth Observations for Water Security: The Data Cube  
We identify three primary methodological limitations to testing our institutional and cognitive model of change of SES. The case of water security studies highlights significant issues related to measurement heterogeneity, case specificity and static perspective. Data cube technology could help overcome current limitations and offer reliable avenues to test hypothesis about the dynamics of social-ecological systems and water security by combining spatial and time data with no major technical requirements for users.  
Earth Observation Data to Consider the Spatial Dimension of Social-Ecological Systems and Water Security  
Remotely-sensed Earth Observations (EO) data appear to be necessary materials to take into account the spatial dimension of water security, and SES in general. Acquired by satellites together with in-situ measurements, they are a source for effective, quantitative and integrated capabilities (e.g., social and ecological) to monitor trends and variability for social-ecological systems. Regarding water security, authors already scrutinize water quantity and quality [6; 72]. With EO data, one can map and monitor water bodies. Optical and radar sensors help identifying changes in area and water quality information can be obtained by applying algorithms to retrieve information (e.g., total suspended matter and chlorophyll content) from water color [73]. Usually, spatial resolution is about 10-30 m and temporal resolution is about 5-15 days.  
One of the main advantages of using EO data is that they provide information at the pixel level and therefore can be aggregated or disaggregated according to ecological or social variables to ensure the spatial fit of the components of an SES. For example, a network of actors can be represented following its spatial perimeter and then can serve as the basic statistical unit from which pixel values can be aggregated and various statistical information can be derived using zonal statistics. Such an approach suits the analysis of spatial mismatch between actors, institutions and resource [74], but with large N dataset which allows inferential analysis. Consequently, the analysis is not framed by administrative boundaries but by the footprint of the variables of interest. In addition, this strategy of data collection could be used for treatment effect research design by allowing to test the treatment in space or time. As an illustration, the impact of the institutional complexity trap on resource use could be tested robustly.  
Two principal missions allow retrieving EO data related to water security. Firstly, the Gravity Recovery and Climate Experiment (GRACE) mission allows tracking anomalies of the Earth's gravity field and, consequently, extract information on groundwater resources. Secondly, the Soil Moisture and Ocean Salinity (SMOS) mission enables mapping soil moisture and can provide useful information for monitoring droughts and extreme events. Other information can be generated from EO data such as precipitation, evapotranspiration, land cover, snow, and temperature, which are all important variables to monitor the water cycle [72]. Usually, EO data are processed to generate information products that describe real-world variables, and these products are in turn converted into evidence-based information for decision support [75].