Disasters Data Pathfinder
Natural disasters affect millions of people every year. According to the United Nations Office for Disaster Risk Reduction (UNDRR), there were roughly 7250 disasters between 1998 and 2017, killing over 1.3 million people. Of those, flooding and storms account for the greatest number of disasters, while earthquakes cause the largest number of deaths, close to 750,000 people.
Understanding the vulnerability and exposure of a community (defined in About the Data) to a disaster aids in the mitigation, prevention, and management of the disaster, while also providing information to help with response and relief efforts.
Disaster Data Pathfinders:
Cyclones is the first of a planned series of Disaster Data Pathfinders. Coming soon, we will offer pathfinders on earthquakes and volcanoes, extreme heat, floods, and landslides.
About the Data
NASA, in collaboration with other organizations (the National Oceanic and Atmospheric Administration [NOAA], the U.S. Geological Survey [USGS], and the European Space Agency), has various instruments that provide information for understanding a number of phenomena that cause disasters, including flooding, cyclonic storms, earthquakes, volcanic eruptions, landslides, and extreme heat events. NASA also provides socioeconomic datasets to help assess the exposure and vulnerability of a community to one of these disasters.
Exposure is the presence of people, animals and ecosystems, environmental resources, infrastructure, or economic, social, and cultural assets in places and settings that could be adversely affected by a disaster. Vulnerability is the propensity to be adversely affected by a disaster, taking into consideration factors such as susceptibility to harm and lack of capacity to cope and adapt. Risk, therefore, is determined not only by the hazards, but also by the exposure and vulnerability to these hazards. Understanding each of these components is critical to disaster risk management efforts and adaptation strategies.
NASA’s Earth science data products are validated, meaning the accuracy has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts. For more information on this process, view NASA’s data maturity levels.
Datasets referenced in this pathfinder are from sensors shown in the table below, with their spatial and temporal resolutions (alphabetized by sensor name). When available, NASA’s Land, Atmosphere Near real-time Capability for Earth Observing System (LANCE) provides data to the public within 3 hours of satellite overpass, which allows for near real-time (NRT) monitoring and decision making.
|Measurement||Satellite/Platform||Sensor||Spatial Resolution||Temporal Resolution|
|Clouds||NASA/NOAA Geostationary Operational Environmental Satellite-East (GOES-East) and GOES-West||Advanced Baseline Imager (ABI)||1 km||10 min|
|Clouds||Japan Meteorological Agency Himawari-8||Advanced Himawari Imager||1 km||10 min|
|Surface Kinetic Temperature, Topography||Terra||Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)||15 m Very Near Infrared (VNIR), 30 m short-wave infrared (SWIR), 90 m thermal infrared (TIR)||Variable|
|Relative Humidity||Aqua||Atmospheric Infrared Sounder (AIRS) *||1°||daily, monthly|
|Surface Reflectance||Landsat 7||Enhanced Thematic Mapper (ETM)||15, 30, 60 m||16 days|
|Active Fire and Thermal Anomalies, Cloud Top Temperature, Land Surface Temperature, Surface Reflectance, Sea Surface Temperature, Vegetation Indices||Terra and Aqua||Moderate Resolution Imaging Spectroradiometer (MODIS) *||250 m, 500 m, 1000 m, 5600 m||1-2 days|
|Land Surface Temperature, Surface Reflectance||Landsat 8||Operational Land Imager (OLI)
Thermal Infrared Sensor (TIRS)
|15, 30, 60 m||16 days|
|Sulfur Dioxide||Aura||Ozone Monitoring Instrument (OMI) *||13 km x 24 km||daily|
|Sulfur Dioxide||Joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP)||Ozone Mapping and Profiler Suite (OMPS) *||50 km x 50 km||101 minutes, daily|
|Freeze/Thaw, Soil Moisture||Soil Moisture Active Passive (SMAP)||Radar (active) and microwave radiometer (passive)||9 km, 36 km||3 days|
|Elevation/Topography||Space Shuttle||Shuttle Radar Topography Mission (SRTM)||30 m||Static|
|Surface Reflectance||Sentinel-1 and -2||Synthetic Aperture Radar (SAR)||25 x 40 m, 5 x 5 m, and 5 x 20 m||12 days (using together 6 days)|
|Precipitation||Integrated multi-satellite data||TRMM Multi-satellite Precipitation Algorithm (TMPA) and Integrated Multi-satellite Retrievals for GPM (IMERG)||0.1° x 0.1° or 0.25° x 0.25°||half hourly, daily, monthly|
|Sulfur Dioxide||Sentinel 5-P||TROPOspheric Monitoring Instrument (TROPOMI)||7 km x 3.5 km||daily|
|Fire/Thermal Anomalies, Land Surface Temperature, Nighttime Imagery, Sea Surface Temperature, Surface Reflectance, Vegetation Indices||Suomi NPP||Visible Infrared Imaging Radiometer Suite (VIIRS) *||500 m, 1000 m, 5600 m||daily|
|* sensors from which select datasets are available in LANCE
Note: this is not an exhaustive list but rather only includes datasets with NASA's Earth Observing System Data and Information System (EOSDIS)
In addition to mission data, NASA has a series of models that use satellite- and ground-based observational data to produce high-quality fields of land surface states and fluxes. There are a few reasons model data may be preferred over remote sensing observations, including obtaining more complex data parameters, temporal coverage, spatial coverage, and/or data completeness. Models are often used for projections and forecasts, but time is not the only dimension in which projections can be made. Models can also project in space, offering data where sensors are unavailable.
For instance, satellite observations of land surface temperatures can only be made where there is a clear view of the land. Clouds and dust can obscure views, and observations are further dependent on the type of land cover, so highly reflective areas, such as snow and urban areas, can be challenging to observe. A model, however, can bring in additional data from ground stations, or other sensors that measure different wavelengths, to fill those gaps.
The Goddard Earth Observing System, Version 5 (GEOS-5) model assimilates data from a variety of observations for each Earth System component. GEOS-5 has a series of weather maps which can be used to produce a 240-hour/10-day forecast of parameters, such as precipitation, humidity, wind speed, and temperature.
The Land Data Assimilation System (LDAS) provides data within a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates of runoff and evapotranspiration.
The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. Climate, for example, is typically measured over a 30-year period, and so MERRA-2 can be used to make quantitative points about changes in climate.
|Model Source||Data Parameter||Spatial Resolution||Temporal Resolution|
|GEOS-5||Land surface temperature, soil moisture, surface humidity, winds||0.3125° x 0.25°||NRT|
|LDAS||Land surface temperature, runoff, surface humidity, soil moisture||0.25° x 0.25°||Monthly, daily, hourly|
|MERRA-2||Land surface temperature, surface humidity, winds, soil moisture||0.5° x 0.667°||Monthly, daily, hourly|
Other NASA Assets of Interest
The Precipitation Measurement Missions (PMM) provides additional information for specific disaster-related applications focused on cyclones, landslides and floods which use PMM data.
NASA’s Applied Sciences Disasters program promotes the use of Earth observations to improve the prediction of, preparation for, response to, and recovery from natural and technological disasters. The Disasters program is supported by NASA scientists representing many data products relevant to natural hazards, including floods, earthquakes, volcanoes, and landslides. This ensures that there is a robust connection between the researchers involved in developing hazard-relevant products and the end users who could benefit from them. Disaster applications and applied research on natural hazards support emergency preparedness leaders in developing mitigation approaches, such as early warning systems, and providing information and maps to disaster response and recovery teams. Explore maps and data at the Disasters Mapping Portal.
Short-Term Prediction Research and Transition Center (SPoRT) is a project that transitions experimental/quasi-operational observations and research capabilities to the operational weather community to improve short-term forecasts on a regional scale.GeoGateway is a web map-based science gateway that expands the utility of NASA’s geodetic imaging data. GeoGateway provides tools for scientific discovery, field use, and disaster response using Interferometric SAR (InSAR) and Global Positioning Systems (GPS) integrated with earthquake faults datasets, seismicity data, and models.
SARVIEWS Hazard Portal is a SAR-based hazard monitoring service funded by NASA’s Applied Sciences program and based at the Alaska Satellite Facility Distributed Active Archive Center (ASF DAAC). It is a fully automatic processing system that produces value-added products in support of monitoring natural disasters. The SARVIEWS processor is implemented in the Amazon Cloud and utilizes modern processing technology to generate geocoded and fully terrain-corrected image time series, as well as interferometric SAR data over areas affected by natural disasters.
It is important to note that NASA enables the conduct of research activities. The agency does not do disaster operations. Many of the research capabilities developed at NASA, however, have been transitioned to operational agencies, such as NOAA, their National Hurricane Center, the USGS, and other agencies, many of which are included below.NOAA has numerous resources that can aid in disaster planning and response. The National Centers for Environmental Information (NCEI) track and evaluate climate events in the U.S. and globally that have great economic and societal impacts. NCEI is frequently called upon to provide summaries of global and U.S. temperature and precipitation trends, extremes, and comparisons in their historical perspective.
- U.S. Billion-Dollar Disaster Events Time Series
NOAA Climate Maps
Provides maximum, minimum, and mean; also get the difference from average and browse data going back and forth in month and year.
The Federal Emergency Management Agency Flood Map Service Center (MSC) is the official public source for flood hazard information produced in support of the National Flood Insurance Program. Use the MSC to find your official flood map, access a range of other flood hazard products, and take advantage of tools for better understanding flood risk.
Global Flood Monitoring System at the University of Maryland is a NASA-funded experimental system using real-time TMPA and IMERG precipitation information as input to a quasi-global (50°N - 50°S) hydrological runoff and routing model. The models output time series graphs and visualizations of flood detection, streamflow, surface water storage, and inundation variables at 1 km resolution.
USGS’s National Landslide Hazards Program aims to reduce long-term losses from landslide hazards by improving our understanding of the causes of ground failure and suggesting mitigation strategies. They provide a web-based interactive map with a consistent set of landslide data from a variety of agencies at the local, state, and federal level.
USGS’s Earthquakes Map provides locations, date/time, and depth of recent earthquakes around the U.S., with filtering capabilities to include temporal options of one-day or the past 30 days and magnitude options of 2.5 or greater or 4.5 or greater.
The National Weather Service Heat Index provides a chart incorporating both temperature and relative humidity. It also has Heat Safety Tips and Resources for dealing with and preparing for excessive heat conditions.
The UNDRR put out the Sendai Framework for Disaster Risk Reduction, which promotes the "...substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries."
Benefits and Limitations of Remote Sensing Data
In determining whether or not to use remote sensing data, it is important to understand not only the benefits but also the limitations of the data. Benefits of using satellite data include:
- Filling in data gaps: the United States is fortunate to have numerous ground-based measurements for assessing water storage, precipitation, and more. However, this is not the case in other countries and even in some of the more remote areas of the United States. Satellite data provide local, regional, and global spatial coverage and is also useful for observing areas that are inaccessible.
- Monitoring in near-real time: some satellite information is available 3-5 hours after observation, allowing for a faster response.
With satellite data, assessments can be made regarding the land surface, precipitation events, ground movement and air temperature. In addition, incorporating satellite data with in-situ data into modeling programs makes for a more robust and integrated forecasting system.
- Spatial resolution: while lower resolution data provide a more global view, as with the Aqua/Terra MODIS measurements, the spatial resolution is too coarse for certain assessments. This is not the case for instruments at higher resolutions, like those on Landsat.
- Temporal resolution: Many satellites only pass over the same spot on Earth every 1-2 days and sometimes as seldom as every 16+ days. This is the satellite’s return period.
- Spectral Resolution: Passive instruments (those that use energy being reflected or emitted from Earth for measurements) are not able to penetrate cloud or vegetation cover, which can lead to data gaps or a decrease in data utility. This is not the case when using data from microwave or thermal sensors (active sensors).
It is difficult to combine all of the desirable features into one remote sensor; to acquire observations with moderate to high spatial resolution (like Landsat) a narrower swath is required, which in turn requires more time between observations of a given area resulting in a lower temporal resolution. Researchers have to make trade-offs. Finding a sensor with the spatio-temporal resolution capable of addressing your research, application, or decision-making process needs is a crucial first step to getting started with using remote sensing data.
Published May 11, 2020
Page Last Updated: May 21, 2020 at 11:45 AM EDT