## 4 Assessing EO Contribution to SDGs Indicators Using a Maturity Matrix Framework (MMF) Approach
The latest developments in satellites continue to help to support the more environmental-based SDG indicators. One of the latest SDG indicators introduced at the IAEG-SDG December 2020 update ([PERSON] et al., 2017) is 13.2.2 (total greenhouse gas emissions per year). EO data can help validate or complement this indicator through the wide availability of satellites; however, EO is not currently discussed in the methodological guidelines ([PERSON] et al., 2018). Satellites (e.g., Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), TanSat) have been used to measure atmospheric column-averaged concentrations of the key greenhouse gases CO\({}_{2}\) and CH\({}_{4}\) at the global level. These can be used to complement the current methods, using flux inversions to provide estimates of natural fluxes of CO\({}_{2}\) and CH\({}_{4}\). ESA's Greenhouse Gases Climate Change Initiative ([PERSON] et al., 2018) has created consistent and quality-assured products with rigorous uncertainty analysis based on these satellites, which are available via the Copernicus Climate Data Store ([PERSON] et al., 2018). More recent public (e.g., Sentinel 5P) ([PERSON] et al., 2018; [PERSON] et al., 2018) and private (e.g., GHGSat) ([PERSON] et al., 2018; [PERSON] et al., 2018) missions provide more detailed spatial information and can begin the process of determining emission fluxes (rather than concentrations of mixed gases) for the larger emissions (city air pollution and oil and gas fugitive emissions) ([PERSON] et al., 2018; [PERSON] et al., 2018). Therefore, the current data from these satellites can be translated into actionable information to achieve the goals of COP 26 and SDG 13 (climate action) ([PERSON] et al., 2018). Furthermore, there are new missions in development that will further increase our understanding and direct monitoring of GHG emissions (e.g., Microcarb). An area of current research is the exploration of the potential of other medium and high resolution platforms, not initially designed for atmospheric retrievals, such as Sentinel 2, PRISMA, WorldView-3, to fill the spatial and temporal gaps in mapping methane plumes from point emitters ([PERSON] et al., 2018). Figure 4: EO contributions for SDG indicators against the MMF 2.0 and the latest publications (Authors’ assessment). As can be seen in Supplementary Material Figure S2, 11 indicators that had weak support from EO, have 'upgraded' to providing partial or strong support from EO due to new publications with new EO-based methods. For instance, indicator 7.1.1 (proportion of population with access to electricity) had an MMS of 2.5 (amber) in the original MMF 2.0 dashboard analysis, based on the project India Lights [199]. This project was a collaboration between Development Seed, the World Bank and the University of Michigan that used the DMSP images to extract the light output of individual villages to show those that had access to electricity. However, in this latest review, we increased this indicator's MMS score from 2.5 (amber) to 4 (green) based on a joint project (by Facebook, The Energy Sector Management Assistance Program (ESMAP) at the World Bank, the KTH Royal Institute of Technology, the World Resources Institute (WRI), and the University of Massachusetts Amherst) that developed a predictive model for mapping medium-voltage distribution using night-time satellite data, a MODIS land cover dataset and geospatial products (e.g., different type of roads, railways, political boundaries). The increased score came from the new work on assessing uncertainty (with more validation against ground-truth data) and improved practicability premises (with the method now integrated into the end-user's decision-making in several countries [200] with the possibility to replicate the model (using the model documentation and codes) to other countries [201].