National Statistical Offices (NSOs) need timely, comparable and spatially disaggregated data to produce robust SDG statistics. Under the work done by the European Space Agency, the EO4Society Applications section and their activities “EO engines for SDG target and indicator workflows” turn trusted Earth Observation (EO) methods and datasets into pre‑operational data pipelines that slot into statistical processes, so indicators are faster to compute, easier to validate, and more consistent across time and space.
The 2030 Agenda is a data‑driven framework. The UN Global Indicator Framework and Eurostat’s EU SDG indicator set are opening the door to new data sources where they increase availability, quality, timeliness and disaggregation. Earth observation satellite missions from public and private sources provide long‑term, open, policy‑driven EO with regular global coverage, exactly the characteristics needed to modernise SDG reporting.
How EO can help
Earth Observation gives SDG monitoring a living pulse. Satellites deliver timely, repeatable measurements that build stable, multi-annual time series, even in remote or data-poor regions (e.g., Sentinel-2 five-day revisit), so trends can be tracked as they happen, not years later. Because coverage is wall-to-wall, EO provides spatially disaggregated evidence from national down to municipalities complementing official surveys, closing observation gaps and reducing costs while producing audit-ready maps and tables that slot into existing statistical workflows.
European Space Agency (ESA) worked with institutions responsible for SDG monitoring, such as national statistical offices and custodian agencies, to identify their main needs: timely data, comparable methods, and information broken down by location. Pilot activities then tested different EO methods for selected indicators (e.g. 11.3.1, 6.6.1, 15.3.1) against official reference data, focusing on accuracy, quantified uncertainty, and reproducibility. The methods that passed these tests were scaled across regions and years using traceable, version-controlled code, standardized metadata, and robust QA/QC procedures. They were packaged as easy-to-use “EO Engines”: ready-made processing and analytics tools that plug into EO platforms through interoperability standards and come with clear workflows, documentation and training. Throughout the process, ESA worked with “champion users” through Living Labs to co-design and test the solutions, ensuring that the resulting datasets, dashboards, and pipelines can be directly integrated into institutional SDG workflows.
The next step is their deployment on ESA-supported platforms such as APEx and the Network of Resources (NoR), enabling both routine production and on-demand processing. These cloud-based implementations support FAIR algorithm hosting, performance monitoring, dashboards, and reproducible services designed to last beyond individual projects.
Examples of EO supporting SDG reporting from ESA projects
SDG 1.4.1 – Access to Basic Services
For SDG 1.4.1, the project Urban TEP and its settlement layers (such as the Global Human Settlement Layer (GHSL), World Settlement Footprint (WSF), and Global Urban Footprint (GUF) combined with road networks and Points of Interest) provide proxy measures for access to electricity, transport, and public facilities, revealing underserved areas to support equitable infrastructure planning.
SDG 2.4.1 – Sustainable Agriculture
For SDG 2.4.1, the projects WorldCereal (scale-up), SDG-Rice (Consistent Rice Information for Sustainable Policy (CRISP)), SEN4Rust, NewCAP, and AI Tillage deliver crop-type and crop-season maps, along with crop-health and risk indicators derived from Sentinel satellite data. These datasets enable the detection of agricultural practices and support evidence-based monitoring of sustainable and productive agriculture.
SDG 3.9.1 / 11.6.2 – Air Quality
For SDGs 3.9.1 and 11.6.2, the project CitySatAir integrates data from Sentinel-5P (TROPOMI) with population grids to map exposure proxies and identify urban air pollution hot spots. This EO-based integration supports urban air-quality trend analysis and informs clean-air policy actions.
SDG 6.3.2 – Ambient Water Quality
For SDG 6.3.2, the projects Orbit Guardian (engine) and WorldWater (scale-up) utilise Sentinel-2 and Sentinel-3 OLCI observations to retrieve turbidity and chlorophyll-a proxies. These EO-based indicators enable the monitoring of inland water quality, tracking environmental status and trends in alignment with national and EU standards.
SDG 6.4.1 – Water Use Efficiency
For SDG 6.4.1, the project WorldWater fuses surface-water dynamics with land-cover and productivity proxies from the Copernicus Global Land Service (CGLS) Dry Matter Productivity (DMP) and the ESA Climate Change Initiative (CCI). These EO-derived indicators provide water-use efficiency analytics, complementing national accounts and the Water Exploitation Index Plus (WEI+).
SDG 6.6.1 – Water-Related Ecosystems
For SDG 6.6.1, the project EO4WI – Wetland Extent Inventories employs Sentinel-1 and Sentinel-2 data together with the Global Surface Water Explorer (GSWE) to map lakes, rivers, and wetlands. These EO products support national and subnational roll-ups aligned with the UN Freshwater Ecosystems Explorer framework.
SDG 11.3.1 – Sustainable Urbanization
For SDG 11.3.1, the project Urban TEP SDGs provides a reproducible workflow that combines datasets from GHSL, WSF, GUF, and the Copernicus Land Monitoring Service (CLMS) Imperviousness. These data products generate built-up area change, population density, and SDG 11.3.1 ratio indicators, enabling urban planners to assess sustainable urban growth.
SDG 11.5.2 – Disaster Losses
For SDG 11.5.2, the projects Geohazards TEP and settlement mapping layers overlay event extents—such as floods, earthquakes, and fires—with population and infrastructure data. These EO-based analyses support rapid assessment of damage and service disruption, complementing national disaster loss reporting systems.
SDG 13.1.1 – Disaster Impacts
For SDG 13.1.1, the projects HeatAdapt, IMPALA, and WorldEmission combine hazard exposure analytics (e.g., heat islands, floods) with population distribution data. This EO integration enables estimation of the number of people directly affected by disasters, supporting targeted adaptation and resilience planning.
Coastal Pollution
For SDG 14.1.1a, the projects EU Mon (engine) and BICOME (pathfinder) apply Sentinel-3 OLCI data to map coastal chlorophyll-a anomalies and identify eutrophication hot spots. They also explore floating-debris layers, providing complementary EO-based indicators to inform coastal pollution management.
SDG 15.1.1 – Forest Area
For SDG 15.1.1, the projects SDG-Forest Management, Forestry TEP, and PeopleEcosystem Accounting utilise Sentinel-1 and Sentinel-2 data pipelines to distinguish forest and non-forest areas. These EO time-series products link forest extent with management status and ecosystem accounting, supporting long-term forest monitoring.
SDG 15.3.1 – Land Degradation
For SDG 15.3.1, the projects SEN4LDN and Prisma4Africa deliver EO products following the UNCCD framework, combining land-cover change, productivity trends, and carbon-stock proxies. These integrated datasets support national baselines and local assessments for tracking and mitigating land degradation.
