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The GEOSS Platform is a key contribution to the goal of building the Global Earth Observation System of Systems (GEOSS). It enables a harmonized discovery and access of Earth observation data, shared online by heterogeneous organizations worldwide. This work analyzes both what is made available in the GEOSS Platform by the data providers and how users are utilizing it including multiyear trends, updating a previous analysis published in 2017. The present statistics derive from a 2021 EOValue report funded by the European Commission. The offer of GEOSS Platform data has been the object of various analyses, including data provider characterization, data sharing trends, and data characterization (comprising metadata quality analysis, thematic analysis, responsible party identification, spatial–temporal coverage). GEOSS data demand has also been the object of several analyses, including data consumer characterization, utilization trends, and requested data characterization (comprising thematic analysis, spatial–temporal coverage, and popularity). Among the findings, a large amount of shared data, mostly from satellite sources, emerges with an issue of low metadata quality and related discovery match. Moreover, the trend in usage is decreasing. Therefore, the progressive disconnection of the GEOSS platform from its data Providers and Users and other possible causes are also reported. [ABSTRACT FROM AUTHOR]

The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before. These changes will unlikely stop or even decelerate in the near future. There is an urgent need for a new scientific approach and an advanced form of evidence-based decision-making towards the benefit of society, the economy, and the environment. To understand the impacts and interrelationships between humans as a society and natural Earth system processes, we propose a new engineering discipline, Big Earth Data science. This science is called to provide the methodologies and tools to generate knowledge from diverse, numerous, and complex data sources necessary to ensure a sustainable human society essential for the preservation of planet Earth. Big Earth Data science aims at utilizing data from Earth observation and social sensing and develop theories for understanding the mechanisms of how such a social-physical system operates and evolves. The manuscript introduces the universe of discourse characterizing this new science, its foundational paradigms and methodologies, and a possible technological framework to be implemented by applying an ecosystem approach. CASEarth and GEOSS are presented as examples of international implementation attempts. Conclusions discuss important challenges and collaboration opportunities. [ABSTRACT FROM AUTHOR]

In 2015, it was adopted the 2030 Agenda for Sustainable Development to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. The year after, 17 Sustainable Development Goals (SDGs) officially came into force. In 2015, GEO (Group on Earth Observation) declared to support the implementation of SDGs. The GEO Global Earth Observation System of Systems (GEOSS) required a change of paradigm, moving from a data-centric approach to a more knowledge-driven one. To this end, the GEO System-of-Systems (SoS) framework may refer to the well-known Data-Information-Knowledge-Wisdom (DIKW) paradigm. In the context of an Earth Observation (EO) SoS, a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data – e.g. social and economic datasets. These elements are: Essential Variables (EVs), Indicators and Indexes, Goals and Targets. Their generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social Ecosystem. This includes: collect, formalize, publish, access, use, and update knowledge. ConnectinGEO project analysed the knowledge necessary to recognize, formalize, access, and use EVs. The analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains. [ABSTRACT FROM AUTHOR]

In the Earth system science domain, most current digital infrastructures are able to support data access rather than provide answers to complex questions – noticeably, supporting the ability to address the what-if questions posed by users. To this purpose, integrated modeling is an indispensable element. This work follows a recognizable gap characterized by high-level steps towards model integration: the Science-to-Information Technology barrier. This manuscript introduces an innovative approach to address such a gap in the “Model Web” framework initiative promoted by GEO (Group on Earth Observation) on its GEOSS (Global Earth Observation System of Systems) program. The methodological approach consists of five technologically neutral steps to build executable workflows from abstract business processes. A high-level architecture, based on the brokering pattern, is introduced to implement the proposed approach. By extending the GEOSS brokering framework, a proof-of-concept implementation is presented, argued, and ultimately experimentations and conclusions are discussed. [ABSTRACT FROM AUTHOR]

This paper addresses the emerging issue of integrating data sharing and computing e-infrastructures for multidisciplinary applications. In the recent years several solutions have been proposed to implement digital infrastructures for sharing and processing scientific data and observations. Spatial data infrastructures currently enable effective and efficient geo-information data sharing in many disciplinary communities, and innovative solutions are under development to support new open data and linked data paradigms. In parallel, High Performance Computing systems, computing grids and more recently cloud services, enable fast processing of big data. However, the integration of data and computing e-infrastructures is a raising issue in multidisciplinary research. In the context of the Global Earth Observation System of Systems (GEOSS) initiative, an innovative approach has been proposed. Taking into account that the heterogeneity of data and computing e-infrastructures and related technologies cannot be reduced beyond a certain extent, since it is due to the need of supporting use cases and scenarios from different scientific communities, a brokering solution has been designed and developed. A Business Process Broker (BPB) is a component which takes a formal description of a scientific business process, and translates it in an executable process which can be run on multiple and remote processing and workflow services. In doing this it solves all the interoperability issues in a (semi-)automated way. It allows lowering the entry barrier for both computing service providers and users, decoupling the specification of the scientific process from the underlying enabling infrastructures. The paper presents and discusses a BPB use-case from the European project IASON, implementing an Earth Observation application involving satellite image mosaicking, HPC computing services and spatial data e-infrastructures. [ABSTRACT FROM AUTHOR]

There are many expectations and concerns about Big Data in the sector of Earth Observation. It is necessary to understand whether Big Data is a radical shift or an incremental change for the existing digital infrastructures. This manuscript explores the impact of Big Data dimensionalities (commonly known as ‘V’ axes: volume, variety, velocity, veracity, visualization) on the Global Earth Observation System of Systems (GEOSS) and particularly its common digital infrastructure (i.e. the GEOSS Common Infrastructure). GEOSS is a global and flexible network of content providers allowing decision makers to access an extraordinary range of data and information. GEOSS is a pioneering framework for global and multidisciplinary data sharing in the EO realm. The manuscript introduces and discusses the general GEOSS strategies to address Big Data challenges, focusing on the cloud-based discovery and access solutions. A final section reports the results of the scalability and flexibility performance tests. [ABSTRACT FROM AUTHOR]

For disciplinary and domain applications, systems interoperability largely deals with the adoption of agreed technologies, standards, specifications and interfaces with a disciplinary/domain service bus or means of information exchange, if available. However, multi-disciplinary efforts make more complex demands on the type of systems and arrangements needed to support cross-domain activities. Thus, interoperability among diverse disciplinary and domain systems must be pursued adopting more flexible and sustainable approaches. This paper discusses the challenges for multi-disciplinary interoperability. The recent Brokering approach is introduced; this solution aims at interconnecting the heterogeneous disciplinary and domain service buses, avoiding the imposition of any federated or common specification. It can deliver a range of services such as discovery and access through a Broker Framework. The Brokering approach has been successfully introduced by the EuroGEOSS research project and recently adopted by the GEOSS Common Infrastructure (GCI). US NSF EarthCube initiative also has recognized the importance of brokering for its reference architecture. The GI-* technology, empowering the EuroGEOSS and the GCI brokering frameworks, is presented and discussed. [ABSTRACT FROM PUBLISHER]

Abstract: The Group on Earth Observation (GEO) Model Web initiative utilizes a Model as a Service approach to increase model access and sharing. It relies on gradual, organic growth leading towards dynamic webs of interacting models, analogous to the World Wide Web. The long term vision is for a consultative infrastructure that can help address “what if” and other questions that decision makers and other users have. Four basic principles underlie the Model Web: open access, minimal barriers to entry, service-driven, and scalability; any implementation approach meeting these principles will be a step towards the long term vision. Implementing a Model Web encounters a number of technical challenges, including information modelling, minimizing interoperability agreements, performance, and long term access, each of which has its own implications. For example, a clear information model is essential for accommodating the different resources published in the Model Web (model engines, model services, etc.), and a flexible architecture, capable of integrating different existing distributed computing infrastructures, is required to address the performance requirements. Architectural solutions, in keeping with the Model Web principles, exist for each of these technical challenges. There are also a variety of other key challenges, including difficulties in making models interoperable; calibration and validation; and social, cultural, and institutional constraints. Although the long term vision of a consultative infrastructure is clearly an ambitious goal, even small steps towards that vision provide immediate benefits. A variety of activities are now in progress that are beginning to take those steps. [Copyright &y& Elsevier]

River discharge is a critical water cycle variable, as it integrates all the processes (e.g. runoff and evapotranspiration) occurring within a river basin and provides a hydrological output variable that can be readily measured. Its prediction is of invaluable help for many water-related tasks including water resources assessment and management, flood protection, and disaster mitigation. Observations of river discharge are important to calibrate and validate hydrological or coupled land, atmosphere and ocean models. This requires using datasets from different scientific domains (Water, Weather, etc.). Typically, such datasets are provided using different technological solutions. This complicates the integration of new hydrological data sources into application systems. Therefore, a considerable effort is often spent on data access issues instead of the actual scientific question. This paper describes the work performed to address multidisciplinary interoperability challenges related to river discharge modeling and validation. This includes definition and standardization of domain specific interoperability standards for hydrological data sharing and their support in global frameworks such as the Global Earth Observation System of Systems (GEOSS). The research was developed in the context of the EU FP7-funded project GEOWOW (GEOSS Interoperability for Weather, Ocean and Water), which implemented a “River Discharge” application scenario. This scenario demonstrates the combination of river discharge observations data from the Global Runoff Data Centre (GRDC) database and model outputs produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) predicting river discharge based on weather forecast information in the context of the GEOSS. [ABSTRACT FROM AUTHOR]