## 5 Discussion  
The SDG framework, with its targets and indicators, stands as the most comprehensive, universal, and ambitious plan to end poverty, promote prosperity and people's well-being and protect the environment, and has been agreed to by 193 world leaders spanning the developed and developing worlds. The achievement of the SDGs relies prominently on the availability and use of relevant, reliable, and timely data to measure progress, inform policies and target those areas that need improvement. Traditional data sources have administrative and technical limitations, in terms of data access, standardisation and quality, lack of awareness concerning the benefits of current technologies, lack of financial resources, technology and skills gaps, geographical constraints (in terms of developing space projects), and coverage gaps across space and time [202]. Indeed, surveys suggest that 73% of countries require assistance to upgrade existing data sources [203]. Thus, the world is currently not on track in terms of being able to populate the SDG indicators; only 44% of the SDG indicators have sufficient data for global monitoring. However, the advent of new EO technologies, either by themselves or combined with traditional data sources and methods, offers the opportunity to provide robust and timely data on a routine basis for monitoring and decision-making that supports the achievement of the SDGs. Given a large number of satellites/instruments and their capabilities (spatial, spectral and temporal resolution) (Table S1), free and open access portals (Table S2), data cubes (Table S3), and EO thematic platforms (Table S4), there is potential to monitor every place on Earth. EO data can help save time and cost [204; 205] compared to the generally more labour-intensive traditional approaches. There are some common misconceptions related to the economic cost of VHR [206; 207]; performing cost-effectiveness analysis can help identify the most effective and competitive data [208; 209]. For instance, [PERSON] et al. [51] examined the role of using satellite data to assess the household wealth by considering the spatial landscape use, size of the building, agricultural productivity, land use and cover surrounding the household as predictor variables. They also estimated the price for traditional and EO analyses. Surveying 330 households in rural areas of western Kenya would cost USD 106,500 per year, compared with USD 1750 to USD 5000 per year for 100 km\({}^{2}\) (covering the same 330 households) using VHR satellite data. They demonstrated that satellite data can predict household wealth with 62% accuracy compared with the traditional sampling methods and suggest that EO data be used to understand the dynamics of changes in wealth between less frequent household surveys. Nevertheless, there are capacity issues within NSOs that need to be addressed to enable access to and use of EO data. These issues are not only about providing training to NSO staff, but also about the strategic direction in the political agreement process and cooperation between geospatial and EO communities, organisations, ministries, and stakeholders. Likewise, there is a need for investment in the official statistics community in terms of EO data manipulation,interpretation, and digitalisation. This issue can be resolved by increasing technical capacity and infrastructure, but also via regulations designed to build and consolidate trust among different stakeholders involved in data sharing. Since 2015, GEO and its initiative EO4 SDG, custodian agencies and academia increased their efforts to explore the potential of EO and geospatial data to support SDG indicators and targets; thus, in the last couple of years, there have been several reviews that assess the EO contributions for SDG indicators under various frameworks and analyses alongside practical guidance [14; 15; 27; 35; 36; 90; 154; 155; 156; 157; 158].