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Diseases Data Pathfinder

Aedes aegypti mosquitoes carry several tropical diseases, including chikungunya, dengue, Zika, and yellow fever. They are recognized by white markings on their legs. (Image courtesy of CDC/James Gathany.) From: https://earthobservatory.nasa.gov/features/disease-vector

Aedes aegypti mosquitoes carry several tropical diseases, including chikungunya, dengue, Zika, and yellow fever. They are recognized by white markings on their legs. For more information, see NASA's Earth Observatory. Image courtesy of CDC/James Gathany.

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.

About the Data

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.

Platform Sensor Spatial Resolution Temporal Resolution Measurement
Aura Ozone Monitoring Instrument (OMI) * 13 km x 24 km 1-2 days Aerosol Optical Depth, Nitrogen Dioxide (COG), Ozone, UV Radiation
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)
NASA/NOAA 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
Terra Measurements of Pollution in the Troposphere (MOPITT) * 1° x 1° daily, monthly Carbon Monoxide
NASA/German Space Agency (DLR) Gravity Recovery and Climate Experiment (GRACE) 0.125° Giovanni: daily
Earthdata: 7-day
Groundwater
International Space Station
Note: data are available in areas between 51.6° S to 51.6° N latitude
Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) 70 m ~ 1-7 days Land Surface Temperature, Evapotranspiration
Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 15 m, 30 m, 90 m Variable Land Surface Temperature, Surface Reflectance
ESA (European Space Agency) Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) 7 km x 3.5 km daily Nitrogen Dioxide, Carbon Monoxide, Ozone, UV Radiation
ESA Sentinel-3 Ocean and Land Color Instrument (OLCI) 300 m 2 days Ocean Color
Japan Aerospace Exploration Agency 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
Global Precipitation Measurement (GPM) Integrated multi-satellite data Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Algorithm (TMPA)
Integrated Multi-satellite Retrievals for GPM (IMERG)
0.1° x 0.1° or 0.25° x 0.25° half-hourly, daily, monthly Precipitation
NASA/multi-national Satélite de Aplicaciones Científicas-D (SAC-D) Aquarius passive microwave radiometers and active scatterometer 7 days 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
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
NASA/USGS Landsat 7 Enhanced Thematic Mapper (ETM) 15 m, 30 m, 60 m 16 days Surface Reflectance
NASA/USGS Landsat 8 Operational Land Imager (OLI)
Thermal Infrared Sensor (TIRS)
15 m, 30 m, 60 m 16 days Surface Reflectance

* 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.

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° daily, monthly

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Use the Data

Use the Data
Scientists use data from satellites and ground stations to predict the spread of chikungunya, a mosquito-borne viral disease, in a new project called CHIKRisk. The model forecasted an elevated risk of chikungunya for July 2020 in India, Mexico, Indonesia, Malaysia, and Philippines.

Rift Valley fever risk map and outbreaks from 2006-2011 across Africa. Credit: Assaf Anyamba, NASA's Goddard Space Flight Center.

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:

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Find Air and Water Quality Data

Find Air and Water Quality Data

Air Quality

Many diseases, such as COVID-19, asthma, and other upper respiratory illnesses, are exacerbated by air pollution. Trace gases and particulate matter, for example, aggravate respiratory conditions. Air quality issues are preventable, but prevention requires a knowledge of where vulnerable populations exist and what interventions are needed in those communities. Using observations of trace gases, dust, and other airborne particulate matter combined with socioeconomic data can help do just that.

For more information about aerosol optical depth, trace gas data, and pollutant transport data, please see the Health and Air Quality Data Pathfinder.

Aerosol Optical Depth (AOD) | AOD to PM2.5 | Nitrogen Dioxide (NO2) | Aerosol Index | Dust Score

Aerosol Optical Depth (AOD)

Screenshot showing 2 world maps displaying burning fires and a second map showing average monthly aerosol amounts.

Locations of burning fires (top image) compared to average monthly aerosol optical depth (bottom image).

AOD is a column-integrated value of aerosols in the atmosphere obtained by measuring the scattering and absorption of solar energy from the top of the atmosphere to the surface. The non-aerosol signal of surface reflectance needs to be separated from the aerosol signal to accurately obtain an AOD. This is challenging because the satellite instrument cannot penetrate cloud cover and highly reflective surfaces, such as ice or snow. This, in turn, can lead to misrepresentations in the data. To address this, scientists developed algorithms for data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS): the Dark Target algorithm and the Deep Blue algorithm. In the latest MODIS dataset collection, these two algorithms have been merged, using the highest quality for each.

The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite also collects AOD data, but at a finer spatial resolution. VIIRS uses the Deep Blue (DB) algorithm over land and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water to determine atmospheric aerosol loading for daytime cloud-free, snow-free scenes. Downloading a VIIRS data file will provide data with just the land algorithm, just the ocean algorithm, and the merged algorithm. As with all remote sensing data, make sure you are choosing the best product for your area and use.

Data Products for Measuring AOD

Research quality data products can be accessed using Earthdata Search (data are in HDF or NetCDF format, and can be opened using NASA'S Panoply application):

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive NASA data analysis tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type; for more information on choosing a type of plot, see the Giovanni User Manual. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

  • OMI AOD in Giovanni
    The Ozone Monitoring Instrument (OMI) aboard the Aura satellite has a coarser spatial resolution than MODIS and VIIRS, but provides data at individual wavelengths from the ultraviolet (UV) to the visible. Within Giovanni, you can plot daily data at these individual wavelengths. This is important because pollutants have different spectral signatures; for example, a wavelength range around 400 nm can be used to detect elevated layers of absorbing aerosols such as biomass burning and desert dust plumes. The two AOD products provided through Giovanni use two different algorithms: OMI Multi-wavelength (OMAERO) and OMI UV (OMAERUV). OMAERO is based on the multi-wavelength algorithm and uses up to 20 wavelength bands between 331 nm and 500 nm. This algorithm uses reflectances for a wide variety of microphysical aerosol models representative of desert dust, biomass burning, volcanic, and weakly absorbing aerosol types. OMAERUV uses the near-UV algorithm, which is capable of retrieving aerosol properties over a wider variety of land surfaces than is possible using measurements only in the visible or near-IR.
  • MODIS AOD in Giovanni
    Provides data products with both the Dark Target and Deep Blue algorithms as well as the combined Dark Target/Deep Blue algorithm at daily and monthly intervals.

Near real-time data, which are available generally within three hours of a satellite observation through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE), can be visualized and interactively explored using NASA Worldview:

  • MODIS Aqua/Terra Combined Algorithm AOD
    The merged Dark Target/Deep Blue AOD layer provides a more global, synoptic view of AOD over land and ocean. It is available from 2000 to present.
  • VIIRS Level 2 Deep Blue Aerosol Product
    The product uses the Deep Blue algorithm over land and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water to determine atmospheric aerosol loading. The product is designed to facilitate continuity in the aerosol record. Deep Blue uses measurements from multiple Earth observing satellites to determine the concentration of atmospheric aerosols along with the properties of these aerosols.
  • OMI AOD Multi-wavelength and UV
    The multi-wavelength layer and the UV absorbing layer displays the degree to which airborne particles (aerosols) prevent the transmission of light through the process of absorption (attenuation). The UV extinction layer indicates the level at which aerosols prevent light from traveling through the atmosphere. Toggling between these layers can help distinguish the types of aerosols present.

AOD to PM2.5

As mentioned above, AOD is the quantity of light removed from a beam by scattering or absorbing during its path through a medium and is a unitless measure. PM2.5, on the other hand, is a measure of the mass of particles in a specific size range (generally 2.5 micrometers and smaller) within a given volume of air near the surface. Particles less than 10 micrometers in diameter pose a significant health threat because they can get deep into lungs, and some may even get into the bloodstream, according to the U.S. Environmental Protection Agency (EPA). There are a few differences between AOD and PM2.5:

  • AOD is an optical measurement; PM2.5 is a mass concentration measurement.
  • AOD is an integrated column measurement from the top of the atmosphere to the surface; PM2.5 is a ground measurement.
  • AOD is an area-averaged measurement; PM2.5 is a point measurement.

Because the two measurements are so different, it may seem that there is no correlation. However, they do correlate and there are several techniques to convert AOD to PM2.5. It is important to note that while there is a relationship between AOD and PM2.5, there are other factors that can affect AOD, like humidity, the vertical distribution of aerosols, and the shape of the particles. For example, an increase in humidity will increase the size of particles and therefore increase the AOD even though the PM2.5 level will be the same.

Ground-based AOD measurements are available online through the Aerosol Robotic Network (AERONET). The EPA's ground-based PM and Ozone combined Air Quality Index (AQI) can be accessed at AirNow.

NASA's Applied Remote Sensing Training (ARSET) program has a Jupyter Notebook available through the ARSET GitHub site that accesses VIIRS AOD data and converts AOD to PM2.5. For more information on using this notebook, view the ARSET MODIS to VIIRS Transition for Air Quality Applications.

For trends in PM2.5, there are several resources that utilize both ground-based and remote sensing data:

Nitrogen Dioxide (NO2)

Nitrogen Dioxide (NO2) is a pollutant that can aggravate respiratory conditions in humans, especially those with asthma, leading to an increase of symptoms, hospital admissions, and emergency visits. The primary sources of NO2 are fossil fuel burning, automobile exhaust, and industry emissions. Long-term exposure can lead to the development of asthma and potentially increase susceptibility to respiratory infections. NO2 reacts with other chemicals in the atmosphere, forming particulate matter and ozone, producing haze and acid rain, and contributing to nitrogen pollution in coastal waters. The NASA Air Quality site provides more information on NO2, as well as trend maps and pre-made images of NO2 over cities and power plants.

Research quality data products can be accessed using Earthdata Search:

  • OMI NO2 data
    The OMI sensor aboard the Aura spacecraft provides daily gridded and non-gridded products at 13x24 km resolution; data are in HDF5 format (Hierarchical Data Format Release 5) and can be opened using NASA's Panoply application. A tutorial on using OMI NO2 data is available as a PDF and a webinar on Analyzing NO2 data within Java and Excel is available from the Earthdata YouTube website.
    TROPOspheric Monitoring Instrument​ tropospheric vertical column of nitrogen dioxide​ data opened in NASA tool, Panoply

    TROPOspheric Monitoring Instrument tropospheric vertical column of nitrogen dioxide data opened using NASA's Panoply application. The red circle indicates a change needed in the scaling factor, due to the very small numbers.

  • TROPOMI NO2 data
    The TROPOspheric Monitoring Instrument (TROPOMI) is aboard the ESA (European Space Agency) Sentinel 5 satellite. ESA's TROPOMI NO2 data website provides additional information on this Level 2 data product. Data are in NetCDF format, and can be opened using Panoply. Because of the very small values in tropospheric vertical column of NO2, you will need to change the scaling factor in Panoply (see image from June 2018 at right). 

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive NASA data analysis tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type; for more information on choosing a type of plot, see the Giovanni User Manual. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

Near real-time data can be visualized and interactively explored using NASA Worldview:

NASA also has a global nitrogen dioxide monitoring site that provides imagery of daily NO2 from OMI.

Aerosol Index

The Aerosol Index (AI) is a measurement related to AOD and indicates the presence of an increased amount of suspended particles in the atmosphere. High concentrations of aerosols can exacerbate conditions like asthma, bronchitis, and other respiratory conditions. The main aerosol types included in the AI are desert dust, large fire events, biomass burning, and volcanic ash plumes. The lower the AI, the clearer the sky.

Global image of atmospheric aerosols with sources highlighted in different colors.

This image shows how tropical cyclones (light blue areas on right side of map near Japan), dust storms (purple areas over Africa, the Middle East, and Asia), and fires (red areas over North America and Africa) contributed to suspended airborne particles throughout the atmosphere on 23 August 2108. Source: NASA Earth Observatory.

Research quality data products can be accessed using Earthdata Search:

  • OMI AI
    OMI provides an Ultraviolet Aerosol Index; data are in HDF5 format and can be opened using NASA's Panoply application. Note that when opening the data in Panoply, there are a number of different data fields from which to choose. Select UVAerosolIndex.
  • TROPOMI AI data
    ESA TROPOMI AI provides additional information on this Level 2 data product. Data are in NetCDF format, and can be opened using Panoply.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive NASA data analysis tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type; for more information on choosing a type of plot, see the Giovanni User Manual. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

Near real-time data can be visualized and interactively explored using NASA Worldview:

Dust Score

The Dust Score indicates the level of atmospheric aerosols. The numerical scale is a qualitative representation of the presence of dust in the atmosphere, an indication of where large dust storms may form, and the areas that may be affected. Near real-time data can be visualized and interactively explored using NASA Worldview:

  • AIRS Dust Score
    Measurement from the AIRS Infrared quality assurance subset; the imagery resolution is 2 km.

Water Quality

Ocean Color | Sea Surface Salinity

Vibrio cholerae—the bacteria that cause cholera—can live in the guts of microscopic aquatic animals and can lurk in the ocean for months to years. When the right environmental conditions arise, the bacteria can infiltrate water supplies and spread disease to people. (Scanning electron microscope image courtesy of Kirn et al., Dartmouth Medical School, via Wikimedia Commons.)

Vibrio cholerae—the bacteria that cause cholera—can live in the guts of microscopic aquatic animals and can lurk in the ocean for months to years. When the right environmental conditions arise, the bacteria can infiltrate water supplies and spread disease to people. (Scanning electron microscope image courtesy of Kirn et al., Dartmouth Medical School, via Wikimedia Commons.)

Many diseases, such as Rift Valley fever (RVF), cholera, chikungunya, and dengue are generally found in tropical regions with poor water quality and sanitation coupled with limited access to health care services. For example, cholera is contracted from consuming water or food contaminated with the the toxic bacterium Vibrio cholerae. According to the U.S. Centers for Disease Control and Prevention (CDC), an estimated 2.9 million cases of cholera and 95,000 deaths from the disease occur each year around the world.

Cholera occurs in two forms: endemic and epidemic. The endemic form of cholera occurs during the dry season, when freshwater river levels are low and saltwater can more easily penetrate into coastal areas. The extra salt provides a good habitat for the growth of algae, which draws in small crustaceans called copepods that feed on the floating vegetation and are a vector for cholera. This brings copepods closer to sources of water used for drinking, sanitation, and bathing. The epidemic form of cholera, in contrast, occurs suddenly and sporadically. These outbreaks typically occur after a disaster such as when flooding contaminates clean water sources or damages water infrastructure.

The Water Quality Data Pathfinder has additional information on the integration of ground-based data with satellite or airborne data for assessing water quality.

Ocean Color

Chesapeake Bay and surroundings, mosaic of 5 Landsat images taken in October and November 2014.

Chesapeake Bay and surroundings, mosaic of five Landsat images taken in October and November 2014. Credit: USGS.

Measurements of ocean color can provide important information about water quality and the presence of organisms that could carry disease. Ocean color is a measure of the absorption and scattering of light by particles in the water column, such as phytoplankton, sediments, and colored dissolved organic matter (CDOM). The primary satellites used for measuring ocean color from space are the joint NASA/USGS Landsat series of satellites, NASA's Terra and Aqua satellites, the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP), and ESA's (European Space Agency) Sentinel missions. Each of these satellites has sensors acquiring data at different spatial, temporal, spectral, and radiometric resolutions (for detailed information on how satellite-borne sensors collect these data, see What is Remote Sensing?).

In addition to ocean color, sea surface temperature (SST) is a valuable parameter in evaluating the growth of algal blooms. Depending on the species of cyanobacteria involved, toxins in these blooms can affect the central nervous system (neurotoxins), the liver (hepatotoxins), and other systems.

The inherent optical properties (IOP) file in MODIS and VIIRS data provides an estimate of reflectance by CDOM. Specifically, the adg_443_giop is the absorption coefficient of non-algal material plus CDOM. For more information on the algorithm used to generate this product and others, see Algorithm Descriptions at NASA's Ocean Biology DAAC (OB.DAAC).

Research-quality data products can be accessed through NASA partner websites, Earthdata Search, or OB.DAAC:

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive NASA data analysis tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type; for more information on choosing a type of plot, see the Giovanni User Manual. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

  • Aqua MODIS Chlorophyll-a Concentration data
    Data products from MODIS on the Aqua satellite at 4 km resolution provided at both 8-day and monthly temporal resolutions.
  • Aqua MODIS SST data
    Data products from MODIS on the Aqua satellite at 4 km resolution provided at both 8-day and monthly temporal resolutions.

Near real-time data can be visualized and interactively explored using NASA Worldview:

Sea Surface Salinity (SSS)

SMAP SSS image of the Gulf of Mexico and Western Atlantic Ocean.

Soil Moisture Active Passive (SMAP) SSS image for the Gulf of Mexico, Caribbean, and Western Atlantic Ocean for 25 June 2021 visualized using the State of the Ocean (SOTO) tool from NASA's Physical Oceanography DAAC (PO.DAAC). SSS is measured in Practical Salinity Units (PSUs), with yellow and red colors indicating higher PSU values. The averaged salinity in the global ocean is 35.5 PSU. NASA PO.DAAC/JPL image.

Salinity, the amount of salt dissolved in seawater, drives ocean currents that transport heat around the globe. Drinking water with high saline concentrations has been identified as an increasing public health concern due to its contributions to cardiovascular and other diseases. Two missions have been measuring SSS since 2011: the international Aquarius/Satélite de Aplicaciones Científicas (SAC)-D observatory, which carries the NASA Aquarius instrument, and NASA's Soil Moisture Active Passive (SMAP).

Aquarius Version 5 is the official end-of-mission dataset, spanning the complete period of Aquarius science data availability from August 2011 to June 2015. Improving the accuracy of Aquarius' measurements has been a key mission activity to ensure that the data are most useful for science and society. There are two products: the official release and another from JPL based on the Combined Active Passive (CAP) retrieval algorithm.

While SMAP is designed to measure soil moisture over land, algorithm development from the Aquarius mission is applied to SMAP data to derive SSS. There are two SMAP SSS products, one from NASA's Jet Propulsion Laboratory (JPL) and one from a private company called Remote Sensing Systems (RSS). The JPL product is based on the CAP retrieval algorithm and provides a comparative view to RSS, which is based on averages spanning an 8-day moving window.

Note that the Aquarius data are only available from 2011–2015 and are at a much coarser spatial resolution than SMAP. The SMAP data record begins in March/April 2015.

Research-quality SSS data products can be accessed using Earthdata Search:

For subsetting SSS data, use the HiTIDE Tool available through NASA's Physical Oceanography DAAC (PO.DAAC) (see the Tools for Data Access and Visualization section for more information)

SSS data can be visualized using Worldview and PO.DAAC's State of the Ocean (SOTO) tool:

Locations of the Salinity Processes in the Upper Ocean Regional Study (SPURS) field-based campaigns.

Locations of the Salinity Processes in the Upper Ocean Regional Study (SPURS) field-based campaigns. Credit: NASA's Jet Propulsion Laboratory (JPL)

Field-based campaigns provide SSS data on a regional level. Salinity Processes in the Upper Ocean Regional Study (SPURS) is a pair of oceanographic field experiments using a variety of equipment and technology, including salinity-sensing satellites, research cruises, floats, drifters, autonomous gliders, and moorings. The 2012–2013 SPURS-1 field campaign in the North Atlantic focused on a high salinity, high evaporation region. The 2016–2017 SPURS-2 field campaign is the center of the low surface salinity belt associated with the heavy rainfall of the intertropical convergence zone in the Tropical Pacific.

Saildrone is a state-of-the-art, wind-and-solar-powered Uninhabited Surface Vehicle (USV) capable of long distance deployments lasting up to 12 months. This novel sampling platform is equipped with a suite of instruments and sensors providing high quality, georeferenced, near real-time, multi-parameter surface ocean and atmospheric observations.

Research-quality field-based salinity data can be accessed using Earthdata Search:

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Find Habitat Suitability Data

Find Habitat Suitability Data

his image shows the risk of encountering Ixodes scapularis, the tick species that carries Lyme disease. Colors indicate the Risk of Ixodes Scapularis (RIS) in Central and Eastern Canada from 2000 to 2015, with red indicating a high risk of encountering this tick species. Image from Kotchi, S.O., et al., 2021 (doi:10.3390/rs13030524).

This image shows the risk of encountering Ixodes scapularis, the tick species that carries the bacteria causing Lyme disease. Colors indicate the Risk of Ixodes Scapularis (RIS) in Central and Eastern Canada from 2000 to 2015, with red indicating a high risk of encountering this tick species. Image from Kotchi, S.O., et al., 2021 (doi:10.3390/rs13030524).

Vector-borne diseases are human illnesses caused by viruses and bacteria transmitted from mosquitoes, fleas, ticks, and similar sources. According to WHO, diseases such as dengue, malaria, chikungunya, yellow fever, and Zika cause more than 700,000 deaths per year and disproportionately affect the poorest populations in tropical regions. Lyme disease is one of the primary vector-borne diseases in the temperate region of the North Hemisphere. The distribution of vector-borne diseases is determined by a complex set of demographic, environmental, and social factors. Also, the expansion of these diseases into other areas is accelerating as the global climate warms and habitats change. For more details on vector-borne diseases, please see Of Mosquitoes and Models: Tracking Disease by Satellite.

Scientists can predict vector distributions by identifying key environmental characteristics of suitable species habitats, such as areas of standing water that are breeding grounds for mosquito larvae. Measurements from NASA, including land use/land cover, precipitation, temperature, and vegetation indices, can help identify potentially suitable vector habitats.

Land Cover Type | Land Surface Temperature | Vegetation Greenness | Precipitation | Relative Humidity | Soil Moisture

Land Cover Type

MODIS Land Cover Type as seen in the visualization tool Worldview.

MODIS Land Cover Type as seen in the NASA Worldview data visualization application.

Land cover type can provide information about potential larval habitats. Different mosquito species are often associated with different land cover characteristics. In the Amhara region of Ethiopia, for example, lowland pastures are important breeding sites for anopheline mosquitoes; as a result, landscapes with a high proportion of these wetlands have higher incidences of malaria.

The Terra and Aqua MODIS Land Cover Type data product provides global land cover types at yearly intervals that are derived from six different classification schemes (the MODIS Land Cover User Guide provides additional information on these schemes). The product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes.

Land Surface Temperature

Land surface temperature is useful for monitoring changes in weather and climate patterns that can impact the suitability of an area for a specific species to live and function. For example, there are different optimum temperature ranges for the transmission of different mosquito-borne diseases in various ecological contexts.

Satellite images show the relationship between the characteristics of a landscape, and day and night surface skin temperature. Heavily forested areas remain relatively cool throughout the day, while barren and arid areas can be tens of degrees warmer. These images were acquired in the early morning and afternoon of July 6, 2011.

Satellite images show the relationship between the characteristics of a landscape and day (middle image) and night (bottom image) surface skin temperature (top image is a natural color image). Heavily forested areas remain relatively cool throughout the day, while barren and arid areas can be significantly warmer. These images were acquired in the early morning and afternoon of July 6, 2011. Credit: NASA Earth Observatory

Research quality land surface temperature data products can be accessed directly from Earthdata Search (these data also are available through NASA's Land Processes DAAC [LP DAAC] Data Pool). MODIS and ASTER data are available in HDF format while data from VIIRS and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) are available as HDF5:

To quickly extract a subset of ECOSTRESS, MODIS, or VIIRS data for your region of interest, use LP DAAC's AppEEARS tool or NASA's Oak Ridge National Laboratory DAAC's (ORNL DAAC) subsetting tools.

Landsat data can be discovered using Earthdata Search (you will need a USGS Earth Explorer login to download Landsat data):

Data can be visualized and interactively explored using Worldview:

Vegetation Greenness

Screenshot of Normalized Difference Vegetation Index of King Fire area of burn.

False-color image of Normalized Difference Vegetation Index (NDVI) data of King Fire area, September 2013 (left) and Nov 2014. (ORNL DAAC)

Vegetation indices are a proxy measure of green vegetation over a given area and can be used to assess vegetation health. Vegetation indices, in particular the Normalized Difference Vegetation Index (NDVI), can be used to describe habitat suitability for different species of disease-carrying vectors, such as mosquitoes. NDVI uses the difference between near-infrared (NIR) and red reflectance divided by their sum. NDVI values range from -1 to 1. Low values of NDVI generally correspond to barren areas of rock, sand, exposed soils, or snow; higher NDVI values indicate greener vegetation, including forests, croplands, and wetlands. The enhanced vegetation index (EVI) is another widely used vegetation index that minimizes canopy-soil variations and improves sensitivity over areas of dense vegetation.

Vegetation products produced from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA's Aqua and Terra satellites and by the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership satellite can be accessed in various ways.

Research-quality data products can be accessed directly using Earthdata Search (these data also are available through the LP DAAC Data Pool). Datasets are available as HDF files (in some cases they are customizable to GeoTIFF):

LP DAAC's Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and effective way to extract, transform, visualize, and download MODIS and VIIRS vegetation-related data products. AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest, and output data can be downloaded in csv (point), GeoTIFF (area), or NetCDF-4 (area) format. Explore LP DAAC's Getting Started with Cloud-Native Harmonized Landsat Sentinel (HLS) Data in Python Jupyter Notebook for extracting an EVI Time Series from HLS. ORNL DAAC subsetting tools provide a means to simply and efficiently access and visualize MODIS and VIIRS vegetation-related data products, as well.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive NASA data analysis tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type; for more information on choosing a type of plot, see the Giovanni User Manual. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

Vegetation indices can be visualized and interactively explored in Worldview:

  • MODIS NDVI
    This dataset has a spatial resolution of 250 m and a temporal resolution of eight days. 16-day and monthly data are also available through Worldview.
  • MODIS EVI
    This dataset is monthly at 1 km spatial resolution. Rolling 8-day and 16-day data are also available through Worldview.

Precipitation

Near real-time IMERG Early Run Half-Hourly Image, acquired on November 12, 2019.

Near real-time IMERG Early Run Half-Hourly Image, acquired on May 7, 2020. Credit: NASA.

Precipitation is the ultimate source of water for the aquatic habitats of mosquito larvae. However, the direct effects of precipitation on larval survival are highly varied and are not always positive. For example, heavy rain can cause flooding, which can result in high levels of larval mortality. In addition, the effects of rainfall on breeding habitats are highly contingent upon local conditions, such as soil saturation, topography, and land use.

NASA's Precipitation Measurement Missions (PMM) provide a continuous long-term record (over 20 years) of precipitation data through the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. GPM, a TRMM follow-on mission, provides more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

TRMM and GPM products are available individually and have been integrated with data from a global constellation of satellites to yield improved spatial/temporal precipitation estimates providing a temporal resolution of 30 minutes (in the case of GPM). The integrated products are the TRMM Multi-satellitE Precipitation Analysis (TMPA) and the Integrated Multi-satellite Retrievals for GPM (IMERG). IMERG's multiple runs accommodate different user requirements for latency and accuracy (Early = 4 hours, e.g., for flash flood events; Late = 12 hours, e.g., for crop forecasting; and Final = 3 months, with the incorporation of rain gauge data, for research).

NASA, in collaboration with other agencies, has developed precipitation models that incorporate satellite information with ground-based data. These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS uses inputs including precipitation, soil texture, topography, and leaf area index to create model output estimates.

Science quality data products can be accessed using Earthdata Search:

  • TMPA
    Rainfall estimate at 3 hours, 1 day, or near real-time (NRT) and accumulated rainfall at 3 hours and 1 day. Data are in HDF format and can be opened using NASA's Panoply application. Data are available from 1997.
  • IMERG
    Early, Late, and Final precipitation data on the half hour or 1-day timeframe. Data are in NetCDF or HDF format and can be opened using Panoply. Data are available from 2000.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive NASA data analysis tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type; for more information on choosing a type of plot, see the Giovanni User Manual. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

Near real-time data can be accessed using Worldview:

  • IMERG Precipitation Rate
  • AMSR2 Precipitation Rate
    The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument collects data that indicate the rate at which precipitation is falling on the ocean surface and is measured in millimeters per hour (mm/hr).
  • NLDAS Precipitation Total
  • GLDAS Precipitation Total (only goes through 2010)
    The NLDAS monthly Precipitation Total data are generated through temporal accumulation of the hourly data, and the GLDAS monthly data are generated through temporal averaging of the 3-hourly data. The data are in kg/m2/s which is equivalent to mm/s.

In addition, the IMERG Early Run Half-Hourly product is available through NASA's PMM website. This product is generated every half hour with a 6-hour latency from the time of data acquisition

Daymet is a collection of gridded estimates of daily weather parameters, and is available through ORNL DAAC. It is modeled on daily meteorological observations. Weather parameters in Daymet include daily minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii.

Daymet data can be retrieved using Earthdata Search, an ORNL DAAC API, ORNL DAAC tools, and through LP DAAC AppEEARS.

Relative Humidity

Relative humidity at 2 m above the surface from MERRA-2 visualized in the Prediction of Worldwide Energy Resources Data Access Viewer. The graphs display percent relative humidity for the single point over South Carolina.

Relative humidity at 2 m above the surface from MERRA-2 visualized in the Prediction of Worldwide Energy Resources (POWER) Data Access Viewer. The graphs display percent relative humidity for the single point over South Carolina.

For diseases transmitted by vectors without an aquatic development stage, such as ticks and sandflies, relative humidity exerts a strong influence on vector survival. In general, both very low and very high temperatures increase tick mortality rates; however, an increase in humidity can increase the ability of ticks to tolerate higher temperatures. In addition, temperature coupled with humidity can also influence the timing of host-seeking activity and can influence the seasons of highest risk to the public, according to the U.S. Global Change Research Program.

Research-quality data products can be accessed using Earthdata Search:

  • AIRS Relative Humidity
    Data from the Atmospheric Infrared Sounder (AIRS) instrument are available daily at 1 degree, and Level 3 data products are provided in either the descending (equatorial crossing north to south at 1:30 a.m. local time) or ascending (equatorial crossing south to north at 1:30 p.m. local time) orbit. Note that the data were acquired only until 2016.
  • MERRA-2 Humidity
    Humidity data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis product are available in several options: 1-hourly, 3-hourly, 6-hourly. These options provide information on surface specific humidity, specific humidity at 2 m, and relative humidity.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

Soil Moisture

Aedes mosquitoes are often found near dambos—natural, shallow depressions that become flooded during periods of abundant rainfall. Such habitats are common in eastern and southern Africa and are good breeding grounds for mosquitoes.

Aedes mosquitoes are often found near dambos, which are natural, shallow depressions that become flooded during periods of abundant rainfall. Dambos are common in eastern and southern Africa and are good breeding grounds for mosquitoes. Credit: Assaf Anyamba, NASA GSFC

Soil moisture is important for understanding conditions that, along with rainfall, can contribute to ideal breeding habitats for vectors with aquatic larval stages. For example, many mosquito species lay their eggs in moist soil. The eggs will hatch when rain saturates the ground and water levels begin to rise.

Current ground measurements of soil moisture are sparse and have limited coverage; satellite data help fill in those gaps. On the other hand, satellite data are limited by their relatively coarse resolution. Utilizing a combination of ground-based and satellite-acquired data provides spatial and temporal data continuity.

NASA's Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top 5 cm of soil globally, every 2–3 days, at a resolution of 9–36 km. NASA, in collaboration with other agencies, has also developed models of soil moisture content, incorporating satellite information with ground-based data when available. These models are part of NASA's Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS uses inputs including precipitation, soil texture, topography, and leaf area index to model output estimates of soil moisture and evapotranspiration.

Science quality data products can be accessed using Earthdata Search (SMAP datasets are available as HDF5 files which are also customizable to GeoTIFF):

Soil moisture as visualized with ORNL DAAC Soil Moisture Visualizer. The map shows a flight path over Arizona in 2013. In the graph, AirMoss rootzone soil moisture data is plotted with SMAP rootzone soil moisture. Root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth.

Soil moisture as visualized using the ORNL DAAC Soil Moisture Visualizer. The map shows a flight path over Arizona in 2013. In the graph, AirMoss rootzone soil moisture data is plotted with SMAP rootzone soil moisture. Root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth.

ORNL DAAC's Soil Moisture Visualizer integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. LP DAAC's AppEEARS offers another option to simply and efficiently extract subsets, transform, and visualize SMAP data products.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

Data from NASA's SMAP mission can be visualized and interactively explored using Worldview:

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Find Seasonality Data

Find Seasonality Data

An endemic disease is a disease that has a cyclical or seasonal recurrence because the bacteria or viruses that cause the disease are constantly present in the environment, even if only at low levels. For example, seasonal flu viruses can occur at any time of the year and in any environment, but are most commonly found in the fall and winter seasons when temperature and humidity are lower. NASA temperature and humidity data enable scientists to track when environmental conditions might be favorable for disease outbreaks. In addition, NASA ultraviolet (UV) radiation datasets provide information about exposure to solar radiation that can lead to skin cancer, cataracts, and other health issues. 

Air Temperature

Surface air temperature, measured in Kelvin, from the Atmospheric Infrared Sounder (AIRS), May 9, 2020, visualized in Panoply.

Surface air temperature, measured in Kelvin, from the Atmospheric Infrared Sounder (AIRS), May 9, 2020, visualized in Panoply.

Research-quality air temperature data products can be accessed using Earthdata Search:

  • AIRS Surface Air Temperature
    Data from the Atmospheric Infrared Sounder (AIRS) aboard the Aqua satellite are available as daily, 8-day, and monthly at 1 degree. Level 3 data products are provided in either the descending (equatorial crossing north to south at 1:30 AM local time) or ascending (equatorial crossing south to north at 1:30 PM local time) orbit. When you open the HDF file (in a program like Panoply or QGIS), you will see an ascending option and a descending option each with SurfAirTemp.
  • MERRA-2 Temperature
    There are several options available: hourly, 3-hourly, 6-hourly. These options provide information on surface skin temperature, air temperature at 2 m, and air temperature at 10 m.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

Air temperature data can be visualized and interactively explored using Worldview:

Humidity

Relative humidity at 2 m above the surface from MERRA-2 visualized in the Prediction of Worldwide Energy Resources Data Access Viewer. The graphs display percent relative humidity for the single point over South Carolina.

Relative humidity at 2 m above the surface from MERRA-2 visualized in the Prediction of Worldwide Energy Resources (POWER) Data Access Viewer. The graphs display percent relative humidity for the single point over South Carolina.

Research-quality data products can be accessed using Earthdata Search:

  • AIRS Relative Humidity
    AIRS data are daily at 1 degree; Level 3 data products are provided in either the descending (equatorial crossing north to south at 1:30 AM local time) or ascending (equatorial crossing south to north at 1:30 PM local time) orbit. When you open the HDF file (in a program like Panoply or QGIS), you will see an ascending option and a descending option each with RelHumSurf.
  • MERRA-2 Humidity
    There are several options available: hourly, 3-hourly, 6-hourly. These options provide information on surface specific humidity, specific humidity at 2 m, and relative humidity.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

Data can be visualized and interactively explored using Worldview:

NASA's Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides visualizations of temperature and humidity at 2 m.

Ultraviolet Radiation

Time Averaged Map of Irradiance at a wavelength of 310 nm (Local Noon) for June 12, 2020 in . Data are from the Ozone Monitoring Instrument (OMI) aboard the Aura spacecraft.

Time Averaged Map of Irradiance at a wavelength of 310 nm (Local Noon) for June 12, 2020 in . Data are from the Ozone Monitoring Instrument (OMI) aboard the Aura spacecraft.

Ultraviolet (UV) radiation from the Sun has always played an important role in our environment, and affects nearly all living organisms. Overexposure to UV radiation can lead to serious health issues, including cataracts and other eye damage, immune system suppression, and cancer, according to the Environmental Protection Agency (EPA).

The Ozone Monitoring Instrument (OMI) aboard the Aura spacecraft and the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5P satellite provide measurements of solar irradiance at various wavelengths, including UV. OMI data are within UV wavelengths between 305 to 380 nm. TROPOMI data are within UV to shortwave infrared (SWIR) wavelengths. TROPOMI Bands 1 (270 to 300 nm), 2 (300 to 320 nm), and 3 (320 to 405) provide specific measurements for UV wavelengths. UV radiation that reaches Earth's surface is in wavelengths between 290 and 400 nm, with UV-A in wavelengths of 320 to 400 nm and UV-B in wavelengths of 290 to 320 nm.

Research quality data products can be accessed using Earthdata Search:

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool called Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

UV index at local solar noon and UV erythemal daily dose at local solar noon can be visualized and interactively explored using Worldview:

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Find Environmental Impacts Data

Find Environmental Impacts Data
Aerosol optical depth (AOD) measurements over India, March 31 to April 5, for each year from 2019 through 2020. The last map (anomaly) shows how AOD in 2020 compared to the average for 2016-2019.

Aerosol optical depth (AOD) measurements over India from March 31 to April 5 for 2019 (left map) and 2020 (middle map). The 2020 Anomaly map (right map) shows how AOD in 2020 compared to the average AOD for 2016-2019. Credit: Earth Observatory.

To counter the onset and rapid spread of diseases, quarantine and social distancing measures may be implemented. With COVID-19, for example, air traffic nearly ceased, non-essential businesses closed, and the number of people on the road was much lower than normal. This drastic shift in human behavior led to environmental changes that were detected by sensors aboard orbiting satellites, such as decreases in nitrogen dioxide (NO2) and a resulting decrease in tropospheric ozone concentrations.

NASA has numerous Earth science datasets that are used to monitor environmental impacts such as air quality, water quality, biological and land cover changes, and other environmental components. Datasets to aid in monitoring these can be found in the Find Air and Water Quality Data and Find Habitat Suitability Data sections. In addition, ESA (European Space Agency), the Japan Aerospace Exploration Agency (JAXA), and NASA collaborated on a tri-agency COVID-19 Dashboard that combines the resources, technical knowledge, and expertise of the three partner agencies to strengthen our global understanding of the environmental and economic impacts of the COVID-19 pandemic.

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Find Socioeconomic Data

Find Socioeconomic Data

NASA's Socioeconomic Data and Applications Center (SEDAC) has datasets related to population density and size, urban extent, land use and land cover, and poverty. Additional datasets related to the location of U.S. hazardous waste and Superfund sites also are available, and can be used to identify populations near these areas and assess their potential risk. 

Another resource available through SEDAC is the 2020 Environmental Performance Index (EPI), which ranks 180 countries on 32 performance indicators in policy categories including air quality, sanitation and drinking water, and waste management. The EPI uses a methodology that facilitates cross-country comparisons among economic and regional peer groups.

SEDAC's Global COVID-19 Viewer shows demographic data along with regularly updated information about reported global cases of the disease. The COVID-19 Viewer enables users to visualize age and sex data for any area, including areas cutting across country boundaries, through simple age-and-sex structure charts and age pyramids.

Screenshot from COVID-19 Viewer showing map of New York City overlain with two data boxes: one shows COVID-19 metrics as of the time and day of the image; the other shows a demographic table of the area highlighted in the base map.

Screenshot from the SEDAC Global COVID-19 Viewer showing COVID-19 statistics for Passaic, New Jersey, as of 17 June 2021 along with 2010 age distribution data for the selected area. COVID-19 data are updated daily. COVID-19 data: Johns Hopkins University & Medicine Coronavirus Resource Center. Population data: SEDAC’s Gridded Population of the World (GPW) Basic Demographic Characteristics, v4.11, Center for International Earth Science Information Network (CIESIN) Columbia University. 2018. NASA SEDAC.

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Other NASA Assets

Other NASA Assets
Mosquitoes Habitat Mapper visualized through the Globe Visualization System. The maps indicate the number of habitat data counts and genera data counts from citizen scientists. Credit: NASA Globe

Mosquito Habitat Mapper visualized using the GLOBE Visualization System. The maps indicate the number of observations made about mosquito breeding sites, habit, and other variables provided by citizen scientists and schools. Credit: NASA GLOBE.

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 has enlisted thousands of students, teachers, and communities to collect data on mosquitoes through the Mosquito Habitat Mapper tool on the GLOBE program app, GLOBE Observer (more information about this app is available on a Google Story Map). The Mosquito Habitat Mapper allows citizen scientists to train in the GLOBE Mosquito Protocol, identify disease-carrying mosquitoes, eliminate mosquito breeding sites, and help prevent future outbreaks of Zika and other mosquito-borne diseases. Through the U.S. Department of State, the 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. For more details on the application and protocol view the GLOBE Observer Mosquito Habitat Mapper StoryMap. GLOBE is an international science and education program that provides students and the public opportunities to meaningfully contribute to our understanding of the Earth system and the global environment. NASA is the lead agency for GLOBE, with the U.S. Department of State, NOAA, and the National Science Foundation (NSF) as partners.

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External Resources

External Resources
Monitoring insecticide resistance in malaria vectors is essential. 80 of 89 malaria-endemic countries reported monitoring for insecticide resistance between 2010 and 2017. The extent and quality of data varies between countries. This map shows the status for these countries and whether insecticide resistance has been confirmed, is possible, or is susceptible. Credit: World Health Organization

Monitoring insecticide resistance in malaria vectors is essential. 80 of 89 malaria-endemic countries reported monitoring for insecticide resistance between 2010 and 2017. The extent and quality of data varies between countries. This map shows the status for these countries and whether insecticide resistance has been confirmed, is possible, or is susceptible. Credit: World Health Organization

CDC's National Environmental Public Health Tracking Network brings together health and environmental data from national, state, and city sources and provides supporting information to make the data easier to understand. 

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.

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Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AppEEARS | Soil Moisture Visualizer | MODIS/VIIRS Subsetting Tools Suite | Spatial Data Access Tool (SDAT) | SeaDAS

Earthdata Search

Earthdata Search is a tool for discovering and accessing Earth observation data collections from NASA's Earth Observing System Data and Information System (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).

Screenshot of the Search Earthdata site.

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.

Earthdata Search customization tools diagram.

Panoply

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:

Giovanni

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 as NetCDF and converting data to quantifiable map data in the form of latitude-longitude-data value ASCII text.

Worldview

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.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. Post-hurricane image shows widespread outages around San Juan, including key hospital and transportation infrastructure.

Worldview Suomi NPP/VIIRS nighttime lights comparison image showing power outages caused by Hurricane Irma in September 2017. The right image (acquired 1 September 2017) shows the island before Hurricane Irma. The left image (acquired 9 September 2017) shows power outages across island after Hurricane Irma. NASA Worldview image.

AppEEARS

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.

Spatial Subsetting

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). The .shp, .shx, .dbf, or .prj files 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.

Extracting an area in AppEEARS

Selecting Output Options

Two output file formats are available:

  • GeoTIFF
  • NetCDF-4

If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF-4 is selected, outputs will be grouped into .nc files by product and by feature.

If GeoTIFF is selected, you must select a projection

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.

Interpreting Results in AppEEARS

Be sure to check out the AppEEARS documentation to learn more about downloading the output GeoTIFF or NetCDF-4 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.

ORNL DAAC Soil Moisture Visualizer

The Soil Moisture Visualizer allows users to compare soil moisture measurements from multiple sources (figure legends, top left and bottom right) at the same location. In this screenshot, Level 4 Root Zone Soil Moisture (L4 RZSM) data from NASA’s Soil Moisture Active Passive (SMAP) Observatory are shown with data from in situ sensors across the 9-kilometer Equal-Area Scalable Earth (EASE) grid cell encompassing the Tonzi Ranch Fluxnet site in the Sierra Nevada foothills of California. Daily precipitation values for the site (purple spikes) are also provided for reference.

MODIS/VIIRS Subsetting Tools Suite

ORNL DAAC also has several MODIS and VIIRS Subset Tools for subsetting data.

  • With the Global Subset Tool, you can request a subset for any location on Earth, provided as GeoTiff and in 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 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 NEON, ForestGeo, PhenoCam, and LTER.
  • 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.

Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

Top image: The Global Subsets Tool enables users to download available products for any location on Earth. Bottom image: The Fixed Sites Subsets Tool provides spatial subsets for established field sites for site characterization and validation of models and remote sensing products.

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. KMZ files 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.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

You can then select your spatial extent, projection, and output format for downloading.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

SeaDAS

SeaDAS, available at NASA's Ocean Biology DAAC (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 is a comprehensive software package for the processing, display, analysis, and quality control of ocean color data. This image shows ocean color, sea surface temperature and non-algal material plus colored dissolved organic matter.

SeaDAS is a comprehensive software package for the processing, display, analysis, and quality control of ocean color data. This image shows ocean color, sea surface temperature and non-algal material plus colored dissolved organic matter.

SeaDAS processing components (OCSSW)

SeaDAS processing components (OCSSW)

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).

SeaDAS allows for the integration of in-situ data in order to validate satellite measurements.

SeaDAS allows for the integration of in-situ data in order to validate satellite measurements.

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.
  • SeaDAS FAQs
    Frequently asked questions from SeaDAS users.

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Published July 15, 2021

Page Last Updated: Jul 20, 2021 at 4:07 PM EDT