COVID-19 Data Pathfinder
Colorized scanning electron micrograph of a VERO E6 cell (blue) heavily infected with SARS-COV-2 virus particles (orange), isolated from a patient sample. Image captured and color-enhanced at the National Institute of Allergy and Infectious Diseases Integrated Research Facility in Fort Detrick, Maryland. Credit: NIAID
New to using NASA Earth science data? This pathfinder is designed to help guide you through the process of selecting and using applicable datasets, with guidance on resolutions and direct links to the data sources.
After getting started here, there are numerous NASA resources that can help develop your skills further. If you are new to remote sensing, check out What is Remote Sensing? or view the Applied Remote Sensing Training on Fundamentals of Remote Sensing.
In January 2020, the World Health Organization (WHO) began investigating a cluster of medical cases caused by a new strain of the severe acute respiratory syndrome (SARS) coronavirus, SARS-CoV-2. SARS-CoV-2 causes the disease COVID-19, which has spread rapidly throughout the world. Scientists know very little about it.
Researchers across the globe are studying the novel virus to discover the key forces in the virus’ spread. In addition, remote sensing scientists are looking at the potential changes in the environment due to the change in human behavior — quarantine and stay-at-home measures.
Satellites cannot detect the spread of the disease from space, but they can measure changes in Earth’s environment due to changes in human behavior. NASA and other federal agencies use satellite and airborne data to assess regional and global environmental, economic, and societal impacts of the COVID-19 pandemic. (See the Rapid Response and Novel Research in Earth Science funding solicitation.) In addition, because of long-term data collection, historical remote sensing data provide more spatially and temporally complete data records, such as measurements of precipitation, temperature, and humidity, which provide baselines for historical comparisons, when looking at potential seasonality trends.
This data pathfinder provides links to datasets that can be used to research changing environmental impacts from modified human behavior patterns, the possibility of seasonal trends in virus transmission, and water availability.
About the Data
NASA’s Earth science data products are validated, meaning their accuracy has been assessed and verified over a widely distributed set of locations and time periods via several ground-truth and validation efforts. These data are freely and openly available to all users.
Datasets referenced in this pathfinder are from sensors shown in the table below. Some of these datasets are available through NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE provides data to the public within 3 hours of satellite overpass, which allows for near real-time (NRT) monitoring and decision making. If latency is not a primary concern, users are encouraged to use the standard science products, which are produced using the best available calibration, ancillary, and ephemeris information.
| Platform | Sensor | Spatial Resolution | Temporal Resolution | Measurement |
|---|---|---|---|---|
| Global Change Observation Mission 1st - Water, (GCOM-W1) | Advanced Microwave Scanning Radiometer 2 (AMSR2) * | Precipitation Rate: imagery resolution is 2 km, sensor resolution is 5 km | Precipitation rate: daily | Precipitation |
| Terra | Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) | 15 m, 30 m, 90 m | Variable | Land Surface Temperature, Surface Reflectance |
| Aqua | Atmospheric Infrared Sounder (AIRS) Level 2 and 3 products * | 1° x 1° | daily, 8-day, monthly | Surface Air Temperature, relative Humidity, Carbon Monoxide, Ozone |
| 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 Temperature, Evapotranspiration |
| Landsat 7 | Enhanced Thematic Mapper (ETM) | 15, 30, 60 m | 16 days | Surface Reflectance |
| Terra | Measurement of Pollution in the Troposphere (MOPITT) * | 1° x 1° | daily, monthly | Carbon Monoxide |
| Terra and Aqua | Moderate Resolution Imaging Spectroradiometer (MODIS) * | 250 m, 500 m, 1000 m, 5600 m | 1-2 days | Aerosol Optical Depth, Land Surface Temperature, Surface Reflectance, Land Cover Dynamics, Sea Surface Temperature, Ocean Color |
| Sentinel 3 | Ocean and Land Color Instrument (OLCI) | 300 m | 2 days | Ocean Color |
| Landsat 8 | Operational Land Imager (OLI) Thermal Infrared Sensor (TIRS) |
15, 30, 60 m | 16 days | Surface Reflectance |
| Aura | Ozone Monitoring Instrument (OMI) * | 13km x 24km | 1-2 days | Aerosol Optical Depth, Nitrogen Dioxide, Ozone, UV Radiation |
| Soil Moisture Active Passive (SMAP) | Radar (active) - no longer functional Microwave radiometer (passive) |
9 km, 36 km | 1 day | Soil Moisture, Sea Surface Salinity |
| Integrated multi-satellite data | Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Algorithm (TMPA) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) |
0.1° x 0.1° or 0.25° x 0.25° | half-hourly, daily, monthly | Precipitation |
| Sentinel 5-P | TROPOspheric Monitoring Instrument (TROPOMI) | 7km x 3.5km | daily | Nitrogen Dioxide, Carbon Monoxide, Ozone |
| Suomi National Polar-orbiting Partnership (NPP) | Visible Infrared Imaging Radiometer Suite (VIIRS) * | 500 m, 1000 m, 5600 m | daily | Aerosol Optical Depth, Surface Reflectance, Land Surface Temperature, Nighttime Imagery, Sea Surface Temperature, Ocean Color |
| Gravity Recovery and Climate Experiment (GRACE) | 0.125° | Giovanni: daily Earthdata: 7-day |
Groundwater | |
| * sensors from which select datasets are available in LANCE Note: this is not an exhaustive list but rather only includes datasets archived within NASA's Earth Observing System Data and Information System (EOSDIS) |
||||
In addition to mission data, NASA has a series of models that use satellite- and ground-based observational data to produce high-quality fields of land surface states and fluxes. The Land Data Assimilation System (LDAS) provides data in both a global collection (GLDAS) and a North American collection (NLDAS).
The Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) is a NASA atmospheric reanalysis that uses Goddard Earth Observing System Model, Version 5 (GEOS-5) data in its Atmospheric Data Assimilation System (ADAS). The MERRA project focuses on historical climate analyses for a broad range of weather and climate time scales and places the NASA suite of observations in a climate context.
| Model Source | Data Parameter | Spatial Resolution | Temporal Resolution |
|---|---|---|---|
| Land Data Assimilation System (LDAS) | Land surface temperature, Soil moisture, Precipitation | GLDAS: 0.25° FLDAS: 0.1° NLDAS: 0.125° |
Monthly, daily, hourly |
| Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) | Humidity, Precipitation Rate, Temperature, Land Surface Diagnostics, Winds, Soil Moisture | 0.5° x 0.625° | Diurnal, Monthly |
Use the Data
Lighting changes can be seen between January 19 and February 4, 2020 in Jianghan District, a commercial area of Wuhan, and nearby residential areas. (Courtesy of NASA's Earth Observatory.)
General:
- NASA Monitors Environmental Signals From Global Response to COVID-19
- How the Coronavirus Is (and Is Not) Affecting the Environment
- NASA Probes Environment, COVID-19 Impacts, Possible Links
- The Race to Understand the Science of Coronavirus
- NASA Funds Four Research Projects on COVID-19 Impacts
- Astrobiologists Aid in Fighting Coronavirus
Air Quality:
- Airborne Nitrogen Dioxide Plummets Over China
- Airborne Particle Levels Plummet in Northern India
- New-generation satellite observations monitor air pollution during COVID-19 lockdown measures in California
- Reductions in Nitrogen Dioxide Associated with Decreased Fossil Fuel Use Resulting from COVID-19 Mitigation
Nighttime Imagery:
Demographics:
Water Availability:
Benefits and Limitations of Remote Sensing Data
When deciding to use remote sensing data, it is important to consider both the benefits and the limitations of the data.
Benefits of using satellite data include:
- Larger spatial coverage over ground-based data: ground-based data are more comprehensive on a local scale, providing direct observations of phenomena. However, airborne or satellite data are far more extensive, with millions of measurements over regional and global scales, providing more complete spatial coverage.
- Better temporal resolution: many ground-based studies often use data sampled at a single point in time. The temporal resolution of satellite data ranges from hours to weeks. Many satellites pass over the same spot on Earth every one-two days; some as seldom as every 16+ days. These data have been collected over increasingly long periods of time, from the 1970s to the present.
- Monitoring in near-real time: some satellite information is available 3-5 hours after observation, allowing for a faster response than ground-based observations.
Limitations specific to using satellite data in ecological assessments:
- Loss of fine spatial resolution: while lower resolution data provide a more global view, as with the Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, the spatial resolution is too coarse for certain assessments. Most satellite-based data are not at a fine enough resolution to distinguish individual organisms and their movements; for example, using most satellite-based data, scientists can determine the presence/absence of an algal bloom, yet the particular species of algae cannot be determined. This is not the case for instruments with higher resolutions, like those on Landsat or present on airborne missions.
- Spectral resolution: passive instruments (those that use the 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 active instruments like microwave sensors.
It is not possible to combine all desirable features into one remote sensor: to acquire observations with 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 a given area of research, application, or decision making is a crucial first step to getting started with using remote sensing data.
Published May 12, 2020
Page Last Updated: May 21, 2020 at 10:45 AM EDT
