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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Providing uncertainties in satellite datasets used for Earth observation can be a daunting prospect because of the many processing stages and input data required to convert raw detector counts to calibrated radiances. The Sea and Land Surface Temperature Radiometer (SLSTR) was designed to provide measurements of the Earth's surface for operational and climate applications. In this paper the authors describe the traceability chain and derivation of uncertainty estimates for the thermal infrared channel radiometry. Starting from the instrument model, the contributing input quantities are identified to build up an uncertainty effects tree. The characterisation of each input effect is described, and uncertainty estimates provided which are used to derive the combined uncertainties as a function of scene temperature. The SLSTR Level-1 data products provide uncertainty estimates for fully random effects (noise) and systematic effects that can be mapped for each image pixel, examples of which are shown. [ABSTRACT FROM AUTHOR]

This paper presents a new Fundamental Climate Data Record (FCDR) for the visible (VIS) channel of the Meteosat Visible and Infrared Imager (MVIRI), with pixel-level metrologically traceable uncertainties and error covariance estimates. MVIRI has flown onboard Meteosat First Generation (MFG) satellites between 1982 and 2017. It has served the weather forecasting community with measurements of "visible", "infra-red" and "water vapour" radiance in near real-time. The precision of the pre-launch sensor spectral response function (SRF) characterisation, particularly of the visible band of this sensor type, improved considerably with time, resulting in higher quality radiances towards the end of the MFG program. Despite these improvements, the correction of the degradation of this sensor has remained a challenging task and previous studies have found the SRF degradation to be faster in the blue than in the near-infrared part of the spectrum. With these limitations, the dataset cannot be immediately applied in climate science. In order to provide a data record that is suited for climate studies, the Horizon 2020 project "FIDelity and Uncertainty in Climate-data records from Earth Observation" (FIDUCEO) conducted (1) a thorough metrological uncertainty analysis for each instrument, and (2) a recalibration using enhanced input data such as reconstructed SRFs. In this paper, we present the metrological analysis, the recalibration results and the resulting consolidated FCDR. In the course of this study we were able to trace-back the remaining uncertainties in the calibrated MVIRI reflectances to underlying effects that have distinct physical root-causes and spatial/temporal correlation patterns. SEVIRI and SCIAMACHY reflectances have been used for a validation of the harmonised dataset. The resulting new FCDR is publicly available for climate studies and for the production of climate data records (CDRs) spanning about 35 years. [ABSTRACT FROM AUTHOR]

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite only operates over a relatively short period of time, creating a climate data record also requires the combination of space-borne measurements from a series of several (often similar) satellite sensors. Simply combining calibrated measurements from several sensors can, however, produce an inconsistent climate data record. This is particularly true of older, historic sensors whose behaviour in space was often different from their behaviour during pre-launch calibration and more scientific value can be derived from considering the series of historical and present satellites as a whole. Here, we consider harmonisation as a process that obtains new calibration coefficients for revised sensor calibration models by comparing calibrated measurements over appropriate satellite-to-satellite matchups, such as simultaneous nadir overpasses and which reconciles the calibration of different sensors given their estimated spectral response function differences. We present the concept of a framework that establishes calibration coefficients and their uncertainty and error covariance for an arbitrary number of sensors in a metrologically-rigorous manner. We describe harmonisation and its mathematical formulation as an inverse problem that is extremely challenging when some hundreds of millions of matchups are involved and the errors of fundamental sensor measurements are correlated. We solve the harmonisation problem as marginalised errors in variables regression. The algorithm involves computation of first and second-order partial derivatives using Algorithmic Differentiation. Finally, we present re-calibrated radiances from a series of nine Advanced Very High Resolution Radiometer sensors showing that the new time series has smaller matchup differences compared to the unharmonised case while being consistent with uncertainty statistics. [ABSTRACT FROM AUTHOR]

The uncertainty in a climate data records (CDRs) derived from Earth observations in part derives from the propagated uncertainty in the radiance record (the fundamental climate data record, FCDR) from which the geophysical estimates in the CDR are derived. A common barrier to providing uncertainty-quantified CDRs is the inaccessibility to CDR creators of appropriate radiance uncertainty information in the FCDR. Here, we propose radiance uncertainty information designed directly to facilitate estimation of propagated uncertainty in derived CDRs at full resolution and in gridded products. Errors in Earth observations are typically highly structured and complex, and the uncertainty information we propose is of intermediate complexity, sufficient to capture the main variability in propagated uncertainty in a CDR, while avoiding unfeasible complexity or data volume. The uncertainty and error correlation characteristics of uncertainty are quantified for three classes of error with different propagation properties: independent, structured and common radiance errors. The meaning, mathematical derivations, practical evaluation and example applications of this set of uncertainty information are presented. [ABSTRACT FROM AUTHOR]