## 5 Discussion
Despite these developments, some countries are reluctant adopters for several reasons, such as concerns about satellite data \"spying\" and confidentially, or because of a lack of data standardisation and/or because they do not yet have the technical and infrastructure capacity in using EO data [210]. Notwithstanding such reluctance, there are several examples of fruitful collaborations between country NSOs and geospatial agencies, such as the case of Ireland, Sweden, South Africa, Colombia, the Philippines, and the UK that integrated EO and/or geospatial data for indicators that cover SDG 6, 11, 13 and 15. In this paper, the framework developed by [PERSON] et al. [15] was applied to the most recent publications (up to June 2021), updating a similar analysis presented in the earlier paper. Interestingly, rapid progress in terms of the EO data usage for supporting the SDG indicators has occurred in the last 3 years, revealing an additional 30 SDG indicators that can be measured by EO data (mostly through an indirect approach), with 26 of these indicators having socio-economic features. This rapid progress in the most recent years is likely driven by the increased availability of open source data, the collaboration between various organisations, including the commercial sector, academia and GEO and other organisations that continue to provide efforts promoting this type of data at the Statistical Geospatial Integration Forum [211] and other relevant events [212]. However, there remain 139 SDG indicators for which there is currently no EO support. When classifying the SDG indicators based on their sustainability pillars in the present research, we observed that 174 (71%) of the SDG indicators are strongly oriented towards social or economic pillars, which implies some barriers in the use of EO data for indicators in those categories. Likewise, we considered it valuable to classify the SDG indicators with regard to the well-established Driver-Pressure-State-Impact-Response (DPSIR) analysis [213; 214; 215] (a framework that describes the interactions between society, environment, and socio-economic outcomes) as a further perspective on the components of the SDG that can be monitored using EO data. This classification has been performed by [PERSON] et al. [216] and [PERSON] et al. [217], and both found an uneven distribution of the SDG indicators into the five DPSIR components, with a large percentage of response and state indicators (32% and 41% respectively), followed by impact (18%), driving forces (6%) and 3% for pressure indicators. Response indicators cannot be measured directly by EO and can only rarely be assessed via proxy indicators. As an example, consider the response indicator SDG indicator 14.6.1 (degree of implementation of international instruments aiming to combat illegal, unreported and unregulated fishing). This indicator monitors the human action (the response), i.e., whether countries are enforcing international agreements about illegal fishing through management strategies, regulations and policies. EO data can support the overall aims behind this indicator through an indirect approach, as satellites sensors can monitor maritime traffic at night using ship lighting and, thus, implicitly observe fishing activities [136; 218; 219]. When illegal fishing is spotted from space, as [PERSON] et al. [209] have shown in their study, this would reflect poor governance and legislation in those countries (thus this is an indirect observation of the response indicator). Earth observation (EO) could have the highest contributions (based on the wide variety of satellite sensors and capabilities) on the environmental SDG 15 (life on land).