Cyclones Data Pathfinder

Hurricane Isabel, which was once a powerful Category 5 hurricane in the central Atlantic with winds estimated at 160 mph, finally came ashore on September 18, 2003, as a much weaker Category 2 storm.

Hurricane Isabel, which was once a powerful Category 5 hurricane in the central Atlantic with winds estimated at 160 mph, finally came ashore on September 18, 2003, as a much weaker Category 2 storm. Credit: NASA.

Tropical storms are low-pressure systems that form over warm tropical waters, where the sea surface temperatures are greater than about 26.5°C. Because of this critical temperature, they occur in different seasons in the Atlantic, Pacific, and Indian Oceans. The storms are cyclonic, meaning they rotate in either a clockwise (Southern Hemisphere) or counterclockwise (Northern Hemisphere) direction, often having outer edges that extend hundreds of kilometers from the center of the storm. A tropical depression reaches storm status when its winds maintain a speed of 33 knots (around 38 mph or 62 km/hr) or more. A tropical storm reaches hurricane status when its winds maintain a speed of 64 knots (74 mph or 119 km/hr) or more. These massive storms bring sustained heavy winds and rainfall, devastating coastal communities with storm surges and both coastal and inland areas with flooding and winds.

NOAA provides up-to-date information on storm tracking and intensity within the Atlantic and East Pacific Oceans at its National Hurricane Center and the Naval Oceanography Portal provides information on storm tracking within the West Pacific and Indian Oceans through its Joint Typhoon Warning Center. Note that hurricanes, cyclones, and typhoons are all cyclonic storms with wind speeds over 64 knots; the name is dependent on location.

NASA provides information that can help in pre-storm emergency preparedness, by helping urban planners and emergency management professionals understand the exposure and vulnerabilities as well as post-storm damage assessment and response. In addition to the datasets below, NASA has several other projects that may have cyclone-related model-based data or tools. View the Other NASA Assets section to find out more.

Pre-storm Assessment

Pre-storm Assessment

Precipitation

Near real-time IMERG Early Run Half-Hourly Image, acquired onMay 7, 2020

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

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 Global Precipitation Measurement (GPM) missions. The follow-on mission, GPM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage. GPM has developed a story map providing information on the 2020 Hurricane Season.

The products from TRMM and GPM are available individually and have also 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).

Data Products for Measuring Precipitation

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

  • TMPA from Earthdata Search
    Rainfall estimate at 3 hours, 1 day, or NRT and accumulated rainfall at 3 hours and 1 day. Data are in HDF format and can be opened using Panoply. Data are available from 1997.
  • IMERG from Earthdata Search
    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 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 and multiple temporal coverages, 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 in Worldview:

Daymet is a collection of gridded estimates of daily weather parameters. 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 in a variety of ways, including Earthdata Search, an application programming interface (API), tools developed by NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), and through NASA's Land Processes DAAC (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AppEEARS).

Sea Surface Temperature

Data Products for Measuring Sea Surface Temperature (SST)

Research-quality data products from the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and the Joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) can be accessed via 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, 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 and multiple temporal coverages, 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.

A new Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis dataset, the Multi-Scale Ultra High Resolution Sea Surface Temperature (MUR SST), is now available from June 2002 to present at 1 km spatial resolution. The dataset is based upon nighttime skin and sub-skin observations from several instruments, including satellite and in situ observations. The MUR SST can also be visualized at the Physical Oceanography DAAC (PO.DAAC) State of the Oceans (SOTO) tool (see State of the Oceans below).

Winds

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of satellites collect frequent remote sensing measurements of surface wind speeds in the inner core of tropical cyclones. The satellites use the constant and ubiquitous signals from the Global Positioning Satellite (GPS) system. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides surface wind data beginning in 1980 and runs a few weeks behind real time.

Giovanni time-averaged map of surface wind speed during Hurricane Katrina, which made landfall on August 29, 2005.

Giovanni time-averaged map of surface wind speed during Hurricane Katrina, which made landfall on August 29, 2005.

Data Products for Measuring Wind Speed and Direction

Research-quality CYGNSS and MERRA-2 data products can be accessed via 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, 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 and multiple temporal coverages, 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.

Sea Level Pressure

Data Products for Measuring Sea Level Pressure

Research-quality data products can be accessed via 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, 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 and multiple temporal coverages, 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.

Socioeconomic Data

NASA's Socioeconomic Data and Applications Center (SEDAC) provides a number of datasets on population exposure and vulnerability and flood hazard potential.

SEDAC also has several tools to visualize population data, like the POPGRID Viewer that enables direct comparison of different population datasets and the Hazards Mapper web mapping application allows users to estimate the populations in proximity to natural disasters, and to assess exposure.

State of the Oceans

The PO.DAAC's SOTO is an interactive web-based tool to generate informative maps, animations, and plots that communicate and promote the discovery and analysis of the state of the oceans. The tool includes data such as sea surface temperature, surface winds, surface currents, sea surface height, soil moisture, and precipitation.

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Near Real-time Assessment

Near Real-time Assessment

Clouds

The Clean Infrared (10.3 um, Band 13) layer from the Advanced Himawari Imager (AHI), as visualized in Worldview, is useful for detecting clouds all times of day and night and is quite useful in retrievals of cloud top height.

The Clean Infrared (10.3 um, Band 13) layer from the Advanced Himawari Imager (AHI), as visualized in Worldview, is useful for detecting clouds all times of day and night and is quite useful in retrievals of cloud top height.

The Geostationary Operational Environmental Satellites (GOES) -East satellite is centered on 75.2 degrees W, covering the Conterminous US, Canada, Central and South America (so most of the Atlantic Ocean). GOES-West is centered on 137.2 degrees W, covering most of the Pacific Ocean, the U.S., most of Canada, Central, the western half of South America, and parts of Australasia. The Himawari-8 satellite is centered on 140.7 degrees E, covering most of the Pacific Ocean, a portion of Eastern Asia, and parts of Australasia. The data from the red visible layer is used primarily to monitor the evolution of clouds throughout the daylight hours. It is also useful for identifying small-scale features such as river fog/clear air boundaries, or overshooting tops of cumulus clouds. Data are acquired every 10 minutes or more often for mesoscale sectors, with time steps as small as 30 seconds if two mesoscale sectors are overlaid.

Surface Reflectance

  • MODIS Corrected Reflectance in Worldview
    The MODIS Corrected Reflectance imagery is available only as near real-time imagery. The MODIS Corrected Reflectance algorithm utilizes MODIS Level 1B data (the calibrated, geolocated radiances). It is not a standard, research-quality product. The purpose of this algorithm is to provide natural-looking images by removing gross atmospheric effects, such as Rayleigh scattering, from MODIS visible bands 1-7.
  • VIIRS Corrected Reflectance in Worldview
    The VIIRS Corrected Reflectance imagery is available only as near real-time imagery.

Precipitation

Sea Surface Temperature

Cloud Top Temperatures

Infrared light enables NASA to take the temperatures of clouds and thunderstorms that make up tropical cyclones. The stronger the storms are, the higher they extend into the troposphere, therefore resulting in cold cloud-top temperatures.

Weather Maps

NASA's Global Modeling and Assimilation Office provides applications for interactive analysis and visualizations of experimental, climatological data, like this model of precipitation and sea level pressure for May 8, 2020.

NASA's Global Modeling and Assimilation Office provides applications for interactive analysis and visualizations of experimental, climatological data, like this model of precipitation and sea level pressure for May 8, 2020.

The NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System, Version 5 (GEOS-5) has a series of weather maps that can be used to predict parameters such as precipitation, humidity, wind speed, and temperature up to 240 hours out.

  • GEOS-5 Weather Maps
    Within the viewer, select the parameter or field of interest, the area of interest, and indicate the forecast time and the forecast lead hour. Selecting “Animate” shows the forecast for the given parameter over the time period indicated. Note that it may take time to load the images to animate. For wind speed near the surface, select 850 as your level (note; 850 hPa is approximately 5000 ft or 1500 m above sea level).

NASA's Disasters Tropical Cyclone Dashboard

The dashboard contains experimental NASA products that may assist in preparing for or responding to a tropical cyclone. Datasets include precipitation, sea surface temperature, radar reflectivity, flood detection, soil moisture, and wind speed.

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Post-storm Assessment

Post-storm Assessment

Flood Inundation Mapping

NASA has several products that can be used to assess flooding qualitatively: Landsat, the MODIS sensors (on both Terra and Aqua), and Suomi NPP VIIRS. However, these sensors are impeded by cloud cover and nighttime conditions. During a storm, this is a significant drawback. Once the storm has passed, with a pre-event image and a post-event image, a more quantitative assessment of flooding extent can be made.

During the storm, synthetic aperture radar (SAR) may provide a better “view,” as it is able to penetrate cloud cover and works in both day and night conditions. SAR data are more complex, requiring a certain level of processing skill. Refer to the Tools for Data Access and Visualization section for information on processing Sentinel SAR data.

Observations of surface reflectivity from CYGNSS over the southeastern United States and Caribbean. (a) Surface reflectivity observations for the time period Jul 1–Aug 20, 2017, before the hurricane season began. (b) Surface reflectivity observations after Hurricane Harvey (Aug 25–Sep 15, 2017) for the southeastern United States. Observations in the inset of Cuba were recorded in the time period after Hurricane Irma (Sep 8–Sep 30, 2017). (c) Observed change in surface reflectivity after Hurricanes Harvey (southeastern United States) and Irma (Cuba inset).

Observations of surface reflectivity from CYGNSS over the southeastern United States and Caribbean. (a) Surface reflectivity observations for the time period Jul 1–Aug 20, 2017, before the hurricane season began. (b) Surface reflectivity observations after Hurricane Harvey (Aug 25–Sep 15, 2017) for the southeastern United States. Observations in the inset of Cuba were recorded in the time period after Hurricane Irma (Sep 8–Sep 30, 2017). (c) Observed change in surface reflectivity after Hurricanes Harvey (southeastern United States) and Irma (Cuba inset). Credit: Image used by permission of Clara Chew, University Corporation for Atmospheric Research.

CYGNSS is responsive to reflections from standing water and the amount of moisture in the soil, showing promise for mapping flood inundation. Currently flood inundation maps have to be derived from the delay-Doppler maps (DDMs) found in the Level 1 datasets. DDMs represent the correlative power between the received, surface-reflected signal, and a replica signal stored within the receiver. The peak cross-correlation can be related to surface characteristics, like the surface roughness and the dielectric constant, which is the wetness of the surface. Information on derivation of flood inundation maps can be found in research papers, like CYGNSS data map flood inundation during the 2017 Atlantic hurricane season.

Data Products for Measuring Surface Reflectance

Research-quality data products can be accessed via Earthdata Search or through NASA partner websites:

Data can be visualized in Worldview (often in NRT) and via NASA’s experimental Global Flood Mapping and Advanced Rapid Imaging and Analysis (ARIA) projects:

  • VIIRS Corrected Reflectance in Worldview
    Note that a false-color image created by combining bands M11 as red, I2 as green, and I1 as blue is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges.
  • MODIS Flood Inundation Maps
    1-3 or 14-day composites; flood water and surface water layers can be downloaded as shapefiles, while the water product itself can be downloaded as a GeoTIFF.
  • Event Response to Floods at ARIA
    The ARIA Project, a joint effort of the California Institute of Technology (Caltech) and the Jet Propulsion Laboratory (JPL), is developing the infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response.

Soil Moisture

Soil moisture conditions in Texas near Houston, generated by NASA's Soil Moisture Active Passive (SMAP) satellite before and after the landfall of Hurricane Harvey can be used to monitor changing ground conditions due to Harvey's rainfall.

Soil moisture conditions in Texas near Houston, generated by NASA's Soil Moisture Active Passive (SMAP) satellite before and after the landfall of Hurricane Harvey can be used to monitor changing ground conditions due to Harvey's rainfall. Credit: NASA.

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 the Land Data Assimilation System (LDAS), of which there is 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 soil moisture and evapotranspiration.

Research-quality data products can be accessed via Earthdata Search; datasets are available as HDF5 (SMAP) files which are also customizable to GeoTIFF.

The ORNL DAAC Soil Moisture Visualizer integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. See the Tools for Data Access and Visualization section for additional information.

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, 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 and multiple temporal coverages, 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:

Power Outages

Top image shows a typical night before Hurricane Maria made landfall, based upon cloud-free and low moonlight conditions; the below image is a composite that shows light detected by VIIRS on the nights of September 27 and 28, 2017. The images above show widespread outages around San Juan, including key hospital and transportation infrastructure.

Top image shows a typical night before Hurricane Maria made landfall, based upon cloud-free and low moonlight conditions; the below image is a composite that shows light detected by VIIRS on the nights of September 27 and 28, 2017. The images above show widespread outages around San Juan, including key hospital and transportation infrastructure. Credit: NASA.

The VIIRS Day/Night Band shows the earth’s surface and atmosphere using a sensor designed to capture low-light emission sources, under varying illumination conditions, which provides an assessment of power outages across an area due to the storm event.

Note that the imagery to the right is not showing raw imagery of light. A team of scientists from NASA’s Goddard Space Flight Center and Marshall Space Flight Center processed and corrected the raw data to filter out stray light from the Moon, fires, airglow, and any other sources that are not electric lights. Their processing techniques also remove as much other atmospheric interference as possible.

NASA has also developed the Black Marble, a daily calibrated, corrected, and validated product suite, so nightlight data can be used effectively for scientific observations. Black Marble's standard science processing removes cloud-contaminated pixels and corrects for atmospheric, terrain, vegetation, snow, lunar, and stray light effects on the VIIRS Day/Night Band radiances. Black Marble data can be accessed at NASA's Level-1 and Atmosphere Archive and Distribution System Distributed Active Archive Center (LAADS DAAC). Black Marble imagery in Worldview is an image composite that was assembled from clear, cloud free images for 2012 and 2016.

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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)| Sentinel Toolbox

Earthdata Search is a tool for data discovery of Earth Observation data collections from NASA’s Earth Observing System Data and Information System (EOSDIS), as well as U.S and international agencies across the 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 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.

Giovanni

Giovanni is an online environment for the display and analysis of 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 are a means to observe spatial patterns and detect unusual events over time.
  • 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.
  • 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:

  • Giovanni How-To’s on NASA's 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.

Worldview

NASA’s EOSDIS Worldview visualization application provides the capability to interactively browse over 900 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 the entire 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 now includes nine geostationary imagery layers from GOES-East, GOES-West and Himawari-8 available at ten 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.

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

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.

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.

Spatial Subsetting

Define your region of interest in one of these 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

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 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

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 Data. Depending on the dataset chosen, the visualizer provides the included latitude/longitude or an actual site location name and relative time frames of data collection. 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. Lastly it 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. Users 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. An Earthdata login is required to access the data.
  • With the Fixed Subsets Tool, you can download pre-processed subsets for 3000+ 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.
Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

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. Several data sets 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.

Sentinel Toolbox

Before choosing data, it’s important to determine which 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 of the energy gets lost to the medium through absorption.

SAR signal penetration by sensor wavelength λ

SAR signal penetration by sensor wavelength λ. Credit: NASA SAR Handbook.

Note that for biomass estimation, L-band and P-band sensors are preferred over higher frequencies and smaller wavelengths for two reasons: 1) at these bands, the radar waves or energy can penetrate the tree canopy and scatter from larger woody components of the forest, and 2) the scattering from larger tree components, unlike leaves, are more stable temporally and remain highly coherent over the acquisition period in the case of repeated measurements for change detection or interferometric applications (adapted from SAR Handbook, 2019).

The C-band can be used for low-vegetation biomass such as grasslands, shrublands, sparse woodlands, young secondary regeneration, and low-density wetlands.

Another important parameter to take into consideration when choosing a dataset is the polarization, or the direction in which the signal is transmitted and/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, affecting the recorded radar brightness in a specific polarization channel.

Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils).

Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils). Credit: NASA SAR Handbook.

SAR data are complex, requiring a certain level of processing skill.

The European Space Agency's (ESA) Sentinel-1 Mission consists of two satellites, Sentinel-1A and Sentinel-1B, with synthetic aperture radar (SAR) instruments operating at a C-Band frequency. They orbit 180° apart, together imaging the entire Earth every six days. SAR is an active sensor which can penetrate cloud cover and vegetation canopy and can observe at night; therefore it is ideal for flood inundation mapping. It also provides information useful in detecting the movement of Earth material after an earthquake, volcanic eruption, or landslide. SAR data are very complex to process, but ESA has developed a Sentinel-1 Toolbox to aid with processing and analyzing Sentinel-1 data.

For more information on active sensors, see What is Remote Sensing?; for more information on SAR specifically, see What is Synthetic Aperture Radar?

Flood Inundation Mapping

Once you have downloaded the needed SAR data product, it must be calibrated to account for distortion in the data. 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. So 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 different acquisition dates.

Screenshot of the Sentinel-1 toolbox

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:

  1. Radiometric calibration is performed by selecting Radar/Radiometric/Calibration (leave parameters as default).
  2. Geometric correction is done next to fix the main 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 can also specify a map projection in the processing parameters.
Sentinel-1 Toolbox Geometric Correction

Another characteristic of SAR images that must be accounted for is speckle. Speckle is the grey level variation that occurs between adjacent resolution cells, creating 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.

Comparison of speckle in SAR imagery within Sentinel-1 Toolbox

For further information on SAR flood inundation mapping, see the ASF DAAC data recipes for those related to flood inundation.

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Published May 11, 2020

Page Last Updated: Sep 8, 2020 at 12:59 PM EDT