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In May 2007, the INSPIRE directive established the path towards creating the European Spatial Data Infrastructure (ESDI). While the Joint Research Centre (JRC) defined a set of detailed implementation guidelines, the European member states determined the agencies responsible for delivering the different topics specified in the directive’s annexes. INSPIRE’s goal was - and still is - to organize and share Europe’s data supporting environmental policies and actions. However, the way that INSPIRE was defined limited contributions to the public sector, and limited topics to those specifically listed in its annexes. Technical challenges and a lack of appropriate tools have impeded INSPIRE from implementing its own guidelines, and even after 15 years, the dream of a continuous, consistent description of Europe’s environment has still not completely materialized. We should apply the lessons learnt in INSPIRE when we build the Green Deal Data Space (GDDS). To create the GDDS, we should start with ESDI (the European Spatial Data Infrastructure), but also engage and align with the ongoing preparatory actions for data spaces (e.g., for green deal and agriculture) as well as include actors and networks that have emerged or been organized in the recent years. These include: networks of in situ observations (e.g. the Environmental Research Infrastructures (ENVRI) community); Citizen Science initiatives (such as the biodiversity observations integrated in the Global Biodiversity Information Facility (GBIF), or sensor communities for e.g. air quality); predictive algorithms and machine learning models and simulations based on artificial intelligence (such as the ones deployed in the European Open Science Cloud, International Data Space Association and Gaia-X; services driven both by the scientific community and the private sector); remote sensing derived products developed by the Copernicus Services. Most of these data providers have already embraced the FAIR principles and open data, providing many examples of best practice which can assist newer adopters on the path to open science. In the Horizon Europe project AD4GD (AllData4GreenDeal), we believe that, instead of trying to force data producers to adopt cumbersome new protocols, we should take advantage of the latest developments in geospatial standards and APIs. These allow loosely coupled but well documented and interlinked data sources and models in the GDDS while achieving scientifically robust integration and easy access to data in the resulting workflows. Another fundamental element will be the adoption of a common and extensible information model enabling the representation and exchange of Green Deal related data in an unambiguous manner, including vocabularies for Essential Variables to organize the observable measurements and increase the level of semantic interoperability. This will allow systems and components from different technology providers to seamless interoperate and exchange data, and to have an integrated view and access to exploit the full value of the available data. The project will validate the approach in three pilot cases: water quality and availability of Berlin lakes, biodiversity corridors in the metropolitan area of Barcelona and low cost air quality sensors in Europe. The AD4GD project is funded by the European Union under the Horizon Europe program.

WQeMS aims to provide an operational Water Quality Emergency Monitoring Service to the water utilities industry in relation with the quality of the ‘water we drink’. This Copernicus service focuses on monitoring lakes for the delivery of drinking water and will provide open geospatial data products structured in Essential Water Variables. While Essential Climate variables are fully defined, a set of Essential Water Variables was proposed by GEOSS but was never fully adopted. In this communication we will present a metadata manager tool called GeM+ that adopts a general framework for Essential Variables (EV) that includes a renewed proposal for Essential Water Variables. EVs are included in a keyword library that relates them to SDG indicators. In addition, the GEM+ includes a library of quality measures defined in QualityML (that inherits and extends the UncertML approach) vocabulary that will be used and tested for the Water Quality Emergency Monitoring Service. The quality need for a vocabulary proposed by QualityML is now being adopted by the ISO 19157-3 proposal. GEM+ also implements the ISO 19115-1 approach for lineage (provenance) that allows to carefully document data product workflows used to create the geospatial products as a mechanism to describe the traceability, quality and reproducibility of the dada. Examples of water related dataset have been prepared showing how EVs, quality measures and provenance is used to semantically tag data, document quantitative quality estimations and present the sources and processes used to elaborate this Copernicus service candidate products. WQeMS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004157.

Given the circumstance that the process for the revision of the international standard ISO 19157 is currently open, this article presents a critical reflection on its content, application and some challenges posed by the new types of data (e.g. big data, BIM data, etc.), that also have a geospatial component and to which, therefore, this international standard can be applied as well. Proposals are put forward going along three lines of improvement, on the one hand the consideration of new data quality elements and on the other, the reinforcement of the interoperability of this international standard with other standards related to data quality, and finally various improvements (e.g. standardization of evaluation methods, clearly introducing the life cycle, improvement of the definition of metaquality, etc.) of the standard, which come from experience.

Half of the European Union (EU) land and the livelihood of 10 million farmers is threatened by unsustainable land-use intensification, land abandonment and climate change. Policy instruments, including the EU Common Agricultural Policy (CAP) have so far failed to stop this environmental degradation. BESTMAP will: 1) Develop a behavioural theoretical modelling framework to take into account complexity of farmers’ decision-making; 2) Develop, adapt and customize a suite of opensource, flexible, interoperable and customisable computer models linked to existing data e.g. LPIS/IACS and remote sensing e.g. Sentinel-2; 3) Link economic, individual-farm agent-based, biophysical ecosystem services and biodiversity and geostatistical socio-economic models; 4) Produce a simple-to-use dashboard to compare scenarios of Agri-Environmental Schemes adoption; 5) Improve the effectiveness of future EU rural policies’ design, monitoring and implementation.

This deliverable provides a General Framework for the BESTMAP Policy Impact Assessment Modelling (BESTMAP-PIAM) toolset.The BESTMAP-PIAM is based on the notion of defining (a) a typology of agricultural systems, with one (or more) representative case study (CS) in each major system; (b) mapping all individual farms within the case study to a Farm System Archetype (FSA) typology; (c) model the adoption of agri-environmental schemes (AES) within the spatially-mapped FSA population using Agent Based Models (ABM), based on literature and a survey with sufficient representative sample in each FSA of each CS, to elucidate the non-monetary drivers underpinning AES adoption and the relative importance of financial and non-financial/social/identity drivers; (d) linking AES adoption to a set of biophysical, ecological and socio-economic impact models; (e) upscaling the CS level results to EU scale; (f) linking the outputs of these models to indicators developed for the post-2020 CAP output, result and impact reports; (g) visualizing outputs and providing a dashboard for policy makers to explore a range of policy scenarios, focusing on cost-effectiveness of different AES.

WeObserve delivered the first European-wide Citizen Observatory (CO) knowledge platform to share best practices, to address challenges and to inform practitioners, policy makers and funders of COs. We present key insights from WeObserve activities into leveraging challenges to create interlinked solutions, connecting with international frameworks and groups, advancing the field through communities of practice and practitioner networks, and fostering an enabling environment for COs. We also discuss how the new Horizon Europe funding programme can help to further advance the CO concept, and vice versa, how COs can provide a suitable mechanism to support the ambitions of Horizon Europe.

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution.

In recent years, researchers of different communities have increased their efforts in formalizing a set of measurements regularly collected for analysing changes in Drivers, States, Impacts and Res...

Geospatial data is used not only to contemplate reality but also, in combination with analytical tools, to generate new information that requires interpretation. In this process data users gain knowledge about the data and its limitations (the user side of data quality) as well as knowledge on the status and evolutions of the studied phenomena. Knowledge can be annotations on top of the data, responses to questions, a careful description of the processes applied, a piece of software code or scripts applied to the data, usage reports or a complete scientific paper. This paper proposes an extension of the current Open Geospatial Consortium standard for Geospatial User Feedback to include the required knowledge elements, and a practical implementation. The system can incrementally collect, store, and communicate knowledge elements created by users of the data and keep them linked to the original data by means of permanent data identifiers. The system implements a Web API to manage feedback items as a frontend to a database. The paper demonstrates how a JavaScript widget accessing this API as a client can be easily integrated into existing data catalogues, such as the ECOPotential web service or the GEOEssential data catalogue, to collectively collect and share knowledge.

The amount of data that Sentinel fleet is generating over a territory such as Catalonia makes it virtually impossible to manually download and organize as files. The Open Data Cube (ODC) offers a solution for storing big data products in an efficient way with a modest hardware and avoiding cloud expenses. The approach will still be useful up to the next decade. Yet, ODC requires a level of expertise that most people who could benefit from the information do not have. This paper presents a web map browser that gives access to the data and goes beyond a simple visualization by combining the OGC WMS standard with modern web browser capabilities to incorporate time series analytics. This paper shows how we have applied this tool to analyze the spatial distribution of the availability of Sentinel 2 data over Catalonia and revealing differences in the number of useful scenes depending on the geographical area that ranges from one or two images per month to more than one image per week. The paper also demonstrates the usefulness of the same approach in giving access to remote sensing information to a set of protected areas around Europe participating in the H2020 ECOPotential project.

The production of official statistical and geospatial data is often in the hands of highly specialized public agencies that have traditionally followed their own paths and established their own production frameworks. In this article, we present the main frameworks of these two areas and focus on the possibility and need to achieve a better integration between them through the interoperability of systems, processes, and data. The statistical area is well led and has well-defined frameworks. The geospatial area does not have clear leadership and the large number of standards establish a framework that is not always obvious. On the other hand, the lack of a general and common legal framework is also highlighted. Additionally, three examples are offered: the first is the application of the spatial data quality model to the case of statistical data, the second of the application of the statistical process model to the geospatial case, and the third is the use of linked geospatial and statistical data. These examples demonstrate the possibility of transferring experiences/advances from one area to another. In this way, we emphasize the conceptual proximity of these two areas, highlighting synergies, gaps, and potential integration. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

One of the main concerns in adopting citizen science is data quality. Derived products inherit intrinsic limitations of the capture methodology as well as the uncertainties in observations. OpenStreetMap tools are designed to minimize uncertainties in positional accuracy by ensuring a good co-registration of the observations with imagery or direct use of GPS. When thematically annotating objects contributed by citizens, uncertainty increases. During the H2020 GroundTruth 2.0 project two land-cover products derived from OSM were analyzed; one created by the University of Heidelberg (http://osmlanduse.org) and another elaborated by University of Coimbra (https://vgi.uc.pt/vgi/osm/osm2lulc/). To be able to assess the quality of both maps, a third product derived from remote sensing was introduced as a reference map. In GroundTruth 2.0 a tool to show and compare maps as part of the MiraMon Map Browser was developed. The objective was to allow final users to auto-evaluate the quality of their region of interest. The confusion matrix has been used as a method to derive overall commission and omission estimators as well as the Kappa coefficient. Most of the discrepancies between OSM and remote sensing (RS) derived maps are related to different approaches used during data capturing. The data quality tool assesses the quality of individual observations exposed using the OGC standard and describes the quality in an interoperable approach based on QualityML.

The geosciences communities share common challenges related to effective use of the vast and growing amount of data as well as the continueous development of new technology. It is therefore a great potential in learning from the experiences and knowledge aquired across the various fields. The H2020 project NextGEOSS is building a European data hub and platform to support the Earth observation communities with a set of tools and services through the platform. The suite of tools on the platform alllows scalablitly, interoperability and transparency in a flexible way, well suited to serve a multifaceted interdisciplinary community, NextGEOSS is developed with and for multiple communities, the NextGEOSS pilots. This has resulted and continues to provide transfer of experience and knowledge along the whole value chain from data provision to applications and services based on multiple sources of data. We will introduce the NextGEOSS data hub and platform and show some illustrative examples of the exchange of knowledge that facilitates faster uptake of data and advances in use of new technology. An onboarding system is benefitting for existing and new users. A capacity building strategy is an integral part of both the onboarding and the individual services, which will be highligthed in this presentation.

Changes in the rhythm of nature are recognized as a useful proxy for detecting climate change and a very interesting source of data for scientists investigating its effects on the natural ecosystems. In this sense, phenology is the science that observes and studies the phases of the life cycling of living organisms and how the seasonal and interannual variations of climate affect them.Traditionally, farmers or naturalists and scientists recorded phenological observations on paper for decades. Most of these observations correspond to practices today associated to Citizen Science. So far, in-situ observations were reduced to small traditional specimens closely located to the observer home, such as garden plants or fruit trees, butterflies, swallows or storks and, in general, the volunteers efforts were a bit biased towards accessible locations (close to the roads or urban areas). However, the strong variability of the vegetation phenology across biomes requires having more data to improve the knowledge about these changes. Despite its limitations, local, regional or national networks are dedicated to the collection of evidences on changes of vegetation phenology. At sub-national level in Catalonia (north-east of the Iberian Peninsula), the Catalan weather service deployed the FenoCat initiative and in the H2020 Groundtruth 2.0 project, RitmeNatura.cat (www.ritmenatura.cat) was co-designed as a phenological Citizen Observatory that has a community of phenology observers collecting either occasional or regular observations. It monitors 12 species and provides observers with species-phenophase guidance. Fortunately, scientists have found another ally to increase the collection of vegetation phenology data at global level: remote sensing.Remote Sensing (RS) provides several products with different spatial and spectral resolutions. MODIS with a daily revisit is ideal for detecting phenology in vegetation but in many areas of the world, a spatial resolution of 250 m (MODIS) is too coarse to account for small heterogeneous landscapes. In the other extreme high resolution imagery such as Landsat has a limited temporal resolution of only two revisiting periods per month being too low to generate a regular (and dense enough) time series once cloud cover is masked. Sentinel 2A and B with higher resolution, global coverage and 5 days temporal revisiting offer a good compromise. Still, what was obtainable from space differs methodologically from the in-situ observations and both are hardly comparable. The PhenoTandem Project (http://www.ritmenatura.cat/projects/phenotandem/index-eng.htm), part of the CSEOL initiative funded by ESA, provides an innovation consisting in co-designing a new protocol with citizen scientists that will make in-situ observations interoperate with remote sensing products by selecting the areas and habitats where traditional phenological in-situ observations done by volunteers can be also be observed in Sentinel 2 imageryAnd so harmonizing citizens’ science and remote sensing observations promoted through observatories ensures a promising partnership for phenology monitoring.

The first phase of the citizen science Interoperability Experiment organized by the Interoperability Community of Practice in the EU H2020 WeObserve project under the Open Geospatial Consortium (OGC) innovation program and supported by the four H2020 Citizen Observatories projects (SCENT, GROW, LandSense & GroundTruth 2.0) as well as the EU H2020 NEXTGEOSS project has finalized with the release of an Engineering Report in the OGC website. The activity, initiated by the European Space Agency (ESA), EC Joint Research Center (JRC), the Wilson Center, International Institute for Applied Systems Analysis (IIASA) and CREAF wanted to covered aspects of data sharing architectures for citizen science data, data quality, data definitions and user authentication. The final aim is to propose solutions for Citizen Science data to be integrated in the Global Earth Observation System of Systems (GEOSS). The solution is necessarily a combination of technical and networking components, being the first ones the focus of this work. The applications of international geospatial standards in current citizen science and citizen observatory projects to improve interoperability and foster innovation is one of the main tasks in during the experiment to achieve the final aim.The main result was to demonstrate that OGC Sensor Observing Service (SOS) standard can be used for citizen science data (as already proposed in the OGC SWE4CS discussion paper) by implementing it in servers that were combined by visualization clients showing Citizen Science observations from different projects together. The adoption of SOS opened new opportunities for creating interoperable components such as a quality assessment tool. In parallel, an authentication server was used to federate three project observers in a single community. Lessons learned will be used to define an architecture for the H2020 COS4Cloud project. The second phase of the Interoperability Experiment has already started and developments and tests will be conducted by participants in the next 9 months. Some open issues identified and document in the Engineering Report will be addressed in the second phase of the experiment, including the use of a Definitions Server and the adoption of the OGC SensorThings API as an alternative to SOS. The second phase will finalize in September 2020 with a presentation in the Munich OGC Technical Committee meeting. The call for participation and additional contributions will remain for the whole duration of the activity

First approach to metadata was based on producer's point of view, since producers were responsible for documenting and sharing metadata about their products. Since 2012 (started in EU FP7 GeoViQua project), the Geospatial User Feedback approach described the user perspective on datasets/services (GUF, OGC standard in 2016). In the past users of the data gained knowledge about and with the data, but they lacked the means to easily and automatically share this knowledge in a formal way.In the EU H2020 NextGEOSS project, the NiMMbus system has been matured as an interoperable solution to manage and store feedback items following the OGC GUF standard. NiMMbus can be used as a component for any geospatial portal, and, so far, has been integrated in several H2020 project catalogues or portals (NextGEOSS, ECOPotential, GeoEssential and GroundTruth2.0).User feedback metadata complements producer's metadata and adds value to the resource description in a geospatial portal by collecting the knowledge gained by the user while using the data for the purpose originally foreseen by the producer or an innovative one.The current GEOSS platform provide access to endless data resources. But to truly assist decision making, GEOSS wants to add a knowledge base. We believe that the NiMMbus system is a significant NextGEOSS contribution is this direction.This communication describes how to extend the GUF to provide a set of knowledge elements and connect them to the original data creating a network of knowledge. They can be citations (publications and policy briefs), quality indications (QualityML vocabulary and ISO 19157), usage reports (code and analytical processes), etc. The NiMMbus offers tools to create different levels of feedback starting with comments, providing citations or extract quality indicators for the different quality classes (positional, temporal and attribute accuracy, completeness, consistency) and share them to other users as part of the user feedback and usage report. Usage reports in GUF standards can be extended to include code fragments that other users can apply to reproduce a previous usage. For example, in ECOPotential Protected Areas from Space map browser (continues on H2020 e-Shape project) a vegetation index optimum to observe phenological blooms can be encoded by a user in the layer calculation using a combination of original Sentinel-2 bands. The portal stores that in a JavaScript code (serialized as JSON) that describes which layers and formula were used. Once a user validated the new layer, can decide to make it available to everyone by publishing it as an open source JavaScript code in the NiMMbus system. From then on, any other user of the portal can import it and use it. As the usage description is a full feedback item, the user creating the dynamic layer can also describe any other related information such as comments or advertise a related publication.The system moves the focus to sharing user of the data and complements the producers documentation with the richness of the knowledge that user gain in their data driven research. In addition to augment GEOSS data the system enables a social network of knowledge.

This deliverable, produced within Work Package 6 (WP6) Task 6.1, identifies 78 citizen science (CS), citizen observatory (CO) projects, CO portals and other initiatives of interest (past, current and future) that may either contribute to or make use of the Cos4Cloud platform and services. The goal was to identify a group of projects and initiatives that we might productively work with across the duration of Cos4Cloud, not to produce a comprehensive list of projects.

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.

Citizen Science Projects: How to make a difference, is a massive open online course (MOOC). It was developed by the H2020 WeObserve project and ran on the FutureLearn platform from 2019. The course was designed to assist learners from all backgrounds and geographical locations to discover how to build their own citizen science project to address global challenges and create positive change. It also helped learners with interpreting the information they collected and using their findings to educate others about important local and global concerns. The main learning objectives for the course were: * Discover what citizen science and citizen observatories are * Engage with the general process of a citizen science project, the tools used and where they can be accessed * Collect and analyse data on relevant issues such as environmental challenges and disaster management, and discuss the results of their findings * Explore projects happening around the world, what the aims of these projects are and how learners could get involved * Model the steps to create their own citizen science project * Evaluate the potential of citizen science in bringing about change This course also provided five open-source, downloadable tools which have been tested in previous citizen science projects and created for the use of a wider range of projects. These tools are listed below and available in the research repository: * Empathy timeline tool * Community-level indicators tool * Data postcards tool * Future newspaper tool * Co-evaluation tool

European Geosciences Union (EGU) General Assembly 2020, 4-8 May 2020, The contribution of citizen science to addressing societal challenges has long been recognized. The United Nations (UN) Sustainable Development Goals (SDGs), as an overarching policy framework and a roadmap to guide global development efforts until 2030 for achieving a better future for all, could benefit from the potential that citizen science offers. However, there is a lack of knowledge on the value of citizen science, particularly in addressing the data needs for SDG monitoring, among the UN agencies, national statistical offices, policy makers and the citizen science community itself. To address this challenge, we launched a Community of Practice on Citizen Science and the SDGs (SDGs CoP) in November 2018 as part of the EU Horizon 2020 funded WeObserve project. The SDGs CoP brings together citizen science researchers, practitioners, UN custodian agencies, broader data communities and other key actors to develop an understanding on how to demonstrate the value of citizen science for SDG achievement. The initial focus and the main objective of the SDGs CoP has been to conduct a research study to understand the contribution of citizen science to SDG monitoring and implementation. In this talk, we will present the work of the SDGs CoP. We will first discuss existing data gaps and needs for measuring progress on the SDGs, and then provide an overview on the results of a systematic review that we undertook within the CoP, showing where citizen science is already contributing and could contribute data to the SDG framework. We will provide concrete examples of our findings to demonstrate how citizen science data could inform the SDGs. We will also touch on the challenges for and barriers to the uptake of citizen science data for the SDG monitoring processes, and how to bring this source of data into the scope of official statistics

European Geosciences Union (EGU) General Assembly 2020, 4-8 May 2020, Citizen Observatories are becoming a more and more popular source of input data in many scientific domains. This includes for example research on biodiversity (e.g. counts of specific species in an area of interest), air quality monitoring (e.g. low-cost sensor boxes), or traffic flow analysis (e.g. apps collecting floating car data). For the collection of such data, different approaches exist. Besides frameworks providing re-usable software building blocks (e.g. wq framework, Open Data Kit), many projects rely on custom developments. However, these solutions are mainly focused on providing the necessary software components. Further work is necessary to set-up the necessary IT infrastructure. In addition, aspects such as interoperability are usually less considered which often leads to the creation of isolated information silos. In our presentation, we will introduce selected activities of the European H2020 project COS4CLOUD (Co-designed citizen observatories for the EOS-Cloud). Among other objectives, COS4CLOUD aims at providing re-usable services for setting up Citizen Observatories based on the European Open Science (EOS) Cloud. We will especially discuss how it will make use of interoperability standards such as the Sensor Observation Service (SOS), SensorThings API as well as Observations and Measurements (O&M) of the Open Geospatial Consortium (OGC). As a result, COS4CLOUD will not only facilitate the collection of Citizen Observatory data by reducing the work necessary to set-up a corresponding IT infrastructure. It will also support the exchange and integration of Citizen Observatory data between different projects as well as the integration with other authoritative data sources. This shall increase the sustainability of data collection efforts as Citizen Science data may be used as input for many data analysis processes beyond the project that originally collected the data

17 pages, 4 figures, supplementary material https://doi.org/10.1007/s11625-020-00833-7, The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact, Open access funding provided by International Institute for Applied Systems Analysis (IIASA). [...] This research received funding from the EU FP7 ERC CrowdLand project (No. 617754) and the EU Horizon 2020 research and innovation programme under the WeObserve project​ (No. 776740), With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

Auditing Remote Sensing (RS) data can be a tough task due to the huge heterogeneity of RS data and geoprocessing tools available. In this sense, provenance information, part of the metadata that describes "what", "when", "who", "how" and "where" have generated a particular geospatial data, can be very useful. This contribution focuses on how to query provenance information in order to receive back data sources and geoprocessing tools that have participated in the generation of datasets in a catalog. This way, queries over data provenance can be used to infer data quality, trace errors sources, trust and authority of a geospatial information.

The oceans play a key role in global issues such as climate change, food security, and human health. Given their vast dimensions and internal complexity, efficient monitoring and predicting of the planet’s ocean must be a collaborative effort of both regional and global scale. A first and foremost requirement for such collaborative ocean observing is the need to follow well-defined and reproducible methods across activities: from strategies for structuring observing systems, sensor deployment and usage, and the generation of data and information products, to ethical and governance aspects when executing ocean observing. To meet the urgent, planet-wide challenges we face, methods across all aspects of ocean observing should be broadly adopted by the ocean community and, where appropriate, should evolve into “Ocean Best Practices.” While many groups have created best practices, they are scattered across the Web or buried in local repositories and many have yet to be digitized. To reduce this fragmentation, we introduce a new open access, permanent, digital repository of best practices documentation (oceanbestpractices.org) that is part of the Ocean Best Practices System (OBPS). The new OBPS provides an opportunity space for the centralized and coordinated improvement of ocean observing methods. The OBPS repository employs user-friendly software to significantly improve discovery and access to methods. The software includes advanced semantic technologies for search capabilities to enhance repository operations. In addition to the repository, the OBPS also includes a peer reviewed journal research topic, a forum for community discussion and a training activity for use of best practices. Together, these components serve to realize a core objective of the OBPS, which is to enable the ocean community to create superior methods for every activity in ocean observing from research to operations to applications that are agreed upon and broadly adopted across communities. Using selected ocean observing examples, we show how the OBPS supports this objective. This paper lays out a future vision of ocean best practices and how OBPS will contribute to improving ocean observing in the decade to come.

Earth observations data cubes (EODCs) are a paradigm transforming the way users interact with large spatio-temporal Earth observation (EO) data. It enhances connections between data, applications and users facilitating management, access and use of analysis ready data (ARD). The ambition is allowing users to harness big EO data at a minimum cost and effort. This significant interest is illustrated by various implementations that exist. The novelty of the approach results in different innovative solutions and the lack of commonly agreed definition of EODC. Consequently, their interoperability has been recognized as a major challenge for the global change and Earth system science domains. The objective of this paper is preventing EODC from becoming silos of information, to present how interoperability can be enabled using widely-adopted geospatial standards, and to contribute to the debate of enhanced interoperability of EODC. We demonstrate how standards can be used, profiled and enriched to pave the way to increased interoperability of EODC and can help delivering and leveraging the power of EO data building, efficient discovery, access and processing services.