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Landslides Data Pathfinder

The Mud Creek landslide near Big Sur, California, dumped about 6 million cubic yards (5 million cubic meters) of rock and debris across California Highway 1 on May 20, 2017.

The Mud Creek landslide near Big Sur, California, dumped about 6 million cubic yards (5 million cubic meters) of rock and debris across California Highway 1 on May 20, 2017. Credit: U.S. Geological Survey

Landslides are some of the most pervasive disasters in the world, killing thousands of people each year. The mass movement of land (sediments and soils, bedrock and boulders, even whole mountainsides) down a slope is induced by the force of gravity. There are numerous contributing forces to a landslide, many of which can be monitored or observed through remote sensing data. Intense or prolonged rainfall is the most frequent extrinsic trigger of landslides, as it reduces friction between materials and increases the water pressure within the soil pores, causing it to be more likely to fail. Earthquakes and changes to the landscape from wildfires, road building, and deforestation can also contribute to the instability of the land surface and cause landslides. NASA's Earth observation (EO) data provide important data to estimate landslide hazards. Within the U.S., the United States Geological Survey's (USGS) National Landslide Hazards Program provides important information on identifying and monitoring landslides.

Find and Use Landslide Event Monitoring Data

Find and Use Landslide Event Monitoring Data

Landslide Hazard Assessment

Landslide Viewer is a web portal to open global landslide data and environmental data from NASA, citizen scientists, and other resources. Knowing where and when landslides occur can help communities worldwide prepare for these disasters.NASA's Precipitation Measurement Mission (PMM) has a global Landslide Hazard Assessment for Situational Awareness (LHASA) model, which provides information on potential landslide hazards over space and time. LHASA's landslide "nowcast" is created by comparing Global Precipitation Measurement (GPM) data from the last seven days to a historical threshold for high rainfall (95th percentile). In places where precipitation is unusually high, the susceptibility of the terrain is evaluated, which includes quantitative information on the presence or absence of roads, changes in land cover, the proximity of any major tectonic faults, the weakness of local bedrock, and the steepness of the topography. Information on this model as well as the data behind it is available at Landslides @ NASA. The data can be viewed within the Landslide Viewer and downloaded via API within the Applications Viewer.

Precipitation

PMM provides 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.

Screenshot of IMERG data in Earthdata SearchThe products from 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. The Integrated Multi-satellitE Retrievals for GPM (IMERG) contains multiple runs, which accommodate different user requirements for latency and accuracy (Early = 4 hours, e.g., for flooding events; Late = 12 hours, e.g., for crop forecasting; and Final = 3 months, with the incorporation of rain gauge data, for research).

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

  • 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.

Geographic Information System (GIS) analysis ready datasets in GeoTIFF format can be accessed from the Precipitation Processing System FTPS site. Note that you will need to register to access the FTP site.

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.

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 API and tools developed by NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) or through NASA's Land Processes Distributed Active Archive Center (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AppEEARS).

Soil Moisture

Rainfall decreases the soil's shear strength by accumulating in the subsurface as soil moisture. Soil moisture is, therefore, important to understand if oversaturated conditions exist, when a rainfall event occurs. If so, this could contribute to excess runoff and landslide potential.

Current ground measurements of soil moisture are sparse and have limited coverage; satellite data help fill in those gaps. On the other hand, satellite data are limited by their relatively coarse resolution; the preferred measurement should be chosen based upon your needs. Utilizing a combination of both ground-based and remote sensing data provides for spatial and temporal data continuity.

NASA's Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top five 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.

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

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

ORNL DAAC's Soil Moisture Visualizer integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. LP DAAC's AppEEARS offers another option to simply and efficiently extract subsets, transform, and visualize SMAP data products. See the Tools for Data Access and Visualization section for additional information.

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:

Freeze/Thaw

As sediments go through the weathering process of freezing and thawing, the cohesion between the rock grains is reduced. This destabilizes the overall structure of the material, contributing to landslides. NASA's SMAP mission has a Level 3 product that quantifies the nature, extent, timing, and duration of landscape seasonal freeze/thaw transitions, which can help in the assessment of landslide potential.

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

Data can be visualized in Worldview:

Topography/Elevation

Knowing local topography is essential for disaster managers and emergency management professionals seeking to assess an area's risk level, including landslide potential and runoff.

An ASTER GDEM image of Mt. Raung and the surrounding area.

An ASTER GDEM image of Mt. Raung and the surrounding area. Image Credit: Land Processes Distributed Active Archive Center

A method for delineating topography is NASA's Shuttle Radar Topography Mission (SRTM). SRTM provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane.

On average, compared to geodetic points over the U.S., SRTM data has a lower root mean square error (RMSE); RMSE is a commonly used method to express vertical accuracy of elevation datasets. Digital elevation model data accuracy is typically very sensitive to vegetation cover, however. ASTER tends to perform better over certain landcover types.

February 2020, LP DAAC released a new data product, NASADEM, available at 1 arc second resolution. NASADEM extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products. The improvements were achieved by reprocessing the original SRTM radar signal data and telemetry data with updated algorithms and auxiliary data not available at the time of the original SRTM processing.

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Find and Use Post-Landslide Event Data

Find and Use Post-Landslide Event Data

Once a landslide event has occurred, there are various types of remote sensing data that can be used to measure displacement in an area. A landslide event can be a single landslide or can be numerous (several tens to thousands), caused by a major triggering event, like a tropical storm.

The NASA Landslide Viewer is a GIS-based web portal that provides global landslide data from NASA, citizen scientists, and other resources. Landslides @ NASA has additional information on using the viewer, data citations, and other resources. The viewer provides information on global landslide susceptibility, which combines information on elevation, geology, fault, roads, and forest loss to rank most of the Earth's land surface.

Land Surface Reflectance

The Operational Land Imager (OLI) on Landsat 8 acquired the above image of landslide debris from the Oso, Washington landslide and the barrier lake that formed subsequently on March 23, 2014.

The Operational Land Imager (OLI) on Landsat 8 acquired the image of landslide debris from the Oso, Washington landslide and the barrier lake that formed subsequently on March 23, 2014. Credit: NASA Earth Observatory

Land surface reflectance can be used to monitor and measure the displacement of sedimentary material once a flow or fall has occurred. This is important in assessing damage to the surrounding area, as well as for future hazard mapping.

Moderate resolution instruments that are primarily used for this measurement include MODIS and VIIRS (from the joint NASA/NOAA Suomi National Polar-orbiting Partnership [Suomi NPP] and from the NOAA-20 Joint Polar Satellite System 1 [JPSS-1] satellite). MODIS reflectance products are available at 250 m, 500 m, 1000 m, and 5600 m spatial resolution. VIIRS reflectance products are available at 500 m and 1000 m spatial resolution. MODIS data are acquired every one to two days, whereas the wider swath width of VIIRS allows for daily global coverage.

ASTER, another high-resolution instrument, acquires visible and near-infrared (VNIR) reflectance data at 15 m resolution and SWIR (through 2009) reflectance data at 30 m resolution. Note that ASTER is a tasked sensor, meaning that it only acquires data when it is directed to do so over specific targets, making its temporal resolution variable depending on your target region of interest. ASTER Surface Reflectance products are processed on-demand and so must be requested with additional parameters. Note that there is a limit to 2000 granules per order.

Research-quality surface reflectance data products can be accessed directly via Earthdata Search or the LP DAAC Data Pool; MODIS, VIIRS, and ASTER are available as HDF files, but are also customizable to GeoTIFF:

LP DAAC also provides a tool AppEEARS, which offers a simple and efficient way to access, transform, and visualize geospatial data from a variety of federal data archives. MODIS and VIIRS surface reflectance data are available in AppEEARS, as well as the USGS Landsat Analysis Ready Data (ARD) surface reflectance product.

The ORNL DAAC also provides tools for on-demand subsetting of MODIS and VIIRS land data. In particular, the Subsets API allows users to retrieve custom subsets, analytics and visualization of MODIS and VIIRS data products.

For higher resolution, the Landsat 7 Enhanced Thematic Mapper (ETM+) sensor and the Landsat 8 Operational Land Imager (OLI) instrument, acquires data at 30 m spatial resolution in VNIR every 16 days (or less as you move away from the equator). Landsat 8 was developed as a collaboration between NASA and the USGS. USGS now leads satellite operations and data archiving at the Earth Resources Observation and Science (EROS) center.

Landsat data from USGS's Earth Explorer are available via Earthdata Search. Note that you will need a USGS login to download the data.

Data can be visualized in Worldview:

Vegetation Greenness

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)

Vegetation indices measure the amount of green vegetation over a given area and can be used to assess vegetation health. A commonly-used vegetation index is the Normalized Difference Vegetation Index (NDVI), which uses the difference between near-infrared (NIR) and red reflectance divided by their sum. NDVI values range from -1 to 1. Low values of NDVI generally correspond to barren areas of rock, sand, exposed soils, or snow, while higher NDVI values indicate greener vegetation, including forests, croplands, and wetlands. The enhanced vegetation index (EVI) is another widely used vegetation index that minimizes canopy-soil variations and improves sensitivity over dense vegetation conditions.

Vegetation products from the MODIS instrument (on the Aqua and Terra satellites) and the Suomi NPP VIIRS instrument can be accessed in various ways.

Research-quality data products can be accessed directly via Earthdata Search; datasets are available as HDF files but are, in some cases, customizable to GeoTIFF.

The LP DAAC AppEEARS tool offers a simple and effective way to extract, transform, visualize, and download MODIS and VIIRS vegetation-related data products. AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest, and output data can be downloaded in csv (point), GeoTIFF (area) or NetCDF-4 (area) format. The ORNL DAAC subsetting tools provide a means to simply and efficiently access and visualize MODIS and VIIRS vegetation-related data products as well.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive 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 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:

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

Evaporative Stress Index

Level 4 Evaporative Stress Index PT-JPL, average from August 5, 2018 captured over California's Central Valley. High ESI is in shades of green and low ESI in shades of red.

Level 4 Evaporative Stress Index PT-JPL, average from August 5, 2018, captured over California's Central Valley. High ESI is in shades of green and low ESI in shades of red. Image Credit: LP DAAC

NASA's Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) aboard the International Space Station (ISS) produces Level 4 evaporative stress index (ESI) products. The ESI product is derived from the ratio of Level 3 actual ET to potential ET (PET), calculated as part of an algorithm.

LP DAAC's AppEEARS offers a simple and effective way to extract, transform, visualize, and download ECOSTRESS L1-L4 data products.

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.
Images show 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 Nighttime Imagery layer 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 event.

Research-quality data products can be accessed via Earthdata Search and close to NRT data can be accessed via Worldview:

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.

Interferometric Synthetic Aperture Radar

Ground movement of landslides in the Berkeley Hills of California between 2008 and 2010 was captured using Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), NASA’s airborne prototype for the NASA-Indian Space Research Organisation SAR (NISAR) mission. In this type of map, called an interferogram, the colors show contours	of ground movement.

Ground movement of landslides in the Berkeley Hills of California between 2008 and 2010 was captured using Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), NASA’s airborne prototype for the NASA-Indian Space Research Organisation SAR (NISAR) mission. In this type of map, called an interferogram, the colors show contours of ground movement. Credit: NASA

Repeat-pass radar interferometry from spaceborne platforms is routinely used to produce topographic change maps as digital displacement models (DDMs). When two observations are made from the same location in space but at different times, the interferometric phase is directly proportional to any change in the range of a surface feature. This change allows for the measurement of any displacement or ground deformation that has occurred between the time of the two observations.

Interferometric SAR (InSAR) thus provides centimeter level measurements of displacement from landslides. This displacement or deformation is seen as contour lines; where the lines are closer together, there was a lot of movement. Discontinuities in the contour lines also show where the actual fault rupture is. Contour lines are half of the radar's wavelength, so Sentinel-1 with a radar length of 6 cm has contour lines indicating ground deformation of 3 cm.

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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 | 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 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.

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 Geostationary Operational Environmental Satellite (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 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.

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. You will need an Earthdata login to request 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

Sentinel Toolbox

The European Space Agency (ESA) Sentinel-1 Mission consists of two satellites, Sentinel-1A and Sentinel-1B, with synthetic aperture radar instruments operating at a C-Band frequency. They orbit 180° apart, together imaging the entire Earth every six days. SAR is an active sensor and so can penetrate cloud cover and vegetation canopy and can observe at night. It also provides useful information to detect 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 analysis of Sentinel-1 data.

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

SAR Interferometry

Once you have downloaded the data, a data file before the event and a data file after the event, you will need to coregister the two files and then create an interferogram. The process for doing that follows:

  1. Visualize: Open the files in the Sentinel Toolbox. Important note: DO NOT unzip the downloaded SAR file. When you expand the Bands folder, you will find bands containing the real (i) and imaginary (q) parts of the complex data. In Sentinel-1 IW SLC products, you will find three sub-swaths labeled IW1, IW2, and IW3. To view the data, double-click on the Intensity_Sub-Swath_Polarization band of one of the two images.
  2. Coregister: For interferometric processing, two or more images must be coregistered into a stack. One image is selected as the master and the other images are the "slaves." The pixels in "slave" images will be moved to align with the master image to sub-pixel accuracy. To do this, select Radar/Coregistration/S-1 TOPS Coregistration. For more information on this type of processing, view Sentinel Online's Terrain Observation with Progressive Scans SAR (TOPSAR) processing technique.
    1. In the Read tab, select the first product. This should be the earlier of the two SLCs.
    2. In the Read(2) tab, select the other product. This will be your "slave" image.
    3. In the TOPSAR-Split tabs, select the appropriate sub-swath and polarization for each of the products.
    4. In the Apply-Orbit-File tabs, select the Sentinel Precise Orbit State Vectors. If precise orbits are not yet available for your product, you may select the restituted orbits, which may not be as accurate but will be better than the predicted orbits available within the product.
    5. Sentinel-1 Toolbox coregistration process. Note the arrow to the left and right in the coregistration window; it will cycle through the different tabs.

      Sentinel-1 Toolbox coregistration process. Note the arrow to the left and right in the coregistration window; it will cycle through the different tabs.

      In the Back-Geocoding tab, select the Digital Elevation Model (DEM) to use and the interpolation methods. The default is the SRTM 3 Sec DEM.
    6. In the Write tab, set the Directory path to your working directory.
    7. Click Run to begin coregistering the data. The resulting coregistered stack product will appear in the Product Explorer window with the suffix Orb_Stack.
  3. Interferogram: The interferogram is formed by cross-multiplying the master image with the complex conjugate of the "slave." The amplitude of both images is multiplied while their respective phases are differenced to form the interferogram.
    1. Select the new stack file in the product explorer and then select Radar/Interferometric/Products/Interferogram Formation.
    2. Keep the default values for Interferogram Formation, but confirm that the output Directory path is correct.
    3. Click Run.
    Sentinel-1 Toolbox interferogram

    Through the interferometric processing flow, the tool tries to eliminate other sources of error so that what is left is typically the surface deformation related to an event. You can visualize the phase information at this step.

    Interferometric fringes represent a full 2π cycle of phase change. Fringes appear on an interferogram as cycles of colors, with each cycle representing relative range difference of half a sensor's wavelength. Relative ground movement between two points can be calculated by counting the fringes and multiplying by half of the wavelength. The closer the fringes are together, the greater the strain on the ground.

  4. Multilooking and Phase Filtering: Lastly the phase associated with topography has to be removed and additional phase filtering to reduce noise and enhance the appearance of the deformation fringes.

Step-by-steps of this can be found within ASF DAAC's InSAR data recipes.

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Page Last Updated: Sep 8, 2020 at 3:53 PM EDT