\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Source** & **Data Type** & **Description** & **Data Challenges** \\ \hline \multirow{6}{*}{Census data (CD)} & \multirow{3}{*}{Census data (CD)} & CD is a systematic recording of information, focusing mainly on the population and takes place at least once every 10 years (varies between countries); NSOs are responsible for collecting these data. & \\ \cline{2-4}  & Household surveys & & \\ (HS) & HS provides demographic and socio-economic data. & - & lack of data accessibility and availability, data quality, data continuity, transparency, and accountability \\ \cline{2-4}  & Agricultural survey & AS covers information about land use and ownership, operator characteristics, production practices, crop yields and productivity, income, and expenditures. & - & lack of funding/financing \\ \cline{2-4}  & \multirow{3}{*}{Administrative data} & ADs are collected by government departments and include information about welfare, tax, health, and educational record systems. & - \\ \cline{2-4}  & & CR is a collection of data that records main events in a person’s life (such as birth, marriage, divorce, adoption, and death). The primary responsibility for collecting such data is typically attributed to different Ministries and National Statistical Offices (NSOs). & - \\ \cline{2-4}  & \multirow{3}{*}{Economic statistics (ES)} & ES typically include data on the labour force, tax returns, trade statistics, etc. The scope of these data are to measure the financial performance of economic agents and can include estimates of Gross Domestic Product, Gross National Income, national poverty levels, household income, labour force participation and employment status, and economic losses from disasters. & \\ \cline{2-4}  & & GD refers to all data from previous categories that are located with environmental data, that include geolocations asordinates and topology, allowing the data to be illustrated geographically. & - \\ \cline{2-4}  & \multirow{3}{*}{Geospatial data (GD)} & Environmental data can be derived from readily available imagery from satellites, airborne, etc. & - \\ \cline{2-4}  & & at different spatial resolutions that are coupled with & statistical methodology \\ \cline{2-4}  & & geospatial tools. Satellite imagery is currently used to populate environmental indicators linked to agriculture, biodiversity, frosty and land cover and use change. & - \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{6}{*}{Environmental data (Env)} & Other environmental data refers to ground & - & limited hardware and software \\ \cline{2-4}  & technologies or surveys. Many environmental & - & lack of understanding of EO data \\ \cline{1-1}  & indicators also include real-time monitoring of & - & value prevents investment \\ \cline{1-1}  & conditions, such as air quality in urban areas or water supply. & - & reluctance to change the current \\ \cline{1-1} \cline{2-4}  & & &  method \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data type and challenges associated. explore the potential of using EO derived data. As part of this, it is necessary to give the reader a sense of how EO technology has progressed to date and the authors have included Supplementary Materials which set out some of the technical capabilities of EO-based instruments, EO data portals and data platforms currently available. Our review highlights the main applications of satellite imagery in sustainability and later links to peer-reviewed articles and best practices of populating SDG indicators. The paper applies a framework first developed by [PERSON] et al. [14; 15] designed to assess the potential of EO for the SDG indicators and expands it by considering the classification of indicators into the various indicator types based on the sustainable development pillars. This analysis was designed to identify the areas where more work is needed in the use of EO data for indicators, but also to understand the field that has been covered in recent years.