## 3 Potential of Earth Observation (EO) for Supporting SDG Indicators
EO processing and visualisation platforms provide images that have passed through several 'levels' of processing, integration, and analysis of raw EO data. Corrections at these levels include radiometric calibration, atmospheric correction, and the derivation of products from the satellite data (such as land classification maps); these EO-based products are often further processed and combined with other geo-referenced socio-demographic, economic and environmental data. The new cloud-based infrastructure services are becoming more important as they enable users to access, store, and analyse large volumes of EO data without having to download (and in some cases process) the raw data [53; 81]. These cloud platforms provide 'analysis ready data' (ARD) which are normally geometric, orthorectified and radiometrically calibrated [24]. They can include integrated technologies such as application programming interfaces (API) and web services to provide a more complete solution for big EO data management and analysis. Currently, there are around 14 major platforms that include big EO data management and analysis which offer mainly free data access with commercial infrastructure, but use different storage systems, and access interfaces of large EO data sets (listed in Supplementary Material Table S3) [53]. Among these platforms, Google Earth Engine (GEE) combines a multi-petabyte catalogue of satellite imagery and geospatial datasets with planetary-scale analytical capabilities and is available for scientists, researchers and developers to detect changes, map trends, and to quantify differences on the Earth's surface [28]. However, one barrier to the use of such platforms is the need for computing skills and facilities to download and manipulate large datasets. In terms of EO-derived thematic products and services, ESA, NASA, and other partners have created dedicated portals with advanced visualisation tools and services where users can directly access spectral indices such as NDVI, as well as products derived using advanced and complex algorithms that assess land degradation, land cover change, water quality, etc. These free-to-the-user higher-level products require less expertise to manipulate the EO data and generate products but are more susceptible to data misinterpretation as a result of accessibility with a lower expertise level. These products provide quantitative data to develop a baseline, assess trends, and address SDG indicators (a selected list of these thematic products is presented in Table S4) [82; 83; 84; 85; 86; 87; 88]. Such portals and platforms integrated with big data provide several benefits, as they provide a consistent, standardised product that has been developed by experts, that can be used by a wide range of users without those users needing an expert understanding (of the instruments) but lead to greater misinterpretation with assumptions made about the sensor performance [89; 90]. One of the first systematic data cubes to be established was the Australian Geoscience Data Cube [91], which provides standardised data in a common format on a range of processing platforms and with common pre-processing. These analysis-ready data initiatives have great potential for NSOs; however, there has not yet been the investment to establish how best to use the data at the national level [24]. There can also be a misconception that such data can be considered reliable simply due to the volume of data available; however, this is not necessarily the case [24]. Efforts are being made through satellite communities such as CEOS CARD4L [92] to provide some consistent validation and evaluation of such products, although those initiatives are still at an early stage and quality statements should be cautiously interpreted. It is critical for users of EO information to understand and consider the key characteristics (spectral, temporal, and spatial resolutions, and calibration uncertainties) of the satellites and their applications to determine which are the most suitable to address specific needs [37; 93; 94].