COVID-19 Data Pathfinder

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.

In collaboration with the Amazon Web Service Public Dataset Program, NASA has made some of the datasets available in Cloud Optimized GeoTIFF (COG) format. These datasets are noted with "COG" in the table below.

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 (COG), Land Surface Temperature, Surface Reflectance, Land Cover Dynamics, Sea Surface Temperature, Ocean Color, Vegetation Indices (COG)
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 (COG), 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, UV Radiation
Suomi National Polar-orbiting Partnership (Suomi 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

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Scientists, researchers, decision-makers, and others use remote sensing data in numerous ways. Satellite imagery, coupled with ground-based data, aids in our understanding of many natural phenomena and human behaviors. Below are several use cases illustrating how NASA Earth science data are being used to understand COVID-19 and how changes in human behavior are having impacts on the environment.

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
• Astrobiologists Seek Antiviral Solutions for COVID-19
• Satellites show a Decline in Fire in the U.S. Southeast
• NASA, Partner Space Agencies Amass Global View of COVID-19 Impacts

Seasonality:

• Could COVID-19 Have Seasons? Searching for Signals in Earth Data

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
• Changes in the Observed Tropospheric NO2 Column Density Story Map
• NASA Satellite Data Show 30 Percent Drop In Air Pollution Over Northeast U.S.
• How to Find and Visualize Nitrogen Dioxide Satellite Data
• Nitrogen Dioxide Levels Rebound in China

Nighttime Imagery:

• Nighttime Images Capture Change in China
• Nighttime Images Show Changes in Human Activity

Demographics:

• NASA SEDAC Application Puts Current Global COVID-19 Data at your Fingertips

Water Availability:

• COVID-19-related issues for the Navajo Nation

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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.

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