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Sustainable Development Goals Data Pathfinders

About- Sustainable Development Goals Image (7/10/20)

The 2030 Agenda for Sustainable Development, an international framework signed by all United Nations (U.N.) member states in 2015, outlines 17 Sustainable Development Goals (SDG), with associated targets and indicators. The vision of the SDG framework encourages every country to assume responsibility for planning and providing better outcomes for future generations, leaving no one behind.

Earth observations are an essential source of information in the implementation of solutions and in monitoring progress on meeting the SDGs. Earth observations (from satellite, airborne, and in-situ sensors) provide accurate and reliable information on the state of the atmosphere, ocean, ecosystems, natural resources, and built infrastructure along with their change over time. All remote sensing data provided by NASA, and most data from other agencies' Earth-observing satellites, are freely and openly available to all data users, which can reduce the cost of monitoring the SDGs and provides developing countries a means to acquire and utilize these data for other policy-making purposes.

Many NASA missions collect data that provide spatial, spectral, and temporal information that can be processed and transformed into variables or high-level products that are useful to produce SDG indicators, support SDG monitoring and implementation, and evaluate progress toward achieving sustainable development.

Each Goal below highlights NASA Earth observation data that can aid in calculating indicators and monitoring progress towards achieving SDG Goals and Targets.

About the Data

About the Data

NASA collaborates with other federal entities and international space organizations, including USGS; the Japan Aerospace Exploration Agency (JAXA) and Ministry of Economy, Trade, and Industry (METI); and European Space Agency (ESA), to provide information for understanding a number of phenomena that can be used in monitoring progress towards meeting key indicators within the SDG framework.

The accuracy of NASA's Earth science data products has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts. For more information on this process, please see NASA's data maturity levels.

Datasets referenced in this Pathfinder are from satellite and airborne sensors shown in the table below, including their spatial and temporal resolutions. Note that many satellites/platforms carry multiple sensors; the table below only lists the primary sensor used in collecting the specified measurement. When available, NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides data to the public generally within three hours of satellite overpass, which allows for near real-time (NRT) monitoring and decision making. Sensors from which select datasets are available in LANCE are marked with an asterisk (*).

Note: This is not an exhaustive list of datasets but rather only includes datasets from NASA's Earth Observing System Data and Information System (EOSDIS). km = kilometer; m = meter

Measurement Satellite/Platform Sensor Spatial Resolution Temporal Resolution
Aerosol Index NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Ozone Mapping and Profiler Suite (OMPS) 50 km x 50 km 101 minutes, daily
Aerosol Index ESA Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) 7 km x 3.5 km daily
Aerosol Optical Depth Aura Ozone Monitoring Instrument (OMI) 13 km x 24 km daily
Aerosol Optical Depth, Surface Reflectance, Vegetation Indices Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) * 250 m, 500 m, 1000 m 1 to 2 days
Aerosol Optical Depth, Surface Reflectance, Vegetation Indices NOAA Joint Polar Satellite System (JPSS) NOAA-20 and Suomi National Polar-orbiting Partnership (Suomi NPP) satellites Visible Infrared Imaging Radiometer Suite (VIIRS) * 500 m, 1000 m, 5600 m daily
Land Surface Backscatter JAXA/METI Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 10 m, 100 m
Land Surface Backscatter ESA Sentinel-1 and -2 Synthetic Aperture Radar (SAR) 25 x 40 m, 5 x 5 m, and 5 x 20 m 12 days (using together 6 days)
Land Surface Backscatter Uninhabited Aerial Vehicle
Note: data are available over specific areas
SAR 1.8 m Non-cyclic
Surface Reflectance NASA/USGS Landsat 8 Operational Land Imager (OLI)
Thermal Infrared Sensor (TIRS)
15 m, 30 m, 60 m 16 days
Surface Reflectance NASA/USGS Landsat 7 Enhanced Thematic Mapper (ETM) 15 m, 30 m, 60 m 16 days
Surface Reflectance Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 15 m Very Near Infrared (VNIR), 30 m Short-Wave Infrared (SWIR), 90 m Thermal Infrared (TIR) Variable

* sensors from which select datasets are available in LANCE

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Use the Data

Use the Data
Electrification analysis for 2018 around Lake Victoria. The map was created using processed nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-NASA Suomi NPP satellite, incorporated with land cover type data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and overlaid with different gridded population products. Credit: NASA Earth Observatory

Electrification analysis for 2018 around Lake Victoria. The map was created using processed nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-NASA Suomi NPP satellite, incorporated with land cover type data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and overlaid with different gridded population products. Credit: NASA Earth Observatory

Scientists, researchers, emergency managers, decision makers, and others use remote sensing data in numerous ways. Remote sensing data, coupled with ground-based data, aids in assessing the progress towards meeting SDGs.



SDG 2, Zero Hunger:

SDG 6, Clean Water and Sanitation:

SDG 11, Sustainable Cities and Communities:

SDG 15, Life On Land:

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Other NASA Assets of Interest

Other NASA Assets of Interest
ARSET logo over satellite image for SDG Land Degradation training

NASA’s Applied Remote Sensing Training (ARSET) Earth Observations for Monitoring the UN Sustainable Development Goals aims to show the potential and current applications of Earth observations and geospatial information for monitoring the U.N. SDGs. This two-day training incorporates a combination of lectures, demonstrations, and hands-on activities, featuring guest speakers from the U.N. Global Geospatial Information Management (UN-GGIM), the Group on Earth Observations (GEO), NASA, and other organizations. Participants learn to access, interpret, and apply NASA Earth observations to local and global scales, with a focus on applying Earth observations to address SDG monitoring and reporting.

ARSET Remote Sensing for Monitoring Land Degradation and Sustainable Cities SDGs highlights a tool that uses NASA Earth observations to track land degradation and urban development that meet the appropriate SDG targets. Attendees learn to use a freely-available QGIS plugin called Trends.Earth, created by Conservation International (CI). The training also includes special guest speakers from the United Nations Convention to Combat Desertification (UNCCD) and UN Habitat.

ARSET logo over remote sensing image of city for Population Grids Training

ARSET Introduction to Population Grids and their Integration with Remote Sensing Data for Sustainable Development and Disaster Management focuses on the different global population grids and their application to a range of topics related to development planning and monitoring of the SDGs (e.g., environment, hazards, and access to resources). Attendees are exposed to the latest data and methods used to produce global grids, how the grids incorporate remote sensing inputs, and how population grids can be used in conjunction with other types of data.

ARSET Satellite Derived Annual PM2.5 Datasets in Support of United Nations Sustainable Development Goals provides data and resources to analyze PM2.5 over cities using satellite observations. This training covers accessing the data, analyzing long-term trends, and combining PM2.5 and population datasets to understand long-term exposure.

ARSET Remote Sensing for Mangroves in Support of the UN Sustainable Development Goals provides information and data to understand mangrove extent and biomass, which is essential to managing the sustainability of water ecosystems. Attendees are exposed to the latest tools for mapping mangrove extent using Google Earth Engine and how these observations can be used to report SDG quotas.

ARSET Using the UN Biodiversity Lab to Support National Conservation and Sustainable Development Goals aims to fill a gap of biodiversity, conservation, the SDGs, and how to link NASA satellite data to ecological and human-influenced systems This training extends the influence of NASA-supported tools and resources.

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External Resources

External Resources
Webinar Banner- SEDAC POPGRID (12/3/19)

The POPGRID Data Collaborative aims to bring together and expand the international community of data providers, users, and sponsors concerned with georeferenced data on population, human settlements, and infrastructure.

Trends.Earth is a platform from Conservation International (CI) for monitoring land change using Earth observations in an innovative desktop and cloud-based system. Trends.Earth allows users to plot time series of key indicators of land change (including degradation and improvement), to produce maps and other graphics that can support monitoring and reporting, and to track the impact of sustainable land management or other projects.

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Benefits and Limitations of Remote Sensing Data

Benefits and Limitations of Remote Sensing Data

In determining whether or not to use remote sensing data, it is important to understand not only the benefits but also the limitations of the data. Benefits of using satellite data include:

  • Filling in data gaps: The United States is fortunate to have numerous ground-based measurements for assessing water storage, precipitation, and more. However, this is not the case in other countries and even in some of the more remote areas of the U.S. Satellite data provide local, regional, and global spatial coverage and are also useful for observing areas that are inaccessible.
  • Monitoring in near-real time: Some satellite information is available 3-5 hours after observation, allowing for a faster response. NASA's LANCE supports users interested in monitoring a wide variety of natural and human-created phenomena in a timely manner.

With satellite data, assessments can be made regarding the land surface, precipitation events, ground movement and air temperature. In addition, incorporating satellite data with in-situ data into modeling programs makes for a more robust and integrated forecasting system. When using data and imagery from satellite-borne sensors, it's important to use the right sensor for the spatial, temporal, and spectral resolutions you are seeking:

  • Spatial resolution: While lower resolution data provide a more global view, as with measurements from the MODIS instrument aboard NASA's Terra and Aqua satellites, the spatial resolution is too coarse for certain assessments. This is not the case for higher resolution instruments, like those on the joint NASA/USGS Landsat series of satellites.
  • Temporal resolution: Many satellites only pass over the same spot on Earth every 1-2 days and sometimes as seldom as every 16+ days. This is the satellite's return period.
  • Spectral resolution: Passive instruments (those that use energy being reflected or emitted from Earth for measurements) are not able to penetrate cloud or vegetation cover, which can lead to data gaps or a decrease in data utility. This is not the case when using data from microwave or thermal sensors (active sensors).

It is difficult to combine all of the desirable features into one remote sensor; to acquire observations with moderate to high spatial resolution (like Landsat) a narrower swath is required, which in turn requires more time between observations of a given area resulting in a lower temporal resolution. Researchers have to make trade-offs. Finding a sensor with the spatio-temporal resolution capable of addressing your research, application, or decision-making process needs is a crucial first step to getting started with using remote sensing data.

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Published April 5, 2021

Page Last Updated: Jul 6, 2021 at 11:05 AM EDT