Together with the rapid development of sensor technologies and the implementation of environmental observation networks (e.g. MOSES, TERENO, Digital Earth, eLTER, CUAHSI, ICOS, ENOHA,…) a large number of data infrastructures are being created to manage and provide access to observation data. However, significant advances in earth system understanding can only be achieved through better and easier integration of data from distributed infrastructures. In particular, the development of methods for the automatic real-time processing and integration of observation data in models is required in many applications. The automatic meaningful integration of these data sets is often hindered due to semantic and structural differences between data and poor metadata quality. Improvement in this field strongly depends on the capabilities of dealing with fast growing multi-parameter data and on effort employing data science methods, adapting new algorithms and developing digital workflows tailored to specific scientific needs. Automated quality assessment/control algorithms, data discovery and exploration tools, standardized interfaces and vocabularies as well as data and processing exchange strategies and security concepts are required to interconnecting distributed data infrastructures. Besides the technical integration, also the meaningful integration for different spatial and temporal support or measurement scales is an important aspect. This session focuses on the specific requirements, techniques and solutions to process, provide and couple observation data from (distributed) infrastructures and to make observation data available for modelling and other scientific needs.