The World Health Organization (WHO) notes that disease outbreaks are often associated with environmental conditions. Changes in water and air quality, for example, can affect disease transmission. Recent studies show that dust storm activity in the Southwestern United States is connected to spikes of Valley fever in the region as the dust storms transport the fungal spores that cause the disease. Changes in environmental variables such as temperature and precipitation can also impact disease transmission by changing the habitat suitability for organisms that can transmit infectious pathogens, like mosquitoes, ticks, and rodents. Increases in flooded, vegetated areas provide favorable conditions for mosquitoes that carry the Rift Valley fever virus across sub-Saharan Africa and the Arabian peninsula. Environmental variables, such as temperature and humidity, play an important role in seasonality trends for diseases. As observed from the COVID-19 pandemic, disease outbreaks can lead to environmental changes due to altered human behavior, such as decreased vehicle use and stay-at-home measures leading to reductions in greenhouse gases.
Through its Sustainable Development Goals (SDGs), the United Nations has set a target of ending epidemics of malaria and other tropical diseases by 2030 as well as taking steps to combat water-borne and other communicable diseases.
While sensors aboard Earth observing satellites cannot detect the spread of diseases from space, they provide long-term data records that help address, inform, and monitor many of the factors mentioned above, including air and water quality, habitat suitability, seasonality, and changes in Earth's environment due to changes in human behavior. This data pathfinder provides links to relevant datasets that can be used in each of these cases along with examples of tools that can be helpful in working with these data. While not designed to be a complete list of all salient data and tools in NASA's Earth Observing System Data and Information System (EOSDIS) collection, the following sections will help you chart a path to finding the best data and tools for your particular needs. For information about data and tools specifically for investigations into COVID-19, please see the COVID-19 Data Pathfinder.
Please visit the Earthdata Forum, where you can interact with other users and NASA subject matter experts on a variety of Earth science research and applications topics.
About the Data
NASA collaborates with other federal entities and international space organizations to provide information for understanding environmental changes that can lead to disease emergence, transmission, and outbreaks. All NASA Earth science data products are freely and openly available, and have been extensively validated. Their accuracy has been assessed and verified over a widely distributed set of locations and time periods via numerous ground-truth and validation efforts (such as field campaigns and comparison with in situ instruments collecting similar data) along with scientific analyses.
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 data products are available generally within three hours of a satellite observation, which allows for near real-time (NRT) monitoring and decision making. If low-latency is not a primary concern, users are encouraged to use 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 datasets available in Cloud Optimized GeoTIFF (COG) format. These datasets are noted with "COG" in the table below. Asterisk (*) indicates sensors from which select NRT datasets are available through LANCE. Please note that this list includes only datasets that are part of NASA's Earth Observing System Data and Information System (EOSDIS) collection and is not meant to be an exhaustive list.
NASA/multi-national Satélite de Aplicaciones Científicas-D (SAC-D)
Aquarius passive microwave radiometers and active scatterometer
Sea Surface Salinity
Soil Moisture Active Passive (SMAP)
Radar (active sensor; no longer functional),
Microwave radiometer (passive sensor)
Soil Moisture: 9 km, 36 km
Sea Surface Salinity: 60 km
Sea Surface Salinity: 8 days
Soil Moisture: daily
Sea Surface Salinity, Soil Moisture
Atmospheric Infrared Sounder (AIRS) Level 2 and 3 products *
1° x 1°
daily, 8-day, monthly
Surface Air Temperature, Relative Humidity, Carbon Monoxide, Ozone
NASA/USGS Landsat 7
Enhanced Thematic Mapper (ETM)
15 m, 30 m, 60 m
NASA/USGS Landsat 8
Operational Land Imager (OLI)
Thermal Infrared Sensor (TIRS)
15 m, 30 m, 60 m
* sensors from which select NRT datasets are available in LANCE
COG: Available in Cloud Optimized GeoTiff format
NASA Model Data
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).
NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is an 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.
Land Data Assimilation System (LDAS)
Land Surface Temperature, Soil Moisture, Precipitation
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°
Use the Data
Remote sensing data are a valuable tool for mapping, monitoring, and predicting areas or regions at risk for disease outbreaks. 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 disease outbreaks, including malaria, Rift Valley fever, and COVID-19, and how changes in human behavior are having impacts on the environment:
In an effort to further explore the linkages and research/operational applications of NASA water resource data and disease, NASA's Global Precipitation Measurement (GPM) Disease Initiative launched an applications campaign. The website provides workshop recordings and training webinars.
NASA's Global Learning and Observations to benefit the Environment (GLOBE) Program's Zika Education and Prevention Project has enlisted thousands of students, teachers, and communities to collect data on mosquitoes for a global mapping project and connect with community public health officials to disseminate educational information. The GLOBE Mosquito Habitat Mapper app allows citizen scientists to train in the GLOBE Mosquito Protocol, identify disease-carrying mosquitoes, eliminate their breeding sites, and help prevent future Zika and other mosquito-borne disease outbreaks. For more details on the application and protocol view the GLOBE Observer Mosquito Habitat Mapper StoryMap.
CHIKRisk provides a platform for monitoring chikungunya activity worldwide along with climate-based risk maps for chikungunya occurrence.
GeoHealth Community of Practice is a global network of governments, organizations, and observers. It uses environmental observations to improve health decision-making at the international, regional, country, and district levels. One area of research is infectious disease applications.
The Group on Earth Observations (GEO) advocates the value of Earth observations, engages communities, and delivers data and information in support of public health surveillance by providing insight into the threat of vector-borne and environmentally-linked diseases and taking into account the impacts of climate change.
WHO has developed a Malaria Threats Map that tracks biological challenges to malaria control and elimination.
The EarlY WArning System for Mosquito borne diseases (EYWA) is a prototype system for European countries that addresses the critical public health need for prevention and protection against mosquito-borne diseases. It provides information on reported cases and prevalence of the Culex, Aedes, and Anopheles mosquito genera.
Earthdata Search is a tool for discovering and accessing Earth observation data collections from the EOSDIS collection as well as from U.S. and international agencies across Earth science disciplines.
Users (including those without specific knowledge of the data) can search for and read about data collections, search for data files by date and spatial area, preview browse images, and download or submit requests for data files (with customization for select data collections).
In the project area, for some datasets, you can customize your granule. You can reformat the data and output as HDF, NetCDF, ASCII, KML, or a GeoTIFF. You can also choose from a variety of projection options. Lastly, you can subset the data, obtaining only the bands that are needed.
HDF and NetCDF files can be viewed using NASA's Panoply application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.
Panoply informational videos are available on NASA's YouTube channel:
Files in HDF and NetCDF format can be viewed using NASA's Panoply application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.
Panoply informational videos are available on NASA's YouTube channel:
Giovanni is a NASA online environment for displaying and analyzing geophysical parameters. There are many options for analysis. The following are the more popular ones:
Time-averaged maps are a simple way to observe the variability of data values over a region of interest.
Map animations can be used to observe spatial patterns and detect unusual events over time.
Area-averaged time series are a way to display the value of a data variable that has been averaged from all the data values acquired for a selected region for each time step.
Histogram plots display the distribution of values of a data variable in a selected region and time interval.
For more detailed tutorials:
Giovanni How-To's on NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) YouTube channel.
Data recipe for downloading a Giovanni map in NetCDF format and converting data to quantifiable map data in the form of latitude-longitude-data value ASCII text.
The NASA Worldview visualization application provides the capability to interactively browse almost 1,000 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing Earth as it looks "right now." This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA's Global Imagery Browse Services (GIBS).
Worldview also includes nine geostationary imagery layers from the GOES-East, GOES-West, and Himawari-8 satellites that are available at 10-minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables the differentiation between air mass types (e.g., dry air, moist air, etc.). These full-disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.
AppEEARS, from LP DAAC, offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AppEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. Two types of sample requests are available: point samples for geographic coordinates and area samples for spatial areas via vector polygons. For help using AppEEARS, check out the LP DAAC AppEEARS E-Learning tutorials.
Performing Area Extractions
After choosing to request an area extraction, you will be taken to the Extract Area Sample page where you specify a series of parameters that are used to extract data for your area(s) of interest.
Define your region of interest in one of three ways:
Upload a vector polygon file in shapefile format (you can upload a single file with multiple features or multipart single features). Files in .shp, .shx, .dbf, or .prj format must be zipped into a file folder to upload.
Upload a vector polygon file in GeoJSON format (can upload a single file with multiple features or multipart single features).
Draw a polygon on the map by clicking on the bounding box or polygon icons (single feature only).
Select the date range for your time period of interest.
Specify the range of dates for which you wish to extract data by entering a start and end date (MM-DD-YYYY) or by clicking on the Calendar icon and selecting a start and end date in the calendar.
Adding Data Layers
Enter the product short name (e.g., MOD09A1, ECO3ETPTJPL), keywords from the product long name, a spatial resolution, a temporal extent, or a temporal resolution into the search bar. A list of available products matching your query will be generated. Select the layer(s) of interest to add to the selected layers list. Layers from multiple products can be added to a single request. Be sure to read the list of available products available through AppEEARS.
Selecting Output Options
Two output file formats are available:
If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF4 is selected, outputs will be grouped into files in .nc format by product and by feature.
Interacting with Results
From the Explore Requests page, click the view icon to view and interact with your results. This will take you to the View Area Sample page.
The Layer Stats plot provides time series boxplots for all of the sample data for a given feature, data layer, and observation. Each input feature is renamed with a unique AppEEARS ID (AID). If your feature contains attribute table information, you can view the feature attribute table data by clicking on the information icon to the right of the feature dropdown. To view statistics from different features or layers, select a different aid from the feature dropdown and/or a different layer of interest from the layer dropdown.
Be sure to check out the AppEEARS documentation to learn more about downloading the output GeoTIFF or NetCDF4 files.
Soil Moisture Visualizer
NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC) has developed a Soil Moisture Visualizer tool (read about it at Soil Moisture Data Sets Become Fertile Ground for Applications) that integrates a variety of different soil moisture datasets over North America. The visualization tool incorporates in-situ, airborne, and remote sensing data into one easy-to-use platform. This integration helps to validate and calibrate the data and provides spatial and temporal data continuity. It also facilitates exploratory analysis and data discovery for different groups of users. The Soil Moisture Visualizer offers the capability to geographically subset and download time series data in .csv format. For more information on the available datasets and use of the visualizer view the Soil Moisture Visualizer Guide.
To use the visualizer, select a dataset of interest under the data heading. Depending on the dataset chosen, the visualizer provides the included latitude/longitude or an actual site location name and relative time frame of data collection. Upon selecting the parameter, the tool displays a time series with available datasets. All measurements are volumetric soil moisture. Surface soil moisture is the daily average of measurements at 0-5 cm depth, and root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth. The tool also provides data sources for download.
With the Global Subset Tool, you can request a subset for any location on Earth, provided in GeoTiff and text format, including interactive time-series plots and more. Specify a site by entering the site's geographic coordinates and the area surrounding that site (from one pixel up to 201 x 201 km). From the available datasets, you can specify a date and then select from MODIS Sinusoidal Projection or Geographic Lat/Long. You will need to register for an Earthdata Login to request data.
With the Fixed Subsets Tool, you can download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products. The goal of the Fixed Sites Subsets Tool is to prepare summaries of selected data products for the community to characterize field sites. It includes sites from networks such as National Ecological Observatory Network, Forest Global Earth Observatory network, Phenology Camera network, and Long Term Ecological Research Network.
With the Web Service, you can retrieve subset data (in real-time) for any location(s), time period, and area programmatically using a REST web service. Web service client and libraries are available in multiple programming languages, allowing integration of subsets into a workflow.
Spatial Data Access Tool (SDAT)
ORNL DAAC's SDAT is an Open Geospatial Consortium (OGC) standards-based web application to visualize and download spatial data in various user-selected spatial/temporal extents, file formats, and projections. Datasets including land cover, biophysical properties, elevation, and selected ORNL DAAC archived data are available through SDAT. Files in KMZ format are also provided for data visualization in Google Earth.
Within SDAT, select a dataset of interest. Upon selection, the map service will open displaying the various measurements with the associated granule and a visualization of the selected granule.
You can then select your spatial extent, projection, and output format for downloading.
SeaDAS, available at OB.DAAC, is a comprehensive software package for the processing, display, analysis, and quality control of ocean color data. While the primary focus of SeaDAS is ocean color data, it is applicable to many satellite-based Earth science data analyses.
SeaDAS allows you to visualize data and re-project, crop, and create land, water, and coastline masks as well as perform mathematical and statistical operations, such as plotting histograms and creating scatter plots.
In-situ data can be incorporated as well; this is critical for data validation. To integrate in-situ data, the file must be tab-delimited with fields of data, time, station (with the stations defined in the file), latitude, longitude, and depth. Date needs to be defined as YYYYMMDD and time as HH:MM:SS.
Once the tab-delimited file is complete, you can select Vector/Import and then select your data source. Remember, in order to validate your remotely sensed data, you only want to look at the in-situ data at the surface (depth of 0).
For more detailed tutorials:
SeaDAS Video tutorials and demos
OB.DAAC recommends viewing the first few in the order they are shown. The core videos are listed first, followed by multi-tool case studies; everything below that appears in chronological order by release date.
Frequently asked questions from SeaDAS users.
Trace gases and particulate matter aggravate respiratory conditions. Air quality issues are preventable, but prevention requires a knowledge of where vulnerable populations exist. NASA datasets on aerosol optical depth, dust, nitrogen dioxide, and population information can help address these issues.
Many diseases, such as Rift Valley fever, cholera, chikungunya, and dengue are generally found in tropical regions with poor water quality and sanitation. NASA's ocean color and sea surface salinity datasets can aid in our understanding of regional water quality conditions.
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, such as looking at nighttime lights, changes in pollution, etc.