Towards a Global Ground-Based Earth Observatory (GGBEO): Leveraging existing systems and networks

The United Nations’ 2030 Sustainable Development Goals (SDGs) aim to address critical global challenges by promoting economic growth, social inclusion, and environmental sustainability. Earth observation (EO) satellites have become essential tools in advancing these goals, providing high-frequency, extensive data for tracking environmental changes, assessing ecosystem health, and supporting resource management. By analyzing publication trends and employing the remote sensing impact factor, the study reveals substantial growth in EO data applications and highlights key satellites, such as Landsat, Moderate Resolution Imaging Spectroradiometer, and Sentinel, in monitoring climate action (SDG 13), biodiversity conservation (SDG 15), and other SDGs. It also explores the potential of EO data to foster synergies between SDGs by enabling shared data applications across interconnected goals. For example, Sentinel data support both marine ecosystem monitoring (SDG 14) and climate adaptation (SDG 13), while Landsat data contribute to food security (SDG 2) and water resource management (SDG 6). Additionally, the integration of big Earth data cloud platforms, such as Google Earth Engine, has facilitated data processing and analysis, underscoring the importance of open data policies and cross-platform collaboration for advancing SDG research. Despite existing challenges in data standardization, accessibility, and cross-platform compatibility, advancements in artificial intelligence, machine learning, and collaborative frameworks are anticipated to optimize EO data use. This research underscores the essential role of EO satellite data in achieving the SDGs, providing a foundation for integrated, sustainable global development.

Multiple factors contribute to the increase in climate and weather-related hazards such as floods, droughts, landslides, and severe weather events impacting millions of lives and properties. Climate change poses threats to food, water and human security, acting as a catalyst for human induced migrations.  Despite projected intensification of these threats, hydrometeorological and Early Warning Systems, coupled with Early and Anticipatory Actions, prove effective in climate change adaptation, saving lives and reducing losses. The United Nations launched the Early Warnings for All (EW4All) initiative in November 2022 to protect every person on Earth with Early Warning Systems within five years. Water, central to climate action is also the 6th of the 17 Sustainable Development Goals (SDGs) and affects 15 SDGs, shifting the focus and increasing demand for water-related data, which is always complex to access. Despite several negotiated water agreements, access to relevant hydrological data remains a challenge, hindering their successful implementation and monitoring. Effective interoperable data exchange platforms are essential for timely data, understanding challenges, creating visibility of the data providers, improving cooperation at different scales, and demonstrating the return on investment in data collection. Open access and free exchange of valid hydrological data still faces challenges from technological, policy, political, economic, and cultural perspectives. To address these challenges, the World Meteorological Organization (WMO) launched various initiatives promoting access and exchange of Earth Systems data for effective implementation of Climate Change Adaptation Actions and EW4All. These initiatives include, WMO Unified Data Policy, WMO Integrated Global Observing Networks, Global Basic Observing Networks (GBON), WMO Information Systems (WIS2.0), WMO Hydrological Observing System (WHOS), Hydrological Status and Outlook System, and Systematic Observations Financing Facility. WIS 2.0 provides a framework for WMO data sharing, embracing the Earth system approach, enabling the WMO unified data policy, and supporting the GBON. WHOS, the hydrological component of the WIS 2.0, provides a framework for publication, discovery, access, evaluation, and exchange of hydrological data across different scales and providers, addressing complexities of such data through standardization and brokering approaches benefiting multiple uses. Furthermore, it recognizes the multi-dimensional complexities of hydrological data sharing without imposing specific tool or system platform and building on existing systems, and agreements. At the same time enables the hydrological data sharing, regardless of the specific standard or technology used by the data providers or consumers, by applying a brokering architecture where a specific component (the WHOS broker, based on the DAB technology) takes care of mediation and harmonization. WHOS aims to describe where, which, and how hydrological data exists; provide visibility of creators, publishers, and users; address challenges associated with access and sharing across different scales; consistent metadata description and ontology; integrate different data including surface water, groundwater, and water quality; break silos through consistent approach of data sharing among Cryosphere, Hydrology, Weather, Marine and Ocean. WHOS plays a central role in climate change adaptation, EW4All, and Water Resources Management and has proven valuable for flood forecasting, transboundary cooperation, joint monitoring of the data sharing and access in different cases of implementation.      

Timely access to quality data is essential for the understanding of marine processes. The International Oceanographic Data and Information Exchange (IODE) programme, through its distributed network of National Oceanographic Data Centres (NODCs), is developing the Ocean Data Portal (ODP) to facilitate seamless access to oceanographic data and to promote the exchange and dissemination of marine data and services. The ODP provides the range of processes including data discovery, evaluation and access, and delivers a standards-based infrastructure that provides integration of marine data and information across the NODC network. The key principle behind the ODP is its interoperability with existing systems and resources and the IODE is working closely with the Joint WMO-IOC (World Meteorological Organization-International Oceanographic Commission) Technical Commission for Oceanography and Marine Meteorology (JCOMM) to ensure the ODP is interoperable with the WMO Information System (WIS) that will provide access to marine meteorological and oceanographic data and information to serve a number of applications, including climate. The ODP supports the data access requirements of all IOC programmes areas, including GOOS (Global Ocean Observing System), HAB (Harmful Algal Blooms) and the Tsunami warning system as well as JCOMM. The diverse data standards and formats that have evolved within the oceanographic community make data exchange complex and the IODE community has recognized standards are critical in defining how and what data is exchanged. To ensure the interoperability of data exchanged between the NODCs and the ODP, the IODE, together with JCOMM, has initiated a standards process that will support the accreditation and adoption of core standards by the marine meteorological and oceanographic communities. 1. DATA SHARING PRINCIPLES The Earth's oceans form part of an integrated global system and to address global issues, such as climate change, it is essential for scientists to have access to relevant data, information, and products. The and open sharing of datasets is fundamental to ensure the rapid dissemination of data and information is available to researchers. International policies for the and open exchange of scientific data and information are advocated by a number of international organizations. The Intergovernmental Oceanographic Commission (IOC) of UNESCO (United Nations Educational Scientific and Cultural Organization) has adopted a resolution entitled IOC Oceanographic Data Exchange Policy (Resolution IOC-XXII-6). This policy recognizes that the timely, free and unrestricted international exchange of oceanographic data is essential for the efficient acquisition, integration and use of ocean observations. These data are gathered for a wide variety of purposes including the prediction of weather and climate, the operational forecasting of the marine environment, the preservation of life, and the mitigation of human-induced changes in the marine and coastal environment. Under this policy, IOC member states agree to provide timely, free and unrestricted access to all data, associated metadata and products generated under the auspices of IOC programmes. In addition, IOC member states are encouraged to provide free and unrestricted access to relevant data and associated metadata from non-IOC programmes that are essential for application to the preservation of life, beneficial public use and protection of the ocean environment, the forecasting of weather, the operational forecasting of the marine environment, the monitoring and modelling of climate and sustainable development in the marine environment [1]. Other international organizations have also adopted similar policies to encourage the sharing of data. The World Meteorological Organization (WMO) has adopted a policy for the international exchange of meteorological and related data and products. WMO Resolution 40 provides for the free and unrestricted sharing of data [2]. The Group on Earth Observations (GEO), which is coordinating efforts to build a Global Earth Observation System of Systems, or GEOSS, explicitly acknowledges the importance of data sharing in achieving the GEOSS vision and anticipated societal benefits. GEO is developing a set of high level Data Sharing Principles which call for the full and open exchange of data, metadata, and products shared within GEOSS, recognizing relevant international instruments and national policies and legislation. These Principles also note that All shared data, metadata, and products will be made available with minimum time delay and at

No pixel left behind: Toward integrating Earth Observations for agriculture into the United Nations Sustainable Development Goals framework

The integration of data science and Artificial Intelligence (AI) into geospatial analysis has revolutionized Earth observation, driving progress towards the Sustainable Development Goals (SDGs). Recent developments in data acquisition technologies like high-resolution satellites and sensors have generated vast and diverse datasets for monitoring environmental changes and managing natural resources. Concurrently, innovations in Machine Learning (ML) and AI have significantly enhanced the processing, analysis and interpretation of this geospatial data. Techniques such as deep learning, spatial data mining and automated feature extraction are now essential to deriving actionable insights from complex geospatial datasets. This paper reviews the latest trends and breakthroughs in the application of AI/ML to geospatial data for Earth observation, emphasizing their role in advancing the SDGs. Key areas of focus include improved algorithms for land cover classification, disaster prediction and climate monitoring. These technologies enable more precise and timely responses to environmental challenges, such as deforestation, urbanization and natural disasters, thereby supporting sustainable management and policymaking. Furthermore, the integration of AI with geospatial data enhances predictive modelling, scenario planning and decision support systems, which are critical for achieving SDG targets related to environmental sustainability and resilience. The synthesis of recent research and technological developments highlights the potential of AI/ML approaches for geospatial analysis and their alignment with global sustainability goals. The outcomes underline the requirement for continued innovation and collaboration across disciplines to fully leverage these advancements for effective Earth observation and sustainable development.

PurposeRecent technological developments have encouraged the United Nations to promote the adoption of digital technologies to achieve the Sustainable Development Goals (SDGs). In addition to initiatives from businesses, an increasing number of studies indicate that public service agencies may gain benefits from adopting digital transformation. On a global scale, policymakers are examining the integration of digital technologies, specifically artificial intelligence (AI), into public service delivery (PSD), acknowledging the potential advantages and obstacles for the public sector. Therefore, the objective of this study is to investigate the impact of AI on PSD to support the SDGs initiative.Design/methodology/approachThe research used a qualitative approach to explore the intersection of AI, SDGs and PSD. This approach involved scrutinising relevant publications and conducting an extensive literature review. The research also used bibliographic analysis to discern patterns within the field. Findings from the literature review and bibliographic analysis contributed to identifying research trends that explore the complex relationship among AI, PSD and the SDGs. The model derived from this comprehensive review and analysis elucidates the potential of AI to enhance PSD and contribute to the achievement of the SDGs.FindingsThe bibliographic study revealed significant research trends concerning AI, PSD and SDGs through an empirical investigation of an extensive array of peer-reviewed articles. This investigation focused on how the public sector can improve its delivery of services to citizens and all stakeholders to advance the SDGs. AI holds the promise of revolutionising PSD and bolstering the SDGs. By leveraging AI’s capabilities in data analysis, automation and customisation, governments can enhance the efficiency, effectiveness and accessibility of public services. This, in turn, enables public servants to tackle more complex tasks while providing citizens with personalised and relevant experiences. Additionally, the study advocates modelling the intersection of PSD and AI to achieve sustainable development.Research limitations/implicationsThe employed research methodologies, such as literature reviews and bibliographic analysis, enrich the context of AI, SDGs and PSD. They offer a comprehensive perspective, identify knowledge gaps and furnish policymakers, practitioners and academics with a conceptual framework for informed decision-making and sustainable development endeavours.Originality/valueThe study provides an agenda for AI and SDGs research on application in PSD. It emphasises varied research viewpoints, methods and gaps. This study helps researchers as well as practitioners identify subtopics, intersecting themes and new research pathways.

One of the great challenges of achieving the shared vision of the 2030 Agenda for Sustainable Development is having high-quality, timely, comparable, and accessible data that allows to measure and report progress on the Sustainable Development Goals (SDG). Hence, in many countries, geospatial information (including Earth observation) and algorithms implemented in cloud computing platforms have become important tools to monitor indicators of the SDG thanks to their broad accessibility and global coverage. However, emerging countries still face barriers to the implementation of technologies to manage the large amounts of EO data. This article aims to show the advantages of satellite-based EO in the measurement of SDG indicators, as well as challenges emerging countries face in the use of these technological tools. It addresses why the open-source tool Open Data Cube (ODC) should be seen as a response to the said challenges. Finally, there is a description regarding the experience of Mexico with the use and application of this tool for the measurement of SDG indicators, from the development and implementation of the Mexican Geospatial Data Cube (MGDC) to the results obtained from its application in the support for the measurement of SDG indicators 6.6.1 Change in the extent of water-related ecosystems over time and 15.1.1 Forest area as a proportion of total land area.

<p>The World Meteorological Organization (WMO) supports the National Meteorological and Hydrological Services in their mission to deliver operational hydrology services for achieving water security and the water-dependent/water-related Sustainable Development Goals. Operational hydrology is defined as “the real time and regular measurement, collection, processing, archiving and distribution of hydrological, hydrometeorological and cryospheric data, and the generation of analyses, models, forecasts and warnings which inform water resources management and support water-related decisions, across a spectrum of temporal and spatial scales”. The WMO ‘Vision and Strategy for Hydrology and its associated Plan of Action*’, approved by the Extraordinary Congress in October 2021, identifies eight long-term ambitions for operational hydrology in support of the global water agenda: (1) No one is surprised by a flood, (2) Everyone is prepared for drought, (3) Hydro-climate and meteorological data support the food security agenda, (4) High-quality data supports science, (5) Science provides a sound basis for operational hydrology, (6) We have a thorough knowledge of the water resources of our world, (7) Sustainable development is supported by hydrological information, and (8) Water quality is known. The WMO initiatives aim at improving operational hydrology applications by communicating the needs and benefits of hydrological research in support of operational hydrology, and enabling new research partnerships and collaborations with academia and practice communities. In this presentation, we focus on science priorities and knowledge gaps necessary to improve the delivery and the use of hydrologic data, information, and services in operational hydrology. We discuss the WMO Hydrological Research Strategy and how we can strengthen Hydrology/Water topics under the umbrella of the WMO Research Board. We will also report on the main achievements of an expert team, brought together at the end of 2021 to identify complementary and new research areas to strengthen the linkages between water, weather, climate and environment within the existing WMO related programmes, including the Global Atmosphere Watch (GAW) Programme, the World Climate Research Programme (WCRP), and the World Weather Research Programme (WWRP).</p><p>* The process was led by the WMO Research Board (RB) with inputs from the WMO Hydrological Coordination Panel (HCP), the International Association of Hydrological Sciences (IAHS), and the Intergovernmental Hydrological Programme (UNESCO-IHP)</p>

Achieving the Sustainable Development Goals Requires Transdisciplinary Innovation at the Local Scale

The speed of environmental changes, the increase in the carbon dioxide (CO2) concentration in the atmosphere and its abnormal warming, the greenhouse effect, and the increase in desertified areas are linked to climate change. Trend tables indicate that global changes are worsened by changes in temperature and rainfall resulting from climate change, with a strong anthropogenetic influence. Reports from the United Nations/UN, the IPCC (Intergovernmental Panel on Climate Change), and the World Meteorological Organization (WMO) indicate that the world has already suffered a 1.0 °C global warming above the pre-industrial levels, with a variation between 0.8 and 1.2 °C. Drylands and the poorest countries would be the most affected by these transformations, which involve aggravation of ecological, social, and economic problems. The desertification would spread due to this situation. Among the effects of global climate change, desertification is one of the most complex and harmful. It involves several factors and causes affecting natural, rural, and urban areas, posing major challenges for governments, civil society, the private sector, and future generations. The discussion on the Sustainable Development Goals (SDGs), especially Goal 13 (urgent measures to combat climate change and its impacts), tries to mitigate these issues. However, the mechanisms linked to climate change are still elements of skepticism for scientific denialists.

This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in advancing threat detection and mitigating cybersecurity risks, while concurrently highlighting their application in public health optimization to enhance healthcare outcomes in underserved communities. The study underscores the dual capability of AI-driven frameworks to address critical challenges across cybersecurity and public health, aligning with sustainable development goals (SDGs). In cybersecurity, the research identifies AI and ML as pivotal in real-time threat detection, anomaly analysis, and predictive risk mitigation. Key findings demonstrate how advanced algorithms, such as deep learning and reinforcement learning models, can anticipate and neutralize cyber threats with unparalleled precision, minimizing vulnerabilities in digital ecosystems. Concurrently, the paper examines the adaptation of AI-driven methodologies in public health optimization. By leveraging predictive analytics and resource allocation algorithms, AI frameworks are shown to improve access to healthcare, enhance disease prevention strategies, and optimize patient outcomes in resource-limited settings. The integration of these technologies fosters equity, reduces disparities, and contributes to achieving SDGs related to health and well-being. The study concludes by emphasizing the interdisciplinary application of AI and ML as a cornerstone for innovation. Recommendations include strategic investments in AI infrastructure, cross-sectoral collaborations, and ethical guidelines to ensure the responsible and sustainable deployment of these technologies. Through this integrated approach, the research establishes a roadmap for leveraging AI and ML to address global challenges, driving progress in both cybersecurity and public health sectors.

This paper reviews the key role that Earth Observations (EO) play in achieving the Sustainable Development Goals (SDGs) as articulated in the 2030 Agenda document and in monitoring, measuring, and reporting on progress towards the associated targets. This paper also highlights how the Group on Earth Observations (GEO) would contribute to ensure the actual use of EO in support of the 2030 Agenda; and how the Global Earth Observations System of Systems meets requirements for efficient investments in science and technology and a good return on investment, which is elaborated in the Addis Ababa Action Agenda on development financing. Through a number of examples, we first discuss how extensive EO use would: provide a substantial contribution to the achievements of the SDGs by enabling informed decision-making and by allowing monitoring of the expected results; improve national statistics for greater accuracy, by ensuring that the data are “spatially-explicit” and directly contribute to calculate the agreed SDG Targets and Indicators support the fostering of synergy between the SDGs and multilateral environmental agreements by addressing cross-cutting themes such as climate and energy; and facilitate countries’ approaches for working across different development sectors, which is, according to the special adviser on the 2030 Agenda, a key challenge to achieve the SDGs. We then focus on the role that GEO could play in enabling actual use of EO in support of the 2030 Agenda by directly addressing the Strategic Development Goal 17 on partnerships.

This paper examines artificial intelligence (AI) and sustainable development in China. The Chinese government has developed ambitious policies for global leadership in the field of AI and sustainable development. While China has made progress in several areas of Sustainable Development Goals (SDGs), it is lagging behind in achieving the SDGs overall, and AI technologies can bolster its progress toward SDGs. Using the latest data of 193 countries around the world, the paper also analyzes the implication of AI on sustainable development both at the global and regional levels. Broadly, a strong positive relationship between the government AI readiness and progress toward SDGs is observed. When classifying the SDGs into four dimensions including economy, society, environment, and partnerships, government AI readiness is found to have a strong relationship with economy followed by the society dimension, whereas there are no clear relationships with the environment and partnerships dimensions. To fully harness and scale the power of AI to meet the SDGs, the Chinese policy maker should align the potential AI technologies to address its SDGs gap, and identify the priority or targeted areas and design appropriate business models and incentive structures for scaling viable solutions. AI technologies, when implemented strategically and properly, can accelerate China’s progress toward SDGs.

Universities, such as the University of Helsinki, are facing a growing trend to redefine their strategies and organisations along the lines of sustainability. However, the process of building the structures for sustainability research and education requires the breaking down of existing disciplinary silos. In this chapter, we analyse the new initiatives in research, education and governance, and management operations to which the University committed during 2015–2018 through the lens of Sustainable Development Goals (SDGs). We also explore the factors that enable or hinder sustainability transition at a university. The results of the SDG mapping show that SDG 4 (Quality Education) is an overarching goal represented in all new initiatives within research, education and university management. SDG 17 (Partnerships) and SDG 3 (Health and Wellbeing) are also equally strongly emphasised. However, SDGs 1 (No Poverty), SDG 6 (Clean Water and Sanitation) and SDG 5 (Gender Equality) are not considered, or if so, given little emphasis. Our analysis revealed that small niche innovations, tactical and operational activities at the grassroots level like networks, science activism and student awareness pushed for regime-level changes. However, the financial incentives and policy changes initiated on the regime level enabled the niche-level innovations to develop and led to strategic decisions providing a window of opportunity to initiate structural changes.

The Sustainable Development Goals (SDG) framework aims to end poverty, improve health and education, reduce inequality, design sustainable cities, support economic growth, tackle climate change and leave no one behind. To monitor and report the progress on the 231 unique SDGs indicators in all signatory countries, data play a key role. Here, we reviewed the data challenges and costs associated with obtaining traditional data and satellite data (particularly for developing countries), emphasizing the benefits of using satellite data, alongside their portal and platforms in data access. We then assessed, under the maturity matrix framework (MMF 2.0), the current potential of satellite data applications on the SDG indicators that were classified into the sustainability pillars. Despite the SDG framework having more focus on socio-economic aspects of sustainability, there has been a rapidly growing literature in the last few years giving practical examples in using earth observation (EO) to monitor both environmental and socio-economic SDG indicators; there is a potential to populate 108 indicators by using EO data. EO also has a wider potential to support the SDGs beyond the existing indicators.