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Wildfires Data Pathfinder

New to using NASA Earth data? This pathfinder is designed to help guide you through the process of selecting and using applicable datasets, with guidance on resolutions and direct links to the data sources.

After getting started here, there are numerous NASA resources that can help develop your skills further. If you are new to remote sensing, check out What is Remote Sensing? or view the Applied Remote Sensing Training on Fundamentals of Remote Sensing.

A prescribed fire is applied to a Pinus nigra stand in Portugal. (Courtesy P. Fernandes)
A prescribed fire is applied to a Pinus nigra stand in Portugal. (Courtesy P. Fernandes)

According to the Fourth National Climate Assessment, climate change has made its appearance, afflicting the world with extreme weather conditions, including increased heat waves, rising sea levels, and increasing devastation from wildfires, to name just a few. NASA is interested in all of these issues, particularly for fires because of their environmental, societal, and economic impacts. How are changes in climate boosting the increase in fire activity across the globe and what can be done to mitigate the impacts?

About the Data

About the Data

A combination of ground- and satellite-based data provides a unique view of the globe to better understand active fires and hotspots, thermal anomalies, and aerosol transport of smoke and particulate matter. In addition, climatological and vegetative measurements help scientists, researchers, and decision makers prepare for risk and response to events as well as aiding in forecasting events and assessing the post-event impacts.

NASA, in cooperation with its partner agencies and organizations, has a series of instruments that provide data to assist with forecasting, monitoring and assessment. 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.

Scientists, researchers, land managers, and decision makers are using wildfire data in numerous ways (to see how the data is being used, check out the Data User Profiles section of the Wildfire Articles page). Wildfire data can be used to forecast events. By acquiring data on precipitation, soil moisture, drought severity, topography, land surface temperatures, and vegetation density and extent, land and/or wildlife managers can perform pre-fire mapping to indicate potential areas of risk. Near real-time wildfire data, exploring the estimated extent of total area burned and fire radiative power, can be used to assess risk in a given area and develop a more efficient strategy for response. Wildfire data can be used in post-fire mapping, incorporating total burned area, burn severity, and vegetation regrowth.

Satellite

Sensor

Spatial Resolution

Temporal Resolution

Aqua

Atmospheric Infrared Sounder (AIRS) Level 2 and 3 products

1° x 1°

daily, 8-day, monthly

Global Change Observation Mission – Water 1

Advanced Microwave Scatter Radiometer-2 (AMSR2)

2km

daily

Terra

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

30-meter spatial resolution and 1°x1° tiles


Terra and Aqua

Moderate Resolution Imaging Spectroradiometer (MODIS)

250m, 500m, 1km

1-2 days

Landsat 8

Operational Land Imager (OLI)

15, 30, 60m

16 days

Aura

Ozone Monitoring Instrument (OMI)

13km x 24km

daily

Suomi NPP

Ozone Mapping and Profiler Suite (OMPS)

50km x 50km

101 minutes, daily

Soil Moisture Active Passive (SMAP)

Radar (active) and a radiometer (passive)

10-40 km

3 days


Shuttle Radar Topography Mission (SRTM)

30m


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

Suomi NPP

Visible Infrared Imaging Radiometer Suite (VIIRS)

375-750m

1-2 days

Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview

Earthdata Search

Earthdata Search provides a means of accessing all of NASA’s Earth science data across all distributed active archive centers. It provides the only means to access all data regardless of where the data are archived. Within Earthdata Search, you can subset using temporal and geographic constraints. Some data can be customized once the data of interest are selected; to do this, add the data of interest to your project and then click download all.

Screenshot of the Search Earthdata site.

In the project area, you can select to 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 in Panoply, a cross-platform 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.

The National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) has an HDF to GeoTIFF conversion tool, which allows you to geolocate, subset, stitch, and regrid certain HDF-EOS datasets.

Giovanni

Giovanni is an online environment for the display and analysis of geophysical parameters. There are a few options for analysis.

  1. Time-averaged maps are a simple way to observe the variability of data values over a region of interest.
  2. Map animations are a means to observe spatial patterns and detect unusual events over time.
  3. Area-averaged time series are used 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.
  4. Histogram plots are used to display the distribution of values of a data variable in a selected region and time interval.

For more detailed tutorials:

Worldview

Worldview is a visualization tool to interactively browse global, full-resolution satellite imagery layers and then download the underlying data. Many of the imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now". View current natural hazards and events using the Events tab which reveals a list of natural events, including wildfires, tropical storms, and volcanic eruptions.

Worldview's Events tab provides information about events, such as tropical cyclones, wildfires, volcanic eruptions, and even large iceberg movement. Hurricane Barry, as shown in this image, traveled from the Gulf into Louisiana in July 2019.

Worldview's Events tab provides information about events, such as tropical cyclones, wildfires, volcanic eruptions, and even large iceberg movement. Hurricane Barry, as shown in this image, traveled from the Gulf into Louisiana in July 2019.

Animate the imagery over time. Do a screen by screen comparison of data for different time periods or a comparison of different datasets.

Hurricane Maria was a category 5 storm that devastated numerous places, most notably Puerto Rico, in September 2017. In the Woldview comparison, selecting a date pre-storm and then one post-storm shows the nighttime lights over the island, and how the storm affected electricity, even months after.

Hurricane Maria was a category 5 storm that devastated numerous places, most notably Puerto Rico, in September 2017. In the Woldview comparison, selecting a date pre-storm and then one post-storm shows the nighttime lights over the island, and how the storm affected electricity, even months after.


Find and Use Risk and Response Data - Near Real-time

Find and Use Risk and Response Data - Near Real-time

Aerosols/smoke plumes and transport
Near Real-Time Data

Aerosols/smoke plumes and transport

Photo of Camp Fire, 2018 from NNNN data.

Terra MODIS Corrected Reflectance imagery of Camp Fire, 2018.

Aerosols and suspended particles in the air that are made up of smoke and dust from burning fires can be tracked and measured by instruments aboard NASA Earth observing satellites. The NASA Worldview application provides the capability to interactively browse over 800 global, full-resolution satellite imagery layers and download the underlying data. Many of the imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now."

Using MODIS or VIIRS imagery, you can follow a smoke plume and its movement through time. You can also assess the aerosol optical depth (which can be converted to particulate matter with specific algorithms), carbon monoxide, and sulfur dioxide at the fire location, where and when available. Worldview also allows you to capture a snapshot, create an animated gif, compare imagery over time or compare two different imagery layers for the same date.

For a tutorial of Worldview, see the Earthdata webinar, Explore the Earth Every Day

Types of aerosol data provided in NRT:

Aerosol Index (AI)

Aerosols absorb and scatter incoming sunlight, which reduces visibility and increases the optical depth. Satellite-derived AI products are useful for identifying and tracking the long-range transport of smoke from wildfires or biomass burning events. Currently there are two satellite products measuring AI, the Aura Ozone Monitoring Instrument (OMI) and Suomi-NPP’s Ozone Mapping and Profiler Suite (OMPS). AI indicates the presence of ultraviolet (UV)-absorbing particles in the air (aerosols); the higher the AI, the higher the concentration in the atmosphere. For both satellites, the spatial resolution is 2 km and the temporal resolution is daily.

  • AI from OMPS in Worldview
    AI from OMPS includes a newer product, PyroCumuloNimbus, which makes it easier to track the extent and spread of pyroCb and other high-aerosol events. Typically the AI signal remains below 5.0 for most smoke and dust events, the OMPS AI product with an AI range of 0.0 to 5.0 satisfies the needs of most users. However, the AI signal for pyroCb events, which are both dense and high in the atmosphere, easily can be much larger than 5.0. In fact, the highest AI value ever observed (55.0) occurred during a pyroCb event in August 2017.

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 (above) compared to average monthly aerosol optical depth.

AOD indicates the level at which particles in the air (aerosols) prevent light from traveling through the atmosphere. From an observer on the ground, an AOD of less than 0.1 is “clean” - characteristic of clear blue sky, bright sun and maximum visibility. As AOD increases to 0.5, 1.0, and greater than 3.0, aerosols become so dense that the sun is obscured.

  • AERONET
    Ground-based AOD measurements are available online at the Aerosol Robotic Network (AERONET).
  • AOD MODIS Data in Worldview
    Aqua and Terra's Moderate Resolution Imaging Spectroradiometer Combined Value-Added Aerosol Optical Depth layer is a value-added layer based on MODIS Level 2 aerosol products. The layer can give a quick, synoptic view of the level of aerosols in the atmosphere.
  • Deep Blue AOD from MODIS in Worldview
    Deep Blue AOD layer is useful for studying aerosol optical depth over land surfaces. This layer is created from the Deep Blue (DB) algorithm.

Trace Gases from Fires

In addition to the particulate matter, numerous trace gases are found in the atmosphere during and after a fire event. These trace gases, like carbon monoxide (CO) and sulfur dioxide (SO2), are harmful pollutants that can impact public health. Data for trace gases are available from a variety of different satellites. For CO, the Atmospheric Infrared Sounder (AIRS) onboard the Aqua satellite provides the best global coverage at 2 km resolution and twice daily measurements (day and night). AIRS also provides measurements of SO2. Two other instruments, OMI and OMPS (descriptions above) provide information on SO2 at the lower troposphere, middle troposphere, and upper troposphere/stratosphere layers.

  • AIRS CO Total Column (Day/Night) in Worldview
    Indicates the amount of CO in the total vertical column profile of the atmosphere (from Earth’s surface to top-of-atmosphere) and is measured in parts per billion by volume (ppbv).
  • AIRS L2 CO (Day/Night) in Worldview
    Carbon monoxide in units of parts per billion by volume at the 500 hPa pressure level, approximately 5500 meters (18,000 feet) above sea level. AIRS Level 2 data is nominally 45 km/pixel at the equator and the data has been resampled into a 32 km/pixel visualization.
  • AIRS SO2 in Worldview
    Indicates sulfur dioxide column amounts in the atmosphere, measured in Dobson Units (DU).
  • OMI/OMPS SO2 in Worldview
    Indicates the column density of sulfur dioxide at different layers of the atmosphere and is measured in Dobson Units (DU). OMI data are available from 2005-present and OMPS from 2012-present.

Near Real-Time Data

NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) provides data to the public within three hours of satellite observation, which allows for near real-time (NRT) monitoring and decision making. Specifically for fires, both the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board the Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the joint NASA/NOAA Suomi-National Polar-orbiting Partnership (Suomi-NPP) satellite provide satellite derived fire information on hotspots/fires and thermal anomalies, and smoke plume movement via true color reflectance imagery.

There are some differences between the fire datasets that must be considered. Each of these instruments has a different spatial and temporal resolution. MODIS data are at 1km and are acquired daily (Terra satellite passes over in the morning and Aqua in the afternoon), whereas VIIRS are at 375m and are acquired daily, with improved nighttime performance over MODIS. The NOAA-20 VIIRS instrument, data to be made available soon, follows the same orbit as Suomi-NPP but lags behind by about 50 minutes.

Ground observation of active fires compared to output display for MODIS sensor.

Ground observations versus output display for MODIS sensor.

The thermal anomalies/fire NRT data are basically a snapshot in time, showing what is occurring at the moment the data was acquired. It is determined by a contextual algorithm that utilizes the infrared or thermal radiation of the fires. Each MODIS active fire represents the center of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel (see figure to right). Because VIIRS has a higher resolution, it can pick up fires that MODIS overlooks, especially those in relatively small areas.

It is important to note that the NRT products are not considered science quality because predicted geolocation is used. Science quality data, which are an internally consistent, well-calibrated record of the Earth’s geophysical properties to support science, are available with an approximate two to three months lag.

There are two primary ways of exploring NRT fire data through the Fire Information for Resource Management System (FIRMS): through an interactive map or by direct download of the NRT data. The interactive map provides NRT and the full archive of global MODIS and VIIRS fire locations. It also enables users to view the MODIS Terra/Aqua Global Burned Area data product (with an approximate 4-month lag between the date of burn and burned area data product in FIRMS).

Active fire data are available for download for any area of interest, in NRT and from the full archive.

Science quality, or higher-level “standard” data products can be accessed via Earthdata Search.

For a tutorial on using FIRMS data, see the webinar Discover NASA’s FIRMS on the Earthdata YouTube channel. For more in depth information about FIRMS read the FIRMS Frequently Asked Questions.

Find and Use Fire Forecasting Data

Find and Use Fire Forecasting Data

Screenshot of Normalized Difference Vegatation 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)

Many factors contribute to a fire, its intensity and its severity, including changing weather patterns, land cover, vegetative health, drought conditions, and changing land use or land management. As such, it’s important to monitor contributing factors in order to predict the formation of a fire and how it will move through the environment. NASA has several datasets for making these predictions.

Vegetation Greenness

Vegetation indices have been developed to measure the amount of green vegetation over a given area and can be used to assess vegetation health. One commonly used vegetation index is the Normalized Difference Vegetation Index (NDVI), which takes the difference between 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. Increasing NDVI values indicate greener vegetation, including things like forests, croplands, and wetlands. Aqua and Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi-NPP’s Visible Infrared Imaging Radiometer Suite (VIIRS) vegetation data products can be accessed via the following ways:

Science quality (higher-level “standard”) surface reflectance data products can be accessed directly via Earthdata Search; datasets are available as HDF files but are, in some cases, customizable to GeoTIFF.

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, 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.

NRT imagery can be accessed in Worldview.

Precipitation

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. The follow-on mission, GPM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

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

Science quality, or higher-level “standard” data products can be accessed via Earthdata Search:

  • IMERG from Earthdata Search
    Early, Late and Final precipitation data on the half hour or one day timeframe. Data are in NetCDF or HDF format and can be opened using Panoply. Data are available from 2000.
  • TMPA from Earthdata Search
    Rainfall estimate at 3 hours, 1 day or NRT and accumulated rainfall at three hours and one day. Data are in HDF format and can be opened using Panoply. Data are available from 1997.

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, 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.

  • IMERG from Giovanni
    Select a map plot, date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF. Data are only available from 2000.
  • TMPA from Giovanni
    Select a map plot, date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF.

Near real-time data can be accessed from Worldview. The NRT "early run" product at PMM is generated every half hour with a 6-hour latency from the time of data acquisition. 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 unit “kg/m2/s” which is equivalent to “mm/s”. In addition to IMERG and Land Data Assimilation System (< href="https://ldas.gsfc.nasa.gov/index.php" target="_blank">LDAS), the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument collects data that indicates the rate at which precipitation is falling on the surface of the ocean and is measured in millimeters per hour (mm/hr).

Land Surface Temperature

Land surface temperature is useful for monitoring changes in weather and climate patterns and used in wildfire risk assessment.

Science quality, or higher-level “standard” data products can be accessed via Earthdata Search; MODIS and VIIRS data sets are available as HDF files and can be opened using Panoply, but are also customizable to GeoTIFF:


NRT imagery can be accessed via Worldview:

Soil Moisture

Soil moisture is important in forecasting fire events as the dryness of the soil contributes to the overall fire potential in the area. Satellite data can provide a synoptic view of soil moisture across the globe. Although ground- based measurements are at a higher resolution, the data are often sparse and have limited coverage. The preferred measurement should be chosen based upon your needs.

New Smap Image 3 - 8 - 16
SMAP's rotating golden antenna functions like a satellite dish to focus radio waves from Earth's surface into a collector on the spacecraft. Image: NASA JPL/Caltech
NASA's Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top 5cm of the soil globally every three days, at a resolution of 10-40km. The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides a NRT product, which is a daily measurement of surface soil moisture.

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 the Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a US national collection (NLDAS).

Science quality, or higher-level “standard” data products can be accessed via Earthdata Search; MODIS and VIIRS datasets are available as HDF files and can be opened using Panoply, but are also customizable to GeoTIFF:

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, 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.

NRT imagery can be accessed via Worldview:

Topography

Knowing the topography of an area is important so that fire managers and emergency management professionals can anticipate areas of risk, including direction and speed of wind, landslide potential and runoff.

The Shuttle Radar Topography Mission (SRTM) provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of all Earth’s landmass. The ASTER Global Digital Elevation Model (ASTER GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. Elevation data are at a spatial resolution of 30m for both missions.

On average, as compared with geodetic points over the US, SRTM data has a lower root mean square error. However digital elevation model data accuracy is very sensitive to vegetation cover; ASTER performs better over certain landcover types.

Find and Use Post-Fire Impact Data

Find and Use Post-Fire Impact Data

The regional impacts of wildfires include not only burned area, but also changes in runoff patterns and landslide potential. While some areas allow for ground-based measurements of these post-fire impacts, areas that are remote or that have rugged terrain can make ground-based measurements impractical. Remotely-sensed data provide a means to extend our knowledge in these areas.

Total burned area

A combined MODIS burned area product is acquired by employing 500m MODIS Surface Reflectance imagery coupled with 1 km MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data.

Screenshot showing burned area in Earthdata FIRMS.

  • Burned Area Data from Earthdata Search
    Terra and Aqua combined Burned Area data product is a monthly, global gridded 500m product containing per-pixel burned-area and quality information.
  • MODIS Land Surface Reflectance from Earthdata Search
    Adding the Terra/MODIS Bands 7-2-1 layer provides more context; this layer is useful for distinguishing burn scars where burned vegetation shows as red and vegetation is bright green.
  • MODIS Corrected Reflectance Bands 7-2-1 in Worldview
    Worldview Tour provides stories to help you learn more about the visualization tool, the satellite imagery we provide and events occurring around the world. This story takes you on a tour of the Camp Fire event, November 8, 2018 north of Sacramento, California. An example of MODIS Corrected Reflectance, bands 7-2-1, is shown in slide 2.

Burn Severity

 Illustration showing fire intensity versus burn severity (Source: U.S. Forest Service).

Illustration of fire intensity versus burn severity (Source: U.S. Forest Service).

Burn severity is the effect of fire on ecosystem properties, often defined by the degree of mortality of vegetation (relating to soil heating, consumption of litter). Using satellite imagery from Landsat or MODIS allows for pre and post-fire comparisons and a change detection approach. One of the most effective ways to discriminate is by generating a normalized burn ratio. To calculate this, using Landsat data, see the Applied Sciences Remote Sensing Training on Introduction to Remote Sensing for Wildfire Applications.

Vegetation Density and Extent

Normalized Difference Vegetation Index (NDVI) provides a means to assess vegetation health in a given area. Very low values of NDVI (0.1 and below) correspond to barren areas of rock, sand, or snow. Moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical rainforests (0.6 to 0.8). Aqua and Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data can be accessed via the following:

Science quality, or higher-level “standard” data products can be accessed via Earthdata Search; datasets are available as HDF files which can be opened using Panoply or customizable to GeoTIFF.

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, 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.

  • MODIS NDVI from Giovanni
    Select a map plot, date range and region and then plot the data. Data can be downloaded as GeoTIFF.

NRT imagery can be accessed in Worldview.

Precipitation

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. The follow-on mission, GPM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

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

Science quality, or higher-level “standard” data products can be accessed via Earthdata Search:

  • IMERG from Earthdata Search
    Early, Late and Final precipitation data on the half hour or one day timeframe. Data are in NetCDF or HDF format and can be opened using Panoply. Data are available from 2000.
  • TMPA from Earthdata Search
    Rainfall estimate at 3 hours, 1 day or NRT and accumulated rainfall at three hours and one day. Data are in HDF format and can be opened using Panoply. Data are available from 1997.

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, 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.

  • IMERG from Giovanni
    Select a map plot, date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF. Data are only available from 2000.
  • TMPA from Giovanni
    Select a map plot, date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF.

Near real-time data can be accessed from Worldview. The NRT "early run" product at PMM is generated every half hour with a 6-hour latency from the time of data acquisition. 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 unit “kg/m2/s” which is equivalent to “mm/s”. In addition to IMERG and Land Data Assimilation System (< href="https://ldas.gsfc.nasa.gov/index.php" target="_blank">LDAS), the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument collects data that indicates the rate at which precipitation is falling on the surface of the ocean and is measured in millimeters per hour (mm/hr).

Topography

Knowing the topography of an area is important so that fire managers and emergency management professionals can anticipate areas of risk, including direction and speed of wind, landslide potential and runoff.

The Shuttle Radar Topography Mission (SRTM) provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of all Earth’s landmass. The ASTER Global Digital Elevation Model (ASTER GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. Elevation data are at a spatial resolution of 30m for both missions.

On average, as compared with geodetic points over the US, SRTM data has a lower root mean square error. However digital elevation model data accuracy is very sensitive to vegetation cover; ASTER performs better over certain landcover types.

Other NASA Assets of Interest

Other NASA Assets of Interest

Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) data include maps of fire extent and severity, estimates of carbon emissions from fires, and other measurements.

Earthdata has tutorials for utilizing the monthly burned area and thermal anomalies/fire data from MODIS Terra/Aqua. Tutorials are also provided from the Earthdata YouTube channel.

External Fire-Related Resources

Screenshot of the Global Wildfire Information System's Current Situation Viewer The Global Wildfire Information System is a joint initiative of the Group on Earth Observations and the Copernicus Programs. The Global Wildfire Information System (GWIS) aims to bring together existing information sources, including NASA’s MODIS and VIIRS data, to provide a comprehensive view and evaluation of fire regimes and fire effects at a global level.

  • GWIS Current Situation Viewer
    Viewer provides a fire danger forecast as well as a rapid damage assessment, which includes active fires, burnt area, and fire emissions (sulfur dioxide, nitrogen oxides, and particulate matter). The viewer also allows for some basic statistical analysis.

NOAA also maintains several fire and smoke products from the geostationary orbiting satellites, which typically have a much coarser spatial resolution but more frequent observations.

Benefits and Limitations of using Satellite Data

Benefits and Limitations of using Satellite Data

The United States is fortunate to have numerous ground-based measurements for assessing water storage and atmospheric particulate matter. However, this is not the case in other countries and in more rural areas of the United States. Satellite data provides more regional to global spatial coverage; some of the information is available in near real-time allowing for more efficient disaster response. With satellite data, comparisons can be made using pre-event and post-event imagery, providing information on smoke and ash transport, burn severity, vegetation loss, and so much more. Incorporating satellite data with in-situ data (ground-based measurements) in modeling programs makes for a more robust forecasting system.

While the data provides a more global view, the coarse spatial resolution makes it difficult to detect small fires or field level events. In addition, polar-orbiting satellites only pass over the same spot every 1-2 days or sometimes every 16+ days, providing for a much coarser temporal resolution not adequate to monitor the dynamics of a burning fire. Passive satellites (those that use energy being emitted from the Earth for measurements) are not able to penetrate cloud or vegetation cover. This challenge can lead to the inability to see a fire, or the size and radiative power of small fires. Finding the right data, at the required spatial and temporal resolution for your interest, is key.

Last Updated: Nov 7, 2019 at 8:15 AM EST