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Data spaces are conceptualised as a trusted and secure distributed data ecosystem through which to exchange resources in the Web. Several efforts define guidance toward data space implementation, such as reference architectures and frameworks. As yet, the proposed data space solutions do not provide common and mature implementation options yet, and this gap between concept and implementation risks confusing users and developers. However, well-recognised organisations have been developing solutions and standards that address interoperability and good data exchange practices for decades, especially in the domain of geospatial information and remote sensing. Therefore, this paper compares the available solutions, providing the mapping and integration of the proposed blueprints to available interoperable standards. This concrete mapping, followed by a discussion with experts, results in a proposal of integrated reference data space building blocks, and an overview of the related standards and solutions. It is designed to support the effective practical implementation of data spaces and to guide future solution developments. This work can form the base for effective collaboration among different organisations, clearly identifying their scopes. A key role is apparent for standards and use cases from the remote sensing and geospatial domains, which have achieved wide adoption and maturity over the past years. [ABSTRACT FROM AUTHOR]

According to the European Data Strategy established in 2020, the European Open Science Cloud (EOSC) is described as "the basis for a science, research and innovation data space... and will be connected and articulated with the sectoral data spaces", being one of the 14 European Common Data Spaces. While current cross-sectoral interactions with the EOSC are realised through the association's Health Data Task Force, advancements in other EU priorities, such as the Green Deal and the Digital Strategy, should be accelerated in the green and digital transitions and integrated by reinforcing each other to be climate-neutral by 2050. With this motivation, this paper systematically reviews data spaces and digital twins (DTs) within the context of research and innovation. Specifically, focusing on the relevance of the EOSC to the Green Deal Data Space (GDDS) and DTs of the Earth, the relationship between them is explored using a topic search with various keyword combinations in the Web of Science and CORDIS databases. Based on the selected scientific articles and projects, collaboration opportunities are mapped to connect relevant stakeholders. Furthermore, existing and developing service components that could contribute to technical building blocks for the GDDS are identified. In summary, key findings are highlighted, addressing the current gaps and opportunities among the GDDS initiatives presented in this review. [ABSTRACT FROM AUTHOR]

The use of remote sensing generates needs and requirements for in situ data that can be used for calibrating and validating remote sensing products and numerical models. In 2015, the United Nations adopted the 17 Sustainable Development Goals (SDGs), aiming at ending poverty, protecting the planet, and ensuring peace and prosperity. In most papers of this Special Issue, the computation of SDG indicators using remote sensing data shows great potential. Applications in Ecosystems and Forestry Ecosystem health can be monitored from remote sensing using Gross Primary Productivity (GPP). [Extracted from the article]

In the field of Earth observation, the importance of in situ data was recognized by the Group on Earth Observations (GEO) in the Canberra Declaration in 2019. The GEO community focuses on three global priority engagement areas: the United Nations 2030 Agenda for Sustainable Development, the Paris Agreement, and the Sendai Framework for Disaster Risk Reduction. While efforts have been made by GEO to open and disseminate in situ data, GEO did not have a general way to capture in situ data user requirements and drive the data provider efforts to meet the goals of its three global priorities. We present a requirements data model that first formalizes the collection of user requirements motivated by user-driven needs. Then, the user requirements can be grouped by essential variable and an analysis can derive product requirements and parameters for new or existing products. The work was inspired by thematic initiatives, such as OSCAR, from WMO, OSAAP (formerly COURL and NOSA) from NOAA, and the Copernicus In Situ Component Information System. The presented solution focuses on requirements for all applications of Earth observation in situ data. We present initial developments and testing of the data model and discuss the steps that GEO should take to implement a requirements database that is connected to actual data in the GEOSS platform and propose some recommendations on how to articulate it. [ABSTRACT FROM AUTHOR]

There is a growing recognition of the interdependencies among the supply systems that rely upon food, water and energy. Billions of people lack safe and sufficient access to these systems, coupled with a rapidly growing global demand and increasing resource constraints. Modeling frameworks are considered one of the few means available to understand the complex interrelationships among the sectors, however development of nexus related frameworks has been limited. We describe three open-source models well known in their respective domains (i.e. TerrSysMP, WOFOST and SWAT) where components of each if combined could help decision-makers address the nexus issue. We propose as a first step the development of simple workflows utilizing essential variables and addressing components of the above-mentioned models which can act as building-blocks to be used ultimately in a comprehensive nexus model framework. The outputs of the workflows and the model framework are designed to address the SDGs. [ABSTRACT FROM AUTHOR]

When defining indicators on the environment, the use of existing initiatives should be a priority rather than redefining indicators each time. From an Information, Communication and Technology perspective, data interoperability and standardization are critical to improve data access and exchange as promoted by the Group on Earth Observations. GEOEssential is following an end-user driven approach by defining Essential Variables (EVs), as an intermediate value between environmental policy indicators and their appropriate data sources. From international to local scales, environmental policies and indicators are increasingly percolating down from the global to the local agendas. The scientific business processes for the generation of EVs and related indicators can be formalized in workflows specifying the necessary logical steps. To this aim, GEOEssential is developing a Virtual Laboratory the main objective of which is to instantiate conceptual workflows, which are stored in a dedicated knowledge base, generating executable workflows. To interpret and present the relevant outputs/results carried out by the different thematic workflows considered in GEOEssential (i.e. biodiversity, ecosystems, extractives, night light, and food-water-energy nexus), a Dashboard is built as a visual front-end. This is a valuable instrument to track progresses towards environmental policies. [ABSTRACT FROM AUTHOR]

Geospatial data have become a crucial input for the scientific community for understanding the environment and developing environmental management policies. The Global Earth Observation System of Systems (GEOSS) Clearinghouse is a catalogue and search engine that provides access to the Earth Observation metadata. However, metadata are often not easily understood by users, especially when presented in ISO XML encoding. Data quality included in the metadata is basic for users to select datasets suitable for them. This work aims to help users to understand the quality information held in metadata records and to provide the results to geospatial users in an understandable and comparable way. Thus, we have developed an enhanced tool (Rubric-Q) for visually assessing the metadata quality information and quantifying the degree of metadata population. Rubric-Q is an extension of a previous NOAA Rubric tool used as a metadata training and improvement instrument. The paper also presents a thorough assessment of the quality information by applying the Rubric-Q to all dataset metadata records available in the GEOSS Clearinghouse. The results reveal that just 8.7% of the datasets have some quality element described in the metadata, 63.4% have some lineage element documented, and merely 1.2% has some usage element described. [ABSTRACT FROM PUBLISHER]