In September and October 2022, the Group on Earth Observations (GEO) Data Working Group implemented a dialogue series to raise awareness about the GEO Data Sharing and Data 

Management Principles and their benefits with special attention to usability and legal aspects of in-situ and remote sensing Earth Observation data. The intended dialogues’ audience included all Earth observation stakeholders, including data producers, technology providers, scientists, researchers, business developers, decision-makers, and policymakers. Each session covered the theory of the core topic and provided examples of existing implementations.  Leaders and experts presented, via short lightning talks, success stories related to in situ and remote sensing data, the GEO Data Sharing and Data Management Principles, and the FAIR, TRUST, and CARE principles. The diversity of the success stories related to different thematic domains and data types, fostering cross-innovation, and highlighting proven solutions - all serving to introduce discussions. Members of the Earth observation community shared their experiences in implementing the principles, discussed how they tackled challenges and described impacts. The covered topics addressed the data life cycle, the GEO Data Sharing Principles, and the GEO Data Management Principles; elements of Discoverability, Accessibility, Usability (Encoding, Documentation, Provenance, Quality Control), Preservation (Preservation, Verification); Curation (Review and processing, Identifiers); as well as the Data Management Self-Assessment Tool. Standards are instrumental for implementing data management best practices. Broadly sharing a common baseline of understanding and references, the dialogues support community engagement and interoperability. The dialogue series contributes to capacity development on data management in general. All materials, including recordings and presentations, are available on the GEO Knowledge Hub, Youtube, and are open to sharing on other community portals. This communication will present examples of implementations of the GEO DMP extracted from the dialog series as well as new dialogues that will be developed in 2023, with special attention to in-situ data challenges and integration with other data in global solutions.

How to cite: Voidrot, M.-F., Lijla Bye, B., de Salvo, P., Franziskakis, F., Benedict, K., Maso, J., Schubert, C., Rubio, J. M., and Downs, R. R.: New Resources promoting the GEO Data Sharing and Management, FAIR, and CARE principles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11480, https://doi.org/10.5194/egusphere-egu23-11480, 2023.

It is becoming increasingly obvious that in order to address current global challenges and achieve the SDGs in the land-use sector, monitoring and evaluation using remote sensing technologies are essential. In particular, with the Copernicus program of the European Union, unprecedented free and open Earth observation data are becoming available. However, in order to improve our remotely sensed based machine learning models, training data in the form of in-situ or annotated land-use or land cover data which are based on the visual interpretation of aerial photographs or very high resolution satellite data are of utmost importance. Without sufficient training data, many land-use and land cover maps lack sufficient quality.

The presentation will provide an overview of existing and open in-situ data in the field of land-use science. It will highlight what land-use data are currently available including data collected though crowdsourcing and the Geo-Wiki toolbox. In particular, it will provide insights into current gaps in land cover, land-use, livestock, forest as well as crop type information globally. It will draw on existing global data products such as those from the Copernicus global land monitoring service, and more recently generated products such as WorldCover and WorldCereal. Furthermore, tools to close those data gaps will be shown. The presentation will furthermore explore current obstacles and limitations to data sharing and debunk current arguments that are often put forth for not sharing in-situ data. These arguments include limited resources, quality issues, competition, as well as time constraints, etc. Specific attention will be given to the role of doners and funders in more clearly defining open and FAIR requirements for in-situ data. The presentation will close by making the audience aware of the LUCKINet consortium, which is trying to make more reference data openly accessible and to build a consistent global land-use change dataset as well as work done on in-situ data within the EU LAMASUS and OEMC project.

How to cite: Fritz, S., Barrasso, C., Ehrmann, S., Lesiv, M., McCallum, I., Meyer, C., Laso Bayas, J., and See, L.: Opening up FAIR in-situ land-use reference data: current gaps, obstacles and future challenges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16128, https://doi.org/10.5194/egusphere-egu23-16128, 2023.

The in-situ Earth Observation data segment is fragmented and there are significant data gaps to complete an observing system offering global datasets including series with relevant temporal depth. To identify the most urgent user needs, the InCASE project has designed a geospatial in-situ requirements database model, called G‑Reqs, aimed to collect and manage requirements emerging from the Group on Earth Observations (GEO) and the Copernicus community. The expected benefits include enabling a better reuse of in-situ data, enabling geographical upscale, identifying priorities in the needs and identifying communities with a common interest to look for synergies. Starting from the Essential Variables framework, the model offers a user-centric approach based on the expression of data needs and its translation into parametrized requirements for in-situ data. A first implementation was done in a web form and was tested by the EuroGEO community represented by volunteering pilots of the EU H2020 e-shape project, and it is open to research projects, decision-makers looking for policy indicators, remote sensing agencies in need of cal/val data, services produced by commercial companies, Earth system predictive algorithms and Machine Learning modellers, etc., interested in environmental in-situ data. The usefulness of the G-reqs model will lie in its capability to collect, share and analyse requirements, detect essential datasets, gaps, and help to make recommendations to data providers via a consensus process thus promoting the discovery of fit-for-purpose in-situ datasets. The consensus process can result in agreement on recommendations to data providers for producing products that cover emerging needs of the Earth Observation users’ community in terms of spatial, temporal coverage or quality target. In this context, the entire Earth Observation community of users is invited to use the G-reqs as a mechanism to document its in-situ data needs (https://g-reqs.grumets.cat). For example the in-situ networks of observation facilities (ENVRI, e.g. ELTER, GEOBON, among others) can then participate in the analysis, gap detection and recommendations for the creation of new products or modifications of the existing ones to better serve their users. With the G‑reqs as a tool, the In-Situ Data Working Group in GEO can act as a forum where the in-situ data barriers and gaps are discussed and addressed. This communication will present the requirements data model and the current status of the requirements collection as well as next steps to complete the G‑reqs capabilities. This work is inspired by the OSAAP (formerly NOSA) from NOAA, the World Meteorological Organization (WMO) OSCAR requirements database and the Copernicus In-Situ Component Information System (CIS²). The InCASE project is funded by the European Environment Agency (EEA) in the context of the EEA SLA on “Mainstreaming GEOSS Data Sharing and Management Principles in support of Europe’s Environment" in line with the European Strategy for Data, the Green Deal Data Space, and Destination Earth.

How to cite: Brobia, A., Maso, J., Voidrot, M.-F., Serral, I., Zabala, A., and Rubio, J. M.: G-reqs: How a user requirements system in GEO can improve the in-situ data availability?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6359, https://doi.org/10.5194/egusphere-egu23-6359, 2023.

Agricultural monitoring has been an important topic in remote sensing research since the inception of satellite Earth observations. With early national-scale crop yield estimation efforts dating back to the LACIE and AgriSTARS projects of the 1970s and 1980s, the value and importance of food production, combined with the high variability of crop genotypes and phenotypes, have continued to spur constant innovation in mapping and monitoring agricultural land from remote sensing data.

Cropland is a highly dynamic surface type that can be difficult to map with high precision and accuracy for myriad reasons, including: variability in crop type and crop rotations; intra-season growth cycles; multi-cropping practices; crop health; fallow land; farming practices; soil fertility; seed genetics; environmental factors; and more. Each of these variables also changes through time due to climate change, advancing technology, and changes in socio-economic or political drivers. Mapping and modeling agricultural variables, therefore, require constant recalibration and validation against in-situ observations that are representative of the cropping regime.

The Essential Agricultural Variables (EAVs), defined by the GEO Global Agricultural Monitoring (GEOGLAM) initiative, provide a basis for identifying the data variables and their functional requirements in terms of spatial and temporal resolution. EAVs are designed to help the GEOGLAM community prioritize the development of products that can be derived from Earth observations data to improve downstream insight into agricultural productivity. At the same time, the EAVs are instructive for developing methods and tools for collecting the in-situ data needed to evaluate the corresponding products.

Operating under the GEOGLAM Data Lifecycle, the EAVs, and the GEO data sharing and management principles, the NASA Harvest consortium on food security and agriculture collects and distributes thousands of in-situ observations for public use in the agricultureal R&D domain. These efforts are underpinned by freely accessible data collection platforms, searchable data discovery and distribution portals, and purpose-driven field measurement methodologies that balance project-specific requirements while ensuring the future reusability of the dataset.

In this presentation, we highlight the status of current in-situ datasets and tools available through NASA Harvest. Their relevance is contextualized within the GEOGLAM EAV framework, and we discuss practical issues of in-situ data collection for agricultural remote sensing applications including farmer data privacy, reducing enumerator errors, coordinating data collection campaigns, limitations of reusing data, and balancing measurement complexity with general utility. 

How to cite: Humber, M., Justice, C., Munshell, B., Mitkish, M., Whitcraft, A., Jarvis, I., Franziskakis, F., Makabe, E., and Becker-Reshef, I.: NASA Harvest Platforms for Collecting and Sharing In-situ Observations of Essential Agricultural Variables, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9941, https://doi.org/10.5194/egusphere-egu23-9941, 2023.

Multi-variate in situ observations from the Earth’s extensive mountain regions are crucial for a plethora of important applications, such as ground truthing remotely sensed data and downscaling climate model outputs. However, as a result of their inhospitable conditions and remoteness, mountains are extremely challenging environments in which to conduct systematic, long-term, and spatially dense in situ monitoring of bio-physical processes, leading to various real or perceived deficiencies in mountain data coverage (or “data gaps”). Gaining a thorough appreciation of these coverage deficiencies is complicated by the very heterogenous nature of the in situ mountain monitoring “landscape”; many different institutions and initiatives, often research-orientated as opposed to operational, employ a high diversity of techniques for a range of different applications. As such, it is currently extremely challenging for stakeholders to efficiently obtain an overview of who is measuring what, where, when, how, and why in a given region of interest. Information on the coverage of data beyond core climatic variables such as air temperature or precipitation is especially lacking. In this context, we present the GEO Mountains In Situ Inventory of Observational Infrastructure. The latest version (v2) contains key metadata for over 51,000 mountain monitoring stations, station networks, experimental basins, or other monitoring locations (e.g. repeated vegetation monitoring sites) across the world’s mountains. It can be viewed using a web mapping application, with the underlying table also available for direct download. Via a system that enables individuals or institutions to propose additions and improvements, we intend to continue developing the inventory in an iterative, community-based fashion. Overall, this effort should expedite access to the corresponding observations (e.g., time-series), reduce infrastructural redundancy, and improve interdisciplinary collaboration around existing sites. Once further expanded, the inventory may also facilitate more extensive and thematically broad data coverage analyses than those hitherto possible, which in turn could inform monitoring infrastructure installation and maintenance investment decisions. In conclusion, we will reflect on potential links between the inventory and a recently proposed set of Essential Mountain Climate Variables.

GEO Mountains (2022). Inventory of in situ mountain observational infrastructure, v2.0. https://www.geomountains.org/resources/resources-surveys/inventory-of-in-situ-observational-infrastructure. doi: 10.6084/m9.figshare.14899845.v2

How to cite: Thornton, J., Palazzi, E., and Adler, C.: A global, cross-disciplinary inventory of mountain monitoring infrastructure, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1970, https://doi.org/10.5194/egusphere-egu23-1970, 2023.

Although it is now known that deep marine processes play a crucial role in the study and assessment of climate variability, deep-sea ocean dynamics remain largely unknown. This is due to the lack of data above 2000 m of depth. 

Given the paucity of observation of the deep ocean environment, is essential to improve the availability and accessibility of in-situ high-quality datasets, standardized in accordance with the FAIR principles. This allows enhanced knowledge of local deep variability, and contributes to maximizing the utility of data, also supporting ocean modeling which so far has been unable to realistically depict the deep-layers state. 

Several high sampling frequency dataset collected by benthic multidisciplinary observatories in key sites such as Mediterranean Sea, Marmara Sea and Atlantic Ocean has been elaborated, verifying the sensor efficiency through post-calibration and standardized Quality Control (QC) procedures, adapted from international protocols and recommendations specifically for deep-sea observations. QC tests were automated as much as possible aiming for a standardized procedure while taking into account the specificity of each different technology used.

The aim of this work is to disseminate verified in-situ dataset, together with properly formatted raw data and metadata, providing high-quality open-access data including Ocean Essential Variables  ready for user analysis intended to fill the gap in deep ocean knowledge.

How to cite: Lo Bue, N., Giambenedetti, B., Embriaco, D., Marinaro, G., Bagiacchi, P., and Vagni, R.: A new release of deep ocean datasets for a better understanding of ocean dynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16889, https://doi.org/10.5194/egusphere-egu23-16889, 2023.

Of the four types of ecosystem services, Cultural Ecosystem Services (CES) is more difficult and underresearched than the others. Because people interact with nature in complex and diverse ways, CES cannot be explained as a direct benefit to people. Even at the national level, evaluation items and indicators for CES are not clear and common, so it is necessary to use data for a detailed review of indicators. This study aims to explore ways to apply quantitative data to CES assessments, thereby facilitating CES evaluations in national ecosystem service evaluations. We decided that, as one of these solutions, we could use the statistical data that the state had built. Among the data available nationally, national statistics are the most uniform and systematic. As for Korea's national statistics, a total of 191,215 kinds of statistics are open (as of June 2022) from 30 topics such as population and environment, central administrative agencies, local governments, financial institutions, public corporations and industrial complexes, research institutes, associations, and cooperatives. Only statistical data that were similar to the indicators of dual ecosystem service evaluation were extracted. This was then condensed into an indicator similar to CES. At this time, the difference between the CES indicator and other service indicators was based on 1) whether the CES indicator was covered in previous research, 2) whether it could act as an inclusive service such as local history, culture, religion, etc., and 3) whether the service benefits depended on human will. As a result, it was condensed into 100 statistics. Indicators of assessment found statistical data to assess landscape, health and healing, leisure recreation, and heritage, and no statistical data to assess educational value. Through this study, it is possible to quantify the evaluation method of cultural services, which is the most difficult to scientifically and quantitatively evaluate among ecosystem services. Through big data analysis, the evaluation status of international and national units can be checked, and the utilization of national statistical portals can be increased as objectified evaluation data. In addition, national statistical construction can be added with the information needed to achieve the country's comprehensive environmental plan and biodiversity goals.

How to cite: Seo, J. and Park, C.: Quantification of evaluation methods of cultural ecosystem services using Statistical Database, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17465, https://doi.org/10.5194/egusphere-egu23-17465, 2023.