This Data Pathfinder is designed to help guide you through the process of selecting and using datasets applicable to wildfires, with guidance on resolutions and direct links to the data sources.
Getting Started with this Pathfinder
Who is the intended audience for this Pathfinder?
This Data Pathfinder is primarily intended for:
Users with some experience using remote sensing data or who are experienced data science professionals
Geographic Information Systems (GIS) users interested in locating data, workflows, web services, maps, and similar resources (for workflows, web services, maps, and similar resources, see the GIS Pathfinder)
Python or R programmers interested in locating data (the Earthdata Developer Portal is a hub for all publicly accessible developer documentation related to NASA Earth Observing System Data and Information System, EOSDIS, enterprise services and applications)
Join our community of NASA data users
While NASA data are openly available without restriction, an Earthdata Login is required to download data and to use some tools (such as Giovanni) with full functionality.
Benefits of having an Earthdata Login:
Provides users a single sign-on, meaning that the user only has to enter their username and password once, to gain access to multiple (different) applications
Saves users’ preferences
Offers ability to receive news/notifications on data and services
Serves greater support for customizable interfaces
Gives supplemental information about data resources
If you have specific questions about how to use data, tools, or resources mentioned in this Data Pathfinder, please visit the Earthdata Forum. There, you can interact with other data users and NASA subject matter experts on a variety of Earth science research and applications topics.
This section provides links to NASA and external tools and applications relevant to analyzing and visualizing data referenced in this Data Pathfinder. NASA's Earth Science Data Systems (ESDS) Program maintains many more resources for data analysis that may be helpful. Explore the full list on the NASA Earthdata Data Tools page.
This section provides links to tools and applications relevant to analyzing and visualizing wildfire data referenced in this Data Pathfinder. NASA's Earth Science Data Systems (ESDS) Program maintains many more resources for data analysis that may be helpful. Explore the full list on the NASA Earthdata Data Tools page.
AppEEARS, available through NASA's Land Processes Distributed Active Archive Center (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
Area samples, for spatial areas via vector polygons
Performing Area Extractions
After choosing to request an area extraction, you will be taken to the Extract Area Sample page where you will specify a series of parameters that are used to extract data for your area(s) of interest.
Define your region of interest in one of three ways:
Upload a vector polygon file in shapefile format (you can upload a single file with multiple features or multipart single features). 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 dates a start and end date in the calendar.
Adding Data Layers
Enter the product short name (e.g., MOD09A1, ECO3ETPTJPL), keywords from the product long name, a spatial resolution, a temporal extent, or a temporal resolution into the search bar. A list of available products matching your query will be generated. Select the layer(s) of interest to add to the Selected layers list. Layers from multiple products can be added to a single request. Be sure to read the list of available products available through AppEEARS.
Selecting Output Options
Two output file formats are available:
If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF-4 is selected, outputs will be grouped into .nc files by product and by feature.
Interacting with Results
Once your request is completed: From the Explore Requests page, click the View icon in order 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 or a different layer of interest from the Layer dropdown.
Be sure to check out the AppEEARS documentation to learn more about downloading the output GeoTIFF or NetCDF-4 files.
Earthdata Search is a tool for searching for and discovering 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. Note that an Earthdata Login is required to download data from Earthdata Search.
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.
The Early Warning eXplorer (EWX) Next Generation Viewer is an interactive web-based mapping application that helps users explore and visualize global geospatial data related to drought monitoring and famine early warning. Meteorological and agricultural drought hazard-related data are offered on a decadal to monthly basis. A guide produced by the Climate Hazards Center, University of California Santa Barbara, provides tutorials for using this tool.
The Fire Information for Resource Management System (FIRMS) distributes Near Real-Time (NRT) active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua and Terra satellites, and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA 20 satellites. MODIS and VIIRS active fire/thermal anomaly data are indications of sensor-detected hotspots, and may be from fire, hot smoke, agriculture, volcanic eruptions, or other sources. FIRMS also provides thermal anomaly data from the Landsat 8 and 9 Active Fire and Thermal Anomalies product, which are generated from data acquired by the Landsat Operational Land Imager (OLI).
Global FIRMS data are available within 3 hours of satellite observation; U.S. and Canada active fire detections are available in real-time.
Giovanni is an online environment for the display and analysis of geophysical parameters. Note that an Earthdata Login is required to use Giovanni. While Giovanni provides many options for analysis, the following are the more popular ones:
Time-averaged maps: A simple way to observe the variability of data values over a region of interest
Map animations: A means to observe spatial patterns and detect unusual events over time
Area-averaged time series: 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
Histogram plots: Used to 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 the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) YouTube channel
Data recipe for downloading a Giovanni map as NetCDF and converting its data to quantifiable map data in the form of latitude-longitude-data value ASCII text
HDF and NetCDF files can be viewed using NASA's Panoply data viewer. Along with viewing data, Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.
The Resilience Analysis and Planning Tool (RAPT) created by the U.S. Federal Emergency Management Agency (FEMA) is a GIS web-based app that offers a variety of data (i.e., census data, infrastructure locations, and hazards, including real-time weather forecasts, historic disasters and estimated annualized frequency of hazard risks) that may complement the NASA data in this Data Pathfinder. These datasets presented in RAPT enable federal, state, local, tribal, and territorial emergency managers and other community leaders to examine and mitigate wildfire risks for community resiliency and environmental justice planning/analysis. RAPT provides a number of resources for users to get familiar with using the tool:
The ESA (European Space Agency) Sentinel-1 Mission consists of two satellites, Sentinel-1A and Sentinel-1B, with synthetic aperture radar (SAR) instruments operating at a C-Band frequency. The satellites orbit 180° apart, and together image the entire Earth every six days. SAR is an active sensor that can penetrate cloud cover and vegetation canopy, and also observe at night. Therefore, it is ideal for flood inundation mapping. It also provides useful information to detect changes in land position after an earthquake, volcanic eruption, or landslide. SAR data are very complex to process; however, ESA has developed a Sentinel-1 Toolbox to aid with processing and analysis of Sentinel-1 data.
Before choosing data, it's important to determine which SAR wavelength band meets your needs, as radar signals penetrate deeper as the sensor wavelength increases. This difference in penetration is due to the dielectric properties of a given medium, which dictate how much of the incoming radiation scatters at the surface, how much signal penetrates into the medium, and how much energy gets lost to the medium through absorption.
Another important parameter to consider when choosing a SAR dataset is the polarization, or the direction in which the signal is transmitted or received: horizontally or vertically. Dual polarization, for example, refers to two different signal directions: horizontal/vertical and vertical/horizontal (HV and VH). Knowing the polarization from which a SAR image was acquired is important, as signals at different polarizations interact differently with objects on the ground and affect the recorded radar brightness in a specific polarization channel.
Once you have downloaded the needed SAR data, the data must be calibrated to account for distortion. The objective in performing calibration is to create an image where the value of each pixel is directly related to the backscatter of the surface. Calibration takes into account radiometric distortion, signal loss as the wave propagates, saturation, and speckle. This process is critical for analyzing images quantitatively; it is also important for comparing images from different sensors, modalities, processors, and acquisition dates.
Important note: DO NOT unzip the downloaded SAR file. Open the .zip file from within the Sentinel Toolbox. When you expand the Bands folder, you will see an amplitude and an intensity file for each polarization option. (The intensity band is a virtual one and is the square of the amplitude.) Open the amplitude file. Subset the data by zooming in to the area of interest and right-clicking on "Spatial Subset from View."
Calibration is done by following these steps:
Radiometric calibration is performed by selecting Radar/Radiometric/Calibration (leave parameters as default).
Geometric correction is done after radiometric calibration. This fixes geometric distortions due to slant range, layover, shadow, and foreshortening.
Terrain correction can be performed by selecting Radar/Geometric/Terrain Correction/ Range-Doppler Terrain Correction. This requires a digital elevation model (within the processing parameters, SRTM is the default selection). You also can specify a map projection in the processing parameters.
Speckle is another characteristic of SAR images that must be addressed. Speckle is the grey level variation that occurs between adjacent resolution cells, and creates a grainy texture. Within the Toolbox, speckle can be removed by selecting "Radar/Speckle Filtering/Single Product Speckle Filter" and then choosing a type of filter; "Lee" is one of the most common.
For further information on SAR flood inundation mapping, see the NASA Alaska Satellite Facility DAAC (ASF DAAC) flood inundation recipes for QGIS or ArcGIS.
ORNL DAAC's SDAT is an Open Geospatial Consortium standards-based web application to visualize and download spatial data in various user-selected spatial/temporal extents, file formats, and projections. Several 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.
You can then select your spatial extent, projection, and output format for downloading.
The Soil Moisture Visualizer tool at NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC) integrates in-situ, airborne, and remote sensing data from a variety of soil moisture datasets covering North America into an easy-to-use platform (read more about this tool at Soil Moisture Data Sets Become Fertile Ground for Applications). 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.
To use the visualizer, select a dataset of interest under Data. Depending on the dataset chosen, the visualizer provides the included latitude/longitude or an actual site location name and data collection relative time frames. Upon selection of 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 visualizer also provides data sources for download.
With the Global Subset Tool, you can request a subset for any location on Earth and receive this subset 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. Note: An Earthdata Login is required 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 that are of relevance to the biodiversity community.
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 users' workflow.
The NASA Worldview data visualization application provides the capability to interactively browse more than 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, which 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 now includes nine geostationary imagery layers from the GOES-East, GOES-West, and Himawari-8 geostationary 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 visualization of 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.
This section provides links to case studies and story maps that communicate how users apply NASA Earth observation data to researching and managing wildfires around the world.
NASA Worldview: Satellite Detections of Fire Tour Story (2021) NASA provides insights into fires and thermal anomalies occurring daily around the world. Satellite-derived fire data and imagery available in Worldview are from the MODIS instrument aboard the Terra and Aqua satellites and the VIIRS instrument aboard the joint NASA/NOAA Suomi NPP and NOAA-20 satellites.
Connection of Sustainable Development Goals to Wildfires
The Sustainable Development Goals (SDGs) are a collection of 17 interlinked global goals designed to be a blueprint for a sustainable future for all of Earth’s inhabitants. The SDGs are part of the 2030 Agenda for Sustainable Development, an international plan signed by all United Nations (UN) member states in 2015 and underpinned by the foundational components of People, Planet, and Prosperity.
The 17 SDGs in the Agenda are made up of 169 objectives that include specific social, economic, and environmental targets. These targets provide a blueprint for developing a more sustainable global future.
Data acquired remotely by sensors aboard satellites and aircraft or installed on the ground play a unique role in tracking the progress toward achieving the SDGs. These remotely sensed Earth observations provide consistent and continuous information on the state of Earth processes and their change over time. These data also are integral components of socioeconomic metrics that provide a measure of how humans co-exist with the environment and the stresses they encounter through natural and human-caused changes to the environment.
NASA Earth observation data are available without restriction to all data users, a policy that is being adopted by other international space agencies and one that reduces the cost of monitoring the SDGs and provides developing countries a means to acquire and utilize these data for other policy-making purposes.
NASA’s datasets are organized by topics that help users to locate, access, and apply relevant and complementary datasets for each SDG. The Wildfire Data Pathfinder addresses (but is not limited to) the following SDGs:
SDG Goals Relevant to Wildfires
Goal 1. End poverty in all its forms everywhere
Target 1.5: Build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social, and environmental shocks and disasters
Goal 2. End hunger, achieve food security and improved nutrition, and promote sustainable agriculture
Target 2.4: Ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production; that help maintain ecosystems; that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding, and other disasters; and that progressively improve land and soil quality
Goal 11. Make cities and human settlements inclusive, safe, resilient, and sustainable
Target 11.5: Significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations
Goal 13. Take urgent action to combat climate change and its impacts
Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries
Target 13.2: Integrate climate change measures into national policies, strategies, and planning
Target 13.3: Improve education, awareness-raising, and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning
The opportunities to connect NASA data to the SDGs are infinite; therefore, the datasets included in specific Data Pathfinders are not intended to be comprehensive. Additionally, NASA datasets are not official indicators for SDG monitoring and decision-making but are complementary.