## 6 Conclusions  
This paper presents the data challenges for monitoring the SDGs and how EO satellite data have been used by different communities to support the SDGs and their indicators. It has analysed the contribution of EO data from assessments carried out by various organisations and the classification of the SDG indicators based on the three pillars of sustainability. We have drawn the following main conclusions from this:  
* To achieve the SDGs, inform policies and investment decisions, high-quality data (relevant, timely, reliable, and internationally comparable) for measurement and validation are needed.
* Indicators require good quality data: the lack of data and/or outdated data, particularly in developing countries, is a major constraint for monitoring the SDGs.
* Traditional data can be expensive and come with other disadvantages: new technologies, including satellite EO data and methods, can play a major role in the provision of data for many SDG indicators.
* The plethora of satellites now observing the Earth provides data at different resolutions and capabilities that have been used in many applications related to sustainable development, including in populating the SDG indicators.
* The framework of SDG indicators has an imbalance between the three pillars of sustainability and is mainly focused on the socio-economic aspects of sustainability, with only 27 environmental and 30 socio-environmental indicators out of its total of 231 unique indicators. The framework is also biased towards the \"state\" and \"response\" indicators of the DPSIR framework.
* Satellite data can be used as indirect support for many SDG indicators and has greater potential to support the SDGs beyond the existing indicators.  
Despite the efforts of various organisations in developing satellite open access platforms (e.g., data cube, EO portals and visualisation platforms), there are still constraints to using these data and services. Furthermore, NSOs in many/several developed countries are constrained in making use of EO data for populating their SDG indicators.  
The following are available online at [[https://www.mdpi.com/article/10.3390/su14031191/s1](https://www.mdpi.com/article/10.3390/su14031191/s1)]([https://www.mdpi.com/article/10.3390/su14031191/s1](https://www.mdpi.com/article/10.3390/su14031191/s1)), Figure S1: MMF 2.0 Dashboard (version 2019); Figure S2: MMF 2.0 Dashboard (version 2021); Table S1: List of satellites and their characteristics; Table S2: Free satellite imagery platform sources; Table S3: Platforms for big EO data management; Table S4: EO thematic products.  
Conceptualization, A.A., S.M., R.J.M., E.R.W. and J.L.; methodology, A.A., S.M., R.J.M., E.R.W. and J.L.; software, A.A.; validation, A.A., S.M., R.J.M., E.R.W. and J.L.; formal analysis, A.A. and E.R.W.; investigation, A.A.; resources, A.A., S.M., R.J.M., E.R.W. and J.L.; data curation, A.A.; writing--original draft preparation, A.A.; writing--review and editing, A.A., S.M., R.J.M., E.R.W. and J.L.; visualization, A.A. and E.R.W.; supervision, S.M., R.J.M., E.R.W. and J.L.; project administration, S.M.; funding acquisition, S.M., R.J.M., and E.R.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Environment Research Council (NERC) SCENARIO Doctoral Training Partnership, Grant/Award NE/L002566/1, CASE award partner the National Physical Laboratory (NPL) and the APC was funded through the University of Surrey.  
Not applicable.  
This research was supported by the SCENARIO Doctoral Training Partnership of the UK Natural Environment Research Council (NERC) with PhD funding for the first author. We thank the National Physical Laboratory (NPL) for co-funding and supervision support as the CASE award partner for this Ph.D. research.  
The authors declare no conflict of interest. The co-funder had a role in the design of the study, in the collection, analyses and interpretation of data, in the writing of the manuscript, and in the decision to publish the results.