In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators that are based on the three pillars of sustainable development: economic, social, and environmental. Substantial challenges remain in obtaining data of the required quality, especially in developing countries, given the often limited resources available. One promising and innovative way of addressing this issue of data availability is to use Earth observation (EO). This paper presents the results of research to develop a novel analytical framework for assessing the potential of EO approaches to populate the SDG indicators. We present a Maturity Matrix Framework and apply it to all of the 232 SDG indicators. The results demonstrate that although the applicability of EO‐derived data do vary between the Sustainable Development Goal indicators, overall, EO has an important contribution to make towards populating a wide diversity of the Sustainable Development Goals indicators.

The Sustainable Development Goals (SDG) framework aims to end poverty, improve health and education, reduce inequality, design sustainable cities, support economic growth, tackle climate change and leave no one behind. To monitor and report the progress on the 231 unique SDGs indicators in all signatory countries, data play a key role. Here, we reviewed the data challenges and costs associated with obtaining traditional data and satellite data (particularly for developing countries), emphasizing the benefits of using satellite data, alongside their portal and platforms in data access. We then assessed, under the maturity matrix framework (MMF 2.0), the current potential of satellite data applications on the SDG indicators that were classified into the sustainability pillars. Despite the SDG framework having more focus on socio-economic aspects of sustainability, there has been a rapidly growing literature in the last few years giving practical examples in using earth observation (EO) to monitor both environmental and socio-economic SDG indicators; there is a potential to populate 108 indicators by using EO data. EO also has a wider potential to support the SDGs beyond the existing indicators.

In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals (SDGs) at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators based on the three pillars of sustainable development: economic, social, and environmental. However, substantial challenges remain in obtaining data of the required quality and quantity to populate these indicators efficiently. One promising and innovative way of addressing this issue is to use Earth observation (EO). The research reported here updates our original work to develop a Maturity Matrix Framework (MMF) for assessing the suitability of EO-derived data for populating the SDG indicators, with a special focus on those indicators covering the more social and economic dimensions of sustainable development, as these have been under-explored in terms of the contribution that can be made by EO. The advanced MMF 2.0 framework set out in this paper is based on a wide consultation with EO and indicator experts (semi-structured interviews with 38 respondents). This paper provides detail of the evolved structure of MMF 2.0 and illustrates its use for one of the SDG indicators (Indicator 11.1.1). The revised MMF is then applied to published work covering the full suite of SDG indicators and demonstrates that EO can make an important contribution to providing data relevant to a substantial number of the SDG indicators.

Satellite-observed night-time light (NTL) data provide a measure of the lighting brightness seen from space at different times and spatial and temporal resolutions, thus offering opportunities to explore them in many applications at different spatial locations (global, regional, local). However, most applications to date have been at relatively large spatial scales, despite the need to measure indicators at a local level. This paper sets out an analysis of the potential of NTL data for populating indicators at more local (neighbourhood, street) scales. We first reviewed the overall potential of NTL data for social indicators at different spatial scales by using a systematic search of the literature and applying the Maturity Matrix Framework (MMF). We also explored a case study (Durango State, Mexico) using Visible Infrared Imaging Radiometer Suite (VIIRS) imageries, other geospatial data, and the social gap index (SGI) to identify social gaps at the local scale. The literature review showed that NTL can play a role in supporting 49 out of 192 sustainable development goal (SDG) indicators having a focus on social issues, but most of these have been explored at the global or country scales. In the case study, we found that low radiance is indeed associated with higher SGI levels (i.e., more social deprivation) and vice versa. However, more research is needed from other contexts to support a link between NTL radiance levels and social indicators at local scales.

Nigeria is a country with a rapidly growing youthful population and the availability of good quality education for all is a key priority in the sustainable development of the country. An important element of this is the need to improve access to high-quality primary education in rural areas. A key indicator for progress on this is the provision of adequate classroom space for the more than 20 million learners in Nigerian public schools because overpopulated classrooms are known to have a strong negative impact on the performance of both pupils and their teachers. However, it can be challenging to rapidly monitor this indicator for the over 60 thousand primary schools, especially in rural areas. In this research, we used satellite Earth Observation (EO) and Nigerian government data to determine the size of available teaching spaces and evaluate the degree of overcrowding in a sample of 1900 randomly selected rural primary schools across 19 Nigerian states spanning all regions of the country. Our analysis shows that 81.4% of the schools examined were overcrowded according to the minimum standard threshold for school size of at least 1.2 m² of classroom space per pupil defined by the Federal Government of Nigeria. Such overcrowding can be expected to have a negative impact on educational performance, on achieving universal basic education and UN Sustainable Development Goal (SDG) 4 (Quality Education), and it can lead to poverty. While measuring floor area can be performed manually on site, collecting, and reporting such data for the number of rural primary schools in a large and populous country such as Nigeria is a serious, time-consuming administrative task with considerable potential for errors and data gaps. Satellite EO data are readily available including for remote areas, are reproducible and are easy to update over time. This paper provides a proof-of-concept example of how such EO data can contribute to addressing this socio-economic dimension of the SDGs framework.

Earth Observation (EO) techniques could offer a more cost-effective and rapid approach for reliable monitoring, reporting, and verification (MRV) of soil organic carbon (SOC). Here, we analyse the available published literature to assess whether it may be possible to estimate SOC using data from sensors mounted on satellites and airborne systems. This is complemented with research using a series of semi-structured interviews with experts in soil health and policy areas to understand the level of accuracy that is acceptable for MRV approaches for SOC. We also perform a cost-accuracy analysis of the approaches, including the use of EO techniques, for SOC assessment in the context of the new UK Environmental Land Management scheme. We summarise the state-of-the-art EO techniques for SOC assessment and identify 3 themes and 25 key suggestions and concerns for the MRV of SOC from the expert interviews. Notably, over three-quarters of the respondents considered that a ‘validation accuracy’ of 90% or better would be required from EO-based techniques to be acceptable as an effective system for the monitoring and reporting of SOC stocks. The cost-accuracy analysis revealed that a combination of EO technology and in situ sampling has the potential to offer a reliable, cost-effective approach to estimating SOC at a local scale (4 ha), although several challenges remain. We conclude by proposing an MRV framework for SOC that collates and integrates seven criteria for multiple data sources at the appropriate scales.