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A new book explores how Earth science knowledge addresses critical global challenges including sustainable development, disaster risk reduction, and climate change.

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6 reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped over time for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1 reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

This paper reviews the key role that Earth Observations (EO) play in achieving the Sustainable Development Goals (SDGs) as articulated in the 2030 Agenda document and in monitoring, measuring, and reporting on progress towards the associated targets. This paper also highlights how the Group on Earth Observations (GEO) would contribute to ensure the actual use of EO in support of the 2030 Agenda; and how the Global Earth Observations System of Systems meets requirements for efficient investments in science and technology and a good return on investment, which is elaborated in the Addis Ababa Action Agenda on development financing. Through a number of examples, we first discuss how extensive EO use would: provide a substantial contribution to the achievements of the SDGs by enabling informed decision-making and by allowing monitoring of the expected results; improve national statistics for greater accuracy, by ensuring that the data are “spatially-explicit” and directly contribute to calculate the agreed SDG Targets and Indicators support the fostering of synergy between the SDGs and multilateral environmental agreements by addressing cross-cutting themes such as climate and energy; and facilitate countries’ approaches for working across different development sectors, which is, according to the special adviser on the 2030 Agenda, a key challenge to achieve the SDGs. We then focus on the role that GEO could play in enabling actual use of EO in support of the 2030 Agenda by directly addressing the Strategic Development Goal 17 on partnerships.

With less than a decade left to attain the Sustainable Development Goals (SDGs), this communication aims to improve understanding of the enabling environment that is essential for Earth observations (EO) to be fully adopted within the institutional settings that drive the implementation of the SDGs and the Global Indicator Framework, an effective review mechanism for tracking progress at global to national and local levels. This paper also serves as an introduction to the Remote Sensing of Environment's Special Issue (SI) on Earth Observation for the Sustainable Development Goals. The seventeen contributions published in this SI showcase the application of EO data, methods, and tools to support countries in target setting for the SDGs, including baseline determination, as well as tracking of progress on SDG implementation and informing sustainable development planning and decision making. The majority of published articles focus on Goals 6 (Clean Water and Sanitation), 14 (Life below Water) and 15 (Life on Land). We also present our own analysis of existing EO systems available to generate data for SDG indicators addressed via the SI contributions, including main resolution characteristics, and assess factors that hinder the full integration of EO solutions for the SDGs within country processes, institutions of government, and policies. We conclude that an urgent need exists for the EO community to work more closely with local and regional governments and other relevant stakeholders to promote the operationalization of EO solutions for implementing the 2030 Agenda at global to local levels. Our review also illustrates the need for transitioning towards new EO for SDG frameworks that are focused on the knowledge element of the data-information-knowledge-wisdom paradigm, rather than the data and information aspects.

This paper reviews the key role that analysis- ready Earth observations (EO) play in achieving the United Nations (UN) Sustainable Development Goals (SDGs) and in monitoring, measuring, and reporting on progress towards the associated targets and the Global Indicator Framework. In response to the enhanced demand for official and complementary data with a breadth of socioeconomic applications and new opportunities for how these data can be sourced, distributed and used, the Committee on Earth Observation Satellites (CEOS) has launched the Open Data Cube (ODC) initiative. This initiative seeks to increase the value and impact of global EO data by providing an open and freely accessible exploitation architecture, while fostering a community of cooperation that promotes EO data and related information usage and sharing for the benefit of society. The ODC initiative is working closely with the Group on Earth Observations (GEO) Earth Observations in Service of the 2030 Agenda for Sustainable Development (EO4SDG) initiative, whose primary purpose is to demonstrate and realize the potential of EO to support country-level implementation of the SDGs.

Remotely sensed Earth observations (EO) have their history firmly rooted in agricultural monitoring, and more recently with applications in food production, food security, and sustainable agriculture. Still, after more than 45 years of observing the Earth's land surface, usage of EO data by operational monitoring entities concerned with global agriculture is uneven. One reason for this is a gap in continuous communication and collaboration between those who undertake research and development of methods for cropland assessment and monitoring, and those who have the mandate to report on agricultural indicators at a national, regional, and global scales. The recent international policy focus on the United Nations 2030 Agenda for Sustainable Development via its Sustainable Development Goals (SDGs) is giving increased attention to measurements and indicators for monitoring and measuring progress for meeting these goals. Satellite EO provide a source of measurements beyond traditional census data collection and statistical reporting. In this vein, this overview paper describes the current and potential uses of EO data and tools that can support the SDGs, particularly highlighting the activities of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative. GEOGLAM is composed of agricultural ministries, intergovernmental organizations, research entities, universities, space agencies, and members of industry concerned with agricultural monitoring. This GEOGLAM community has a broad portfolio of activities which provide information on the state and changes in agricultural production and land use that can be considered as contributions to both supporting the attainment of several of the 17 SDGs and many of their 169 Targets, as well as monitoring their achievement via the Global Indicator Framework. GEOGLAM contributes in particular to Goal 2: Zero Hunger, but also has less immediately apparent contributions in the realms of water (Goal 6), responsible consumption and production (Goal 12), climate action (Goal 13), life on land (Goal 15), and global partnerships for sustainable development (Goal 17). We further characterize the applicability and use of EO data products and tools as they correspond with the United Nations Interagency Expert Group on Sustainable Development Goals (IAEG-SDGs) Global Indicator Framework. This inventory will be complemented by a discussion of the intersection of other policy mandates with the SDGs in the agriculture and food security contexts, and will conclude with a discussion of approaches to improving awareness of EO value and bridging the gap between policy and EO communities, to the societal benefit of all with no one left behind.

Decadal variability in the climate system from the Atlantic Multidecadal Oscillation (AMO) is one of the major sources of variability at this temporal scale that climate models must properly incorporate because of its climate impact. The current analysis of historical simulations of the twentieth century climate from models participating in the CMIP3 and CMIP5 projects assesses how these models portray the observed spatiotemporal features of the sea surface temperature (SST) and precipitation anomalies associated with the AMO. A short sample of the models is analyzed in detail by using all ensembles available of the models CCSM3, GFDL-CM2.1, UKMO-HadCM3, and ECHAM5/MPI-OM from the CMIP3 project, and the models CCSM4, GFDL-CM3, UKMO-HadGEM2-ES, and MPI-ESM-LR from the CMIP5 project. The structure and evolution of the SST anomalies of the AMO have not progressed consistently from the CMIP3 to the CMIP5 models. While the characteristic period of the AMO (smoothed with a binomial filter applied fifty times) is underestimated by the three of the models, the e-folding time of the autocorrelations shows that all models underestimate the 44-year value from observations by almost 50 %. Variability of the AMO in the 10–20/70–80 year ranges is overestimated/underestimated in the models and the variability in the 10–20 year range increases in three of the models from the CMIP3 to the CMIP5 versions. Spatial variability and correlation of the AMO regressed precipitation and SST anomalies in summer and fall indicate that models are not up to the task of simulating the AMO impact on the hydroclimate over the neighboring continents. This is in spite of the fact that the spatial variability and correlations in the SST anomalies improve from CMIP3 to CMIP5 versions in two of the models. However, a multi-model mean from a sample of 14 models whose first ensemble was analyzed indicated there were no improvements in the structure of the SST anomalies of the AMO or associated regional precipitation anomalies in summer and fall from CMIP3 to CMIP5 projects.

This study aims to characterize the spatiotemporal features of the low frequency Atlantic Multidecadal Oscillation (AMO), its oceanic and atmospheric footprint and its associated hydroclimate impact. To accomplish this, we compare and evaluate the representation of AMO-related features both in observations and in historical simulations of the twentieth century climate from models participating in the IPCC’s CMIP5 project. Climate models from international leading research institutions are chosen: CCSM4, GFDL-CM3, UKMO-HadCM3 and ECHAM6/MPI-ESM-LR. Each model employed includes at least three and as many as nine ensemble members. Our analysis suggests that the four models underestimate the characteristic period of the AMO, as well as its temporal variability; this is associated with an underestimation/overestimation of spectral peaks in the 70–80 year/10–20 year range. The four models manifest the mid-latitude focus of the AMO-related SST anomalies, as well as certain features of its subsurface heat content signal. However, they are limited when it comes to simulating some of the key oceanic and atmospheric footprints of the phenomenon, such as its signature on subsurface salinity, oceanic heat content and geopotential height anomalies. Thus, it is not surprising that the models are unable to capture the majority of the associated hydroclimate impact on the neighboring continents, including underestimation of the surface warming that is linked to the positive phase of the AMO and is critical for the models to be trusted on projections of future climate and decadal predictions.