## 2 Data Challenges and Costs  
Results from the Voluntary National Reviews (VNRs) 2019 [16], a process by which each country assesses progress made in achieving the SDGs every year, showed that most developing countries failed to populate the SDG indicators due to a lack of data. However, even when data are available, countries can encounter other challenges as highlighted in Table 2[17]. Developing countries tend to have relatively weak statistical institutions with poor governance, constraints in obtaining quality data, a lack of time series [11], and often have large variations in data collection and presentation methodologies across space and time [18]. [PERSON] et al. [19] highlighted other issues, such as lack of investment in staff, infrastructure, and tools (e.g., computers and software), low human capacity, highly fragmented statistical systems, and inadequate funding, most of these issues are under political influence; thus, data may even be misreported and suppressed for political reasons [20]. All of these issues can make indicator comparisons unreliable [11; 16; 17; 18; 21; 22] (see Table 2). Regarding EO-derived data, a survey conducted by the Intergovernmental Group on Earth Observations (GEO) [23], with 72 respondents from GEO's member countries, revealed a series of challenges that cover organisational, technical capacity and data accessibility issues for NSOs to obtain the necessary data (Table 2). However, according to the UN Global Working Group on Big Data survey, 60% of interviewees (within 93 countries) noted that the main benefits of using big data from satellite imagery and other sources are \"faster and more timely effective statistics\", \"modernisation of the statistical production process\", generating \"new products and services\", and \"cost reductions\" [24]. There have been challenges regarding the temporal resolution (revisit times) of EO satellites when these data are used for certain sensitive applications; however, in recent years there has  
Figure 1: Paper structure. been a shift from multi-day revisit times to daily revisit times [25], particularly through the establishment of small satellite constellations such as CubeSats, SkySat, and Vivid-I, which are able to close this temporal resolution gap [26]. In addition, cloud coverage, particularly in tropical areas, and satellite sensor failure can result in data gaps; this may limit their applicability. However, algorithms have been developed and are now often easily accessible on online computational platforms and portals (e.g., Google Earth Engine), which can partially fill in such data gaps [27; 28]. Given recent developments in satellite programmes, many have noted that there is a need to invest in geospatial departments in NSOs, ensuring adequate human capacity with relevant skills, equipment, and software and policy frameworks to support the collection and use of EO data; countries also need to be open to reform in their institutional and regulatory context [29]. For example, the Applied Sciences Programme within the National Aeronautics and Space Administration (NASA) Earth Science Division has been implementing free training on the practical use of satellite data, covering the application of EO to disaster management, water quality management, health care (e.g., malaria early warning), fisheries management, air quality monitoring, and wildfires [30; 31]. [PERSON] [32] made one of the first estimations of the financial costs of populating the SDG indicators in the policy paper _'Benefits and Costs of the Data for Development Targets for the Post-2015 Development Agenda'_, later elaborated in [33].