# Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security
## 4 Earth Observations for Water Security: The Data Cube
### The Data Cube to Make Earth-Observation-Based Research Replicable over Time and Space  
Measurement heterogeneity and non-replicability are significant limitations to the study of SES dynamics. EO data cubes overcome these limitations. EO data can provide an efficient and effective mechanism to support water security monitoring. However, the full potential information of EO data is difficult to realize mainly because of their complexity, increasing volume, and the lack of efficient processing capabilities [76; 77]. Consequently, new approaches are required to rapidly analyze data in a transparent and repeatable manner and to facilitate the transformation of many EO data into actionable information and decision-ready products [75]. Data cubes are part of these new approaches. To tackle these issues and bridge the gap between user expectations and current big data analytical capabilities, EO Data Cubes (EODC) are a promising solution to store, organize, manage and analyze EO data. The main objective of EODC is to facilitate EO data usage by addressing volume, velocity,and variety challenges, and providing access to many spatiotemporal data, for a given geographic area over a specified time period, in an analysis ready format [13]. EODC reveals to be a promising tool for analyzing SES dynamics and testing the institutional and cognitive model of change. With EODCs, researchers could address the current methodological limitations enabling reliable and causal analysis of SES dynamics. It constitutes a data infrastructure offering: open access materials, systematic measurements and sample combination in a versatile format. Consequently, it facilitates replicable empirical strategies, common measurements and enlarging sample size by including new geographic areas and new periods. Because of the versatility of EODC, EO data can be merged with social data, building an integrative dataset of SES. For instance, with EODC, a discourse network analysis dedicated to grasp degrees of institutional innovation in learning process could be combined with water flows and water quality information over years. There are four currently operational technologies, namely the Open Data Cube supported by Digital Earth Australia [14], the EarthServer [78], the Google Earth Engine [79], and E-sensing [80]. These initiatives are paving the way to broaden the use of EO data by larger communities of users; they represent about 50 different implementations worldwide supporting decision-makers with timely and actionable information converted in meaningful geophysical variables; and ultimately are unlocking the information power of EO data. For example, the Swiss Data Cube holds currently 34 years of Landsat (1984-2018) and three years of Sentinel-2 (2015-2018) Analysis Ready Data (ARD) over Switzerland [13]. (An essential pre-condition to support user applications and generating usable information products is to facilitate data access, preparation, and analyses. The systematic and regular provision of Analysis Ready Data (ARD) can significantly reduce the burden of EO data usage. To be considered as ARD, data should be processed to a minimum set of requirements (e.g., radiometric and geometric calibration, atmospheric correction, and metadata description) and organized in a way that allows immediate analysis without additional effort. ARD correspond in optical imagery to surface reflectance products). This corresponds approximately to 7000 scenes from 1984 to 2018 for a total volume of 4 TB of ARD and more than 110 billion observations that can be converted in different environmental variables. Most of these EODC implementations are using Landsat data, partly because it is the oldest EO program, providing continuous observations for more than 45 years. In addition, since 2008, the entire data archive is freely and openly accessible [81]. This creates an unprecedented opportunities to use Landsat data in different contexts such as land cover changes, ecosystem mapping or water monitoring [82; 83].