Sustainable Development Goals Data Pathfinders

The 2030 Agenda for Sustainable Development outlines 17 Sustainable Development Goals (SDG), with associated targets and indicators.

The 2030 Agenda for Sustainable Development, an international framework signed by all United Nations (UN) 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

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 the 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, Evapotranspiration, Gross Primary Productivity, Land Cover, Land Surface Temperature, Snow Cover, 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, Land Surface Temperature, 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
Evaporative Stress Index, Evapotranspiration, Land Surface Temperature, Water Use Efficiency International Space Station
Note: data are available in areas of 51.6° S to 51.6° N
Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) 70 m ~ 1-7 days
Land Surface Backscatter JAXA/METI Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 10 m, 100 m  
Groundwater NASA Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) Star Camera Assembly (SCA), K-Band Ranging System (KBR), and SuperSTAR Accelerometer (ACC) 0.5° 1 month
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
Land Surface Temperature, 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
Precipitation Integrated multi-satellite data TRMM Multi-satellite Precipitation Algorithm (TMPA) and Integrated Multi-satellite Retrievals for GPM (IMERG) 0.1° x 0.1° or 0.25° x 0.25° half hourly, daily, monthly
Snow Water Equivalent Japanese Aerospace Exploration Agency Global Change Observation Mission -Water Satellite 1 ("Shizuku"), (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2) * 25 km daily, 5-day, monthly
Snow Water Equivalent Aqua Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E)
(Data only available through 2011)
25 km daily, 5-day, monthly
Soil Moisture Soil Moisture Active Passive (SMAP) Radar (active) - no longer functional
Microwave radiometer (passive)
9 km, 36 km 1 day
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

* sensors from which select datasets are available in LANCE

Back to Top

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

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:

Benefits and Limitations of Remote Sensing Data

The United States is fortunate to have numerous in-situ measurements for assessing water quality parameters, yet in-situ measurements have limited sample collection and so are not representative of the entire water body. In other countries and in more rural areas of the United States, sampling is even more limited or non-existent. Satellite data provide more regional to global spatial coverage; some information is available in near real-time, allowing for a more efficient response. Satellite data have also been collected for a longer period of time, providing for data continuity and trend analyses. With satellite data, assessments can be made regarding ocean color, but this provides only qualitative measures. For quantitative water quality monitoring analysis, in-situ measurements are required; the combination of satellite observations with in-situ makes for a more robust and integrated forecasting and response system.

While satellite data provide a more global view, it is important to note that satellite measurements are made through the atmosphere and not at the water level. As such, atmospheric correction algorithms must be run before water quality assessments can be made.

Also note that the sensors all have varying spatial, temporal and radiometric resolutions. For example, many of the polar-orbiting satellites only pass over the same location every 1-2 days, but have a coarser spatial resolution, while others pass over every 16+ days, but have a much finer spatial resolution. Finding the right instrument or understanding the modeling processes for your area of interest is key.

Other challenges include the difficulty in separating water quality parameters of CDOM, NAP, and chlorophyll content when all three are present. Also, remote sensing observations alone are unable to discern between algal types or toxins.

Last Updated
Feb 11, 2021