Long time data series and data stewardship reference model

Abstract

The need for accessing historical Earth Observation (EO) data series strongly increased in the last ten years, particularly for long-term science and environmental monitoring applications. This trend is likely to increase even more in the future, in particular regarding the growing interest on global change monitoring which is driving users to request time-series of data spanning 20 years and more, and also due to the need to support the United Nations Framework Convention on Climate Change (UNFCCC). While much of the satellite observations are accessible from different data centers, the solution for analyzing measurements collected from various instruments for time series analysis is both difficult and critical. Climate research is a big data problem that involves high data volume of measurements, methods for on-the-fly extraction and reduction to keep up with the speed and data volume, and the ability to address uncertainties from data collections, processing, and analysis. The content of EO data archives is extending from a few years to decades and therefore, their value as a scientific time-series is continuously increasing. Hence there is a strong need to preserve the EO space data without time constraints and to keep them accessible and exploitable. The preservation of EO space data can also be considered as responsibility of the Space Agencies or data owners as they constitute a humankind asset. This publication aims at describing the activities supported by the European Space Agency relating to the Long Time Series generation with all relevant best practices and models needed to organise and measure the preservation and stewardship processes. The Data Stewardship Reference Model has been defined to give an overview and a way to help the data owners and space agencies in order to preserve and curate the space datasets to be ready for long time data series composition and analysis.