Challenges to Earth Observation use in Official Statistics
As 2030 approaches and planning for the post-2030 SDG framework accelerates national and international data systems are under growing pressure to deliver faster, more transparent, and spatially disaggregated indicators. The demand for timely and location-specific insights is increasing as policymakers seek to measure local progress, identify inequalities, and ensure that no one is left behind. However, several persistent challenges continue to hinder the effective integration of Earth Observation (EO) into national and global SDG monitoring frameworks.
One of the key obstacles lies in data gaps and limited spatial granularity, which make it difficult to capture subnational dynamics and local variations in SDG performance. Many existing indicators are derived from aggregated or modelled data that obscure the realities of marginalised or remote communities, reducing the precision and inclusiveness of policy responses.
A significant disconnect between EO and statistical workflows further compounds this problem. While EO provides rich, objective, and regularly updated information, many datasets remain underutilised because they are not seamlessly embedded within national indicator methodologies or the data structures of statistical systems. This disconnect limits the potential of EO to complement traditional data sources and to enhance the coverage and timeliness of official statistics.
Another barrier stems from a lack of metadata interoperability and transparency. Uncertainty regarding data sources, processing methods, and spatial or temporal coverage often undermines the confidence of National Statistical Institutes (NSIs) in adopting EO-derived products for official reporting. Without clear documentation and standardised metadata, it becomes difficult to ensure the traceability, auditability, and reproducibility required for official SDG statistics.
Moreover, short-term funding and pilot-based approaches have constrained institutional uptake. Many EO-related initiatives operate on project cycles without long-term sustainability plans, resulting in the loss of expertise, tools, and data continuity once funding ends. This hinders the development of durable, institutionalised EO applications within NSI workflows.
Finally, fragmented coordination among ministries, mapping agencies, and statistical offices creates inefficiencies and duplication of effort. The absence of integrated governance frameworks and shared data standards prevents the systematic use of EO across national SDG indicator processes.
How EO can help
EO provides a transformational opportunity to make the next generation of SDG indicators location-based, consistent, and policy-ready.
- Linking statistics to location: EO enables NSIs to disaggregate indicators geographically, ensuring that progress can be tracked across regions, ecosystems, and administrative boundaries.
- Providing continuous and comparable measurements: Long-term, harmonised satellite observations (e.g., from Copernicus and other missions) allow the detection of land use change, degradation, and productivity trends over time.
- Enhancing transparency through metadata: Standardised metadata and provenance tracking make it possible to trace where data came from, how it was processed, and its uncertainty margin—building trust for official reporting.
- Integrating workflows: EO data can be systematically converted into statistical formats (e.g., SDMX tables), aligning with existing reporting standards.
- Supporting policy relevance: By transforming raw EO data into validated, decision-support tools, countries can move from technical exercises to operational, reproducible indicators that inform national planning and sustainable finance mechanisms.
- Strengthening national capacity: Co-designing methods with NSIs, mapping agencies, and universities builds institutional capability and ensures long-term adoption.
Key examples
1) SDG 15.3.1 – Land Degradation Neutrality
EO methods such as Land Cover Change, Land Productivity Dynamics, and Carbon Stock Assessment allow the identification and classification of degraded versus non-degraded land. These methods feed directly into national SDG workflows, producing transparent and auditable statistics aggregated by administrative unit.
2) Portugal’s national integration model
A coordinated system links multiple institutions such as Ministry for Environment and Climate Action (national UNCCD focal point), Direção-Geral do Território (DGT) (custodian of land-cover maps), Instituto da Conservação da Natureza e das Florestas (ICNF) (forest and fire data), Agência Portuguesa do Ambiente (APA) (environmental reporting), and Instituto Nacional de Estatística (INE) (official statistics). This system is supported by universities and technical partners for method development. This collaboration demonstrates how EO can be mainstreamed into official national SDG reporting.
3) EO raster outputs to statistical reporting structures using practical workflows - SDMX
Practical workflows show how EO rasters can be converted into statistical indicators, validated with high-resolution imagery, and aggregated into formats usable by NSOs.
