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Sea Level Change Data Pathfinder

Sea Level Change Campaign

According to the United Nations, 40% of the world's population lives within 100 km of a coast, meaning that close to three billion people could be impacted by changes in sea level. Coastal communities are centers of economic, social, and cultural development; they also provide significant ecological and environmental services. Global Mean Sea Level (GMSL) is increasing at about 3.3 millimeters per year (mm/y) and is already having catastrophic effects in coastal communities through flooding, erosion, and storm-related hazards.

Thermal expansion and the addition of fresh water to oceans from glacier and ice sheet melt are causing a rise in GMSL. As the atmosphere warms, much of its heat gets absorbed by the ocean, causing the water to expand. More than 90% of warming over the past 50 years has occurred in the ocean. Along with this thermal expansion, land-based glaciers and ice sheets are melting. Greenland is losing about 289 gigatons (Gt) of ice per year and Antarctica about 132 Gt. To put this in perspective, the largest animal on Earth, a blue whale, weighs about 330,000 pounds or 165 tons; each year Earth loses the equivalent in ice of about 2.5 billion blue whales.

Locally and regionally, sea level change can be significantly different from the global average due to factors such as natural and human-induced subsidence (sinking or settling of the ground), ocean currents, and rebound from the compressive weight of Ice Age glaciers.

Exposure and vulnerability are important components in risk-management efforts and adaptation strategies. The presence of people, animals and ecosystems, environmental resources, infrastructure, or economic, social, and cultural assets in places and settings that could be adversely affected by a change in sea level is called exposure. Vulnerability is the propensity of a community to be adversely affected by sea level change, taking into consideration factors such as susceptibility to harm and lack of capacity to cope and adapt. Risk is determined by exposure and vulnerability to hazards.

Our current scientific understanding of sea level change is unprecedented due in large part to the long-term records of sea level and almost 30 years of satellite altimetry. NASA provides a wealth of data that support this understanding.

About the Data

About the Data

NASA collaborates with other federal entities and international space organizations, including NOAA, USGS, the Japan Aerospace Exploration Agency (JAXA) and Ministry of Economy, Trade, and Industry (METI), and the European Space Agency (ESA), to collect and distribute sea level data. Data from these agencies and various NASA instruments can be used for understanding a number of phenomena that contribute to sea level change. NASA also provides datasets to help assess the impacts, exposure, and vulnerability of individual communities to rising sea levels.

Datasets referenced in this pathfinder are from satellite and airborne sensors shown in the table below, including their spatial and temporal resolutions. Note that many satellites/platforms carry multiple sensors; the table below only lists the primary sensor used in collecting the specified measurement. When available, NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides data to the public within three hours of satellite overpass, which allows for near real-time (NRT) monitoring and decision making (sensors from which select datasets are available in LANCE are marked with *).

Note: This is not an exhaustive list of datasets but rather only includes datasets from NASA's Earth Observing System Data and Information System (EOSDIS).

Measurement

Satellite

Sensor

Spatial Resolution

Temporal Resolution

Glacier Surface Elevation NASA Oceans Melting Greenland (OMG) Airborne Glacier and Land Ice Surface Topography Interferometer Airborne (GLISTIN-A) radar 3 m
Global Mean Sea Level, Sea Surface Height Anomalies NASA Topographic Experiment (TOPEX)/Poseidon, Jason-1, Jason-2, Jason-3 NASA Radar Altimeter (NRA), TOPEX Microwave Radiometer (TMR), Poseidon-2, Jason-1 Microwave Radiometer, Poseidon-3, Advance Microwave Radiometer (AMR), Poseidon-3b Global Mean Sea Level: global average
Sea Surface Height Anomalies: 1/6th degree
Global Mean Sea Level: 10 days
Sea Surface Height Anomalies: 5 days
Global Water Storage/Height Anomalies; Global Mean Sea Level NASA Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) Star Camera Assembly (SCA), K-Band Ranging System (KBR), and SuperSTAR Accelerometer (ACC) Global Water Storage: 0.5°
Global Mean Sea Level: global time series
Global Water Storage: 1 month
Global Mean Sea Level: 1 year
Ice Sheet Mass Balance NASA Ice, Cloud and land Elevation Satellite (ICESat) and ICESat-2 Geoscience Laser Altimeter System (GLAS), Advanced Topographic Laser Altimeter System (ATLAS) GLAS: 60-70 m x 60-70 m
ATLAS: 20 m
GLAS: 1288 minute
ATLAS: 91 day
Ice Elevation, Ice Thickness NASA IceBridge Airborne Glacier and Ice Surface Topography Interferometer (GLISTIN-A) 50 m
Land Surface Backscatter JAXA and METI Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 10 m, 100 m
Land Surface Backscatter ESA Sentinel-1 Synthetic Aperture Radar (SAR) 25 x 40 m, 5 x 5 m, and 5 x 20 m 12 days
Nighttime Imagery, Sea Surface Temperature, Surface Reflectance NASA/NOAA Joint Polar Satellite System (JPSS) NOAA-20 satellite and Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) * 500 m, 1000 m, 5600 m daily
Ocean Surface Wind Speed NASA Cyclone Global Navigation Satellite System (CYGNSS) Delay Doppler Mapping Instrument (DDMI) 0.2° daily
Sea Surface Salinity NASA/ Comisión Nacional de Actividades Espaciales Satelite de Aplicaciones Cientificas (SAC) Aquarius passive microwave radiometers and active scatterometer 7 days
Sea Surface Salinity NASA Soil Moisture Active Passive (SMAP) Radar (active) - no longer functional
Microwave radiometer (passive)
60 km 8 days
Surface Elevation, Surface Slope NASA Delta-X Airborne Air Surface Water and Ocean Topography (AirSWOT) 3.6 m
Surface Reflectance NASA/USGS Landsat 7 and Landsat 8 Landsat 7: Enhanced Thematic Mapper Plus (ETM+)
Landsat 8: Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)
15, 30, 60 m 16 days
Surface Reflectance, Sea Surface Temperature NASA Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) * 250 m, 500 m, 1000 m, 5600 m 1-2 days

In addition to mission data available through EOSDIS Distributed Active Archive Centers (DAACs), NASA has a series of environmental simulation models that use satellite- and ground-based observational data. There are a few reasons model data may be preferred over remote sensing observations, including obtaining more complex data parameters, temporal coverage, spatial coverage, and/or data completeness.

Models are often used for projections and forecasts, but time is not the only dimension in which projections can be made. Models can also project into space, offering data where sensors are unavailable. For instance, satellite observations of land surface temperatures can only be made where there is a clear view of the land. Clouds and dust can obscure views, and observations are further dependent on the type of land cover; highly reflective areas, such as snow and urban areas, can be challenging to observe. A model allows researchers to fill those gaps by bringing in additional data from ground stations or other sensors that measure different wavelengths.

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns as well as anomalies. As climate is typically measured over a 30-year period, MERRA-2 data are well suited to make quantitative points about changes in climate.

NASA also has merged/fused products that are derived from multi-sensor data applied to oceanographic equations. The Ocean Surface Current Analysis Real-time (OSCAR) is a pilot processing system and data center providing surface current estimates, which have been computed from satellite altimeter and vector wind data using methods developed during the TOPEX/Poseidon mission.

The Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature (SST) Analyses is part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. MEaSUREs develops consistent global- and continental-scale Earth System Data Records by supporting projects that produce data using proven algorithms and input.

Model / Merged Product

Data Parameter

Spatial Resolution

Temporal Resolution

MERRA-2 Land surface temperature, surface humidity, winds, soil moisture 0.5° x 0.667° Monthly, daily, hourly
MUR SST 1 km daily
OSCAR Surface currents 0.33° x 0.33° 5 days

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Use the Data

Use the Data

Lake Imja near Mount Everest in the Himalaya is a glacier lake that has grown to three times its length since 1990. Credits: Planetary Science Institute/Jeffrey S. Kargel

Lake Imja near Mount Everest in the Himalaya is a glacier lake that has grown to three times its length since 1990. Credit: Planetary Science Institute/Jeffrey S. Kargel

Scientists, researchers, decision makers, and others use remote sensing data in numerous ways. Satellite data, coupled with ground-based data, aids in our understanding of the factors contributing to sea level change, forecasting, risk and response, impacts, and much more. NASA Earth science observations are transforming our approach to this critical issue.

Explore some of the stories behind the data in these feature articles highlighting research activities around the world.

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Find Global Mean Sea Level Data

Find Global Mean Sea Level Data

Satellite sea level observations from 1993 - present. Credit: NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC)

Satellite sea level observations from 1993 - present. Credit: NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC)

GMSL is the globally averaged sea level and trends indicate that it's rising at about 3.3 mm/y (NASA Sea Level Change). GMSL has risen about 8–9 inches (21–24 centimeters) since 1880, with about a third of that coming in just the last two and a half decades. Under funding from NASA's MEaSUREs Program, an annual reconstructed GMSL provides estimates of the contributions from various drivers of sea level change between 1900 and 2018. The reconstructed sea level is based on tide gauge observations aggregated annually. Sea level change contributions from thermal expansion, glacier mass changes, terrestrial water storage changes, and changing mass of the Greenland and Antarctic ice sheets were estimated by combining GRACE and GRACE-FO observations with long-term estimates based on in-situ temperature profiles.

In addition, a GMSL trend has been generated by integrating ocean altimeter data from multiple missions. This GMSL product is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, Ocean Surface Topography Mission (OSTM)/Jason-2 and Jason-3. It starts in September 1992 and continues to the present, with a lag of up to 4 months. All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites. Glacial Isostatic Adjustment (GIA) has not been applied, but it has been smoothed with a 60-day filter.

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Find Glaciers and Ice Sheet Data

Find Glaciers and Ice Sheet Data

Ice Mass Changes | Ice Height and Thickness

Ice Mass Changes

Antarctica ice sheet mass loss with superimposed ice sheet velocity streamlines from 2002-2016.

Antarctica ice sheet mass loss with superimposed ice sheet velocity streamlines from 2002-2016. Credit: NASA

The Intergovernmental Panel on Climate Change recently concluded that glacial melt and ice sheet loss is now the dominant contributor to global mean sea level rise. Summer melting of the Greenland ice sheet has increased to a level unprecedented over at least the last 350 years. Antarctic ice loss is dominated by acceleration, retreat, and rapid thinning of major West Antarctic outlet glaciers, driven by melting of ice shelves by warm ocean waters.

Changes in land water storage and ocean mass can be measured from space using the GRACE and GRACE-FO sensors. Data are available from 2002 to the present. These satellites unambiguously show that the Greenland and Antarctic ice sheets, as well as the glaciers, are shrinking; the Greenland ice sheet is decreasing by about 289 Gt per year, and the Antarctica ice sheet is decreasing by about 132 Gt per year (Hamlington, et al., 2020).

The GRACE and GRACE-FO Ocean, Ice, and Hydrology Equivalent Water Height dataset is gridded monthly global water storage/height anomalies relative to a time-mean. The data are processed at NASA's Jet Propulsion Laboratory (JPL) using the Mascon approach. Mass Concentration blocks (mascons) are essentially another form of gravity field basis function to which GRACE's inter-satellite ranging observations are fit. Using mascons rather than the standard spherical harmonic approach, which has been the standard for the first decade of GRACE/GRACE-FO observations, offers several key advantages. For more information on this approach, view Monthly Mass Grids.

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

The data can be visualized through Worldview or NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC) State of the Ocean (SOTO) tool. 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 ocean.

Ice Height and Ice Thickness

Scientists are working to determine more precisely how much more ice will be lost in both Greenland and Antarctica and when that loss will occur. One key approach to doing this is to analyze changes in the ice sheet's elevation over the past decades where satellite observations are available.

By finding the intersection of elevation track measurements collected by NASA's Ice, Cloud and land Elevation Satellite (ICESat) and Ice, Cloud and land Elevation Satellite-2 (ICESat-2) satellite laser altimeters, researchers are able to make very precise measurements of elevation change that can be converted into estimates of mass change after correcting for changes in snow density using models. ICESat collected data from 2003–2009 and ICESat-2 from 2018–present.

NASA's Operation IceBridge, bridging the temporal gap between the ICESat missions, images Earth's polar ice to better understand connections between polar regions and the global climate system. IceBridge studies annual changes in thickness of sea ice, glaciers, and ice sheets. IceBridge uses airborne instruments to map Arctic and Antarctic areas. IceBridge flights are generally conducted in March – May over Greenland and in October – November over Antarctica. Other smaller airborne surveys around the world are also part of the IceBridge campaign.

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

OpenAltimetry, which is the product of a collaboration between NSIDC, the Scripps Institution of Oceanography, and the San Diego Supercomputer Center at the University of California-San Diego, is a platform for discovery, access, and visualization of data from ICESat and ICESat-2.

Oceans Melting Greenland (OMG) is a NASA mission to understand the role that the ocean plays in melting Greenland's glaciers. From the sky and the sea, OMG gathers data about water temperatures and the glaciers all the way around Greenland to get a better idea of just how fast the ice is melting, and how fast global sea levels will rise.

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

  • OMG Glacial Elevations from Earthdata Search
    This dataset contains 50 m horizontal resolution gridded digital elevation models (DEMs) of Greenland Ice Sheet outlet glaciers between 2016 and 2019. The GLISTIN-A radar measured surface elevations around the periphery of the Greenland Ice Sheet.

Global Land Ice Measurements from Space map of glaciers in Peru, South America.

Global Land Ice Measurements from Space map of glaciers in Peru, South America.

The Global Land Ice Measurements from Space (GLIMS) Initiative has repeatedly surveyed the world's estimated 200,000 glaciers. GLIMS uses data collected by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument aboard the Terra satellite and the NASA/USGS Landsat series of satellites, along with historical observations. Each polygon within the Glacier Outlines layer represents the extent of a particular glacier at a specific time, as well as other possible features of the glacier such as the extent of debris cover or the location of supra-glacial and pro-glacial lakes.

NSIDC has a Greenland Surface Melt Extent Interactive Chart which provides a means of comparing surface melt over the years, from 1979-present. For daily satellite images and information about melting on the Greenland ice sheet, view NSIDC's Greenland Ice Sheet Today.

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Find Ocean Processes Data

Find Ocean Processes Data

Ocean Circulation | Surface Winds | Sea Surface Salinity | Sea Surface Temperature | Sea Surface Height

Ocean Circulation

OSCAR ocean flow data colored by velocity (with dates and color bar), from July 27, 2012. Credit NASA Scientific Visualization Studio

OSCAR ocean flow data colored by velocity (with dates and color bar), from July 27, 2012. Credit: NASA Scientific Visualization Studio

The ocean is dynamic. It is in a constant state of change as winds and density-driven currents transport water across the globe. These currents cause sea levels to differ regionally. The El Niño‐Southern Oscillation (ENSO), commonly known as El Niño, is a warm mass of water that moves from the Western Pacific Ocean toward the Americas. During El Niño events, as warm water pushes across the ocean, heat causes the ocean to expand, in turn causing sea level near the Americas to rise, with changes of up to 20 cm along the west coast of the U.S. (Hamlington, et al., 2015)

Surface current estimates from OSCAR provide horizontal velocity that is directly estimated from sea surface height (SSH), surface vector wind, and SST. These data were collected from various satellites and in situ instruments.

There are two OSCAR products to choose from: data on a 1 degree or a 1/3 degree grid with a 5-day resolution. Research-quality data products can be accessed via Earthdata Search:

Once you download the data, there are two options for currents if you use a program like Panoply to open the NetCDF file—zonal currents and meridional currents. When the upper level winds are parallel or nearly parallel to lines of latitude the wind pattern is termed zonal. When winds cross latitude lines at a sharp angle, the wind pattern is termed meridional.

The data can be visualized through SOTO.

Surface Winds

CYGNSS Ocean Windspeed data from Hurricane Florence acquired 9/14/18. Credit: NASA Disasters

CYGNSS Ocean Windspeed data from Hurricane Florence acquired September 14, 2018. Credit: NASA Disasters

NASA's Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of satellites that collect frequent remote sensing measurements of surface wind speeds. These satellites use 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.

Research-quality CYGNSS and MERRA-2 data products can be accessed via Earthdata Search; for subsetting CYGNSS data, use PO.DAAC's High-level Tool for Interactive Data Extraction (HiTIDE) Tool (see the Tools for Data Access and Visualization section for more 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.

The data can be visualized through PO.DAAC's SOTO tool:

Sea Surface Salinity

Salinity, the amount of salt dissolved in seawater, drives ocean currents that transport heat around the globe. Variations in salinity are negligible when considering GMSL; however it can be an important factor at the ocean-basin level, contributing to thermohaline circulation. Two NASA instruments have been measuring sea surface salinity (SSS) since 2011, Aquarius/SAC-D and the Soil Moisture Active Passive (SMAP).

Aquarius Version 5 is the official end-of-mission dataset, spanning the complete 45-month period of Aquarius science data availability from August 25, 2011 – June 7, 2015. Improving the accuracy of Aquarius' measurements has been a key mission activity to ensure that the data are most useful for science and society. There are two products, the official release and another from NASA's JPL based on the Combined Active Passive (CAP) retrieval algorithm.

NASA's SMAP satellite delivers derived SSS observations for the ocean and soil moisture. Algorithm development from the Aquarius mission is applied to SMAP to retrieve SSS. There are two products, one from JPL and one from Remote Sensing Systems (RSS). The JPL product is based on the CAP retrieval algorithm and provides a comparative view to RSS, which is based on averages spanning an 8-day moving window.

Note that the Aquarius data are only available from 2011–2015 and are at a much coarser spatial resolution than SMAP.

Locations of the Salinity Processes in the Upper Ocean Regional Study (SPURS) field-based campaigns.

Locations of the Salinity Processes in the Upper Ocean Regional Study (SPURS) field-based campaigns. Credit: NASA's Jet Propulsion Laboratory (JPL)

Research-quality SSS data products can be accessed via Earthdata Search; for subsetting SSS data, use PO.DAAC's HiTIDE Tool (see the Tools for Data Access and Visualization section for more information):

The data can be visualized through Worldview and PO.DAAC's SOTO tool.

A) Survey tracks of the 5 saildrones, 3 NASA and 2 NOAA, deployed during the Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC). B) Saildrone SD 1060 leaving Barbados. C) Along track surface temperature maps for the 3 NASA saildrones. D) Along track surface salinity maps for the 3 NASA saildrones.

A) Survey tracks of the 5 saildrones, 3 NASA and 2 NOAA, deployed during the Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC). B) Saildrone SD 1060 leaving Barbados. C) Along track surface temperature maps for the 3 NASA saildrones. D) Along track surface salinity maps for the 3 NASA saildrones. Credit: NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC)

There are also field-based campaigns that provide more information about SSS on a regional level. Salinity Processes in the Upper Ocean Regional Study (SPURS) is a pair of oceanographic field experiments using a variety of oceanographic equipment and technology, including salinity-sensing satellites, research cruises, floats, drifters, autonomous gliders, and moorings. The 2012–2013 SPURS-1 field campaign in the North Atlantic focused on a high salinity, high evaporation region. The 2016–2017 SPURS-2 field campaign is the center of the low surface salinity belt associated with the heavy rainfall of the intertropical convergence zone in the tropical Pacific.

Saildrone is a state-of-the-art, wind-and-solar-powered Uninhabited Surface Vehicle (USV) capable of long distance deployments lasting up to 12 months. This novel sampling platform is equipped with a suite of instruments and sensors providing high quality, georeferenced, near real-time, multi-parameter surface ocean and atmospheric observations.

Research-quality field-based salinity data can be accessed via Earthdata Search:

Thermal Expansion — Sea Surface Temperature

Sea surface temperature anomalies, September 21, 2020, from the Multiscale Ultrahigh Resolution data product. Visualization from the State of the Ocean (SOTO) tool. Credit: NASA

Sea surface temperature anomalies, September 21, 2020, from the Multiscale Ultrahigh Resolution data product. Visualization from the State of the Ocean (SOTO) tool. Credit: NASA

More than 90% of atmospheric heat is absorbed by the ocean, causing the ocean to warm and expand; this is called thermal expansion. This warming has contributed roughly one-third of the global sea-level rise observed by satellite altimeters since 2004. Measurements such as SST and SSH aid in our understanding of this process.

Satellites enable measurement of SST from approximately 10 µm below the surface (infrared bands) to 1 mm (microwave bands) depths using radiometers. The spatial patterns of SST reveal the structure of underlying ocean dynamics.

Research-quality data products from MODIS on Terra and Aqua, and from VIIRS on Suomi NPP and JPSS NOAA-20 can be accessed via Earthdata Search; for subsetting SST data, use PO.DAAC's HiTIDE Tool (see the Tools for Data Access and Visualization section for more 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.

The data can be visualized through Worldview and PO.DAAC's SOTO tool.

SST data from satellites can be combined to create high-resolution Level 4 SST datasets, such as the Group for High Resolution Sea Surface Temperature (GHRSST) MUR SST. MUR provides daily SST at 1 km spatial resolution from June 2002 to present. The dataset is based upon nighttime skin (or surface) and sub-skin observations from several instruments, including satellite and in situ observations.

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

The data can be visualized through Worldview and PO.DAAC's SOTO tool.

Thermal Expansion — Sea Surface Height

Satellite altimeters make observations of SSH as oscillations higher or lower than an established reference height at the ocean surface. SSH data provide critical information to scientists and researchers for understanding sea level rise, storm predictions, ocean currents, and more.

TOPEX/Poseidon was an altimetric mission jointly collaborated by NASA and the French space agency, Centre National D'Etudes Spatiales (CNES). Jason-1 was the first follow-on to the highly successful TOPEX/Poseidon mission; it was followed by OSTM/Jason-2 and now the ongoing Jason-3. Between these four missions, SSH has been collected from 1992 to the present, providing a long-term time series of data.

Research-quality data products can be accessed via Earthdata Search; for subsetting SSH data, use PO.DAAC's HiTIDE Tool (see the Tools for Data Access and Visualization section for more information):

ICESat-2 also collected SSH at variable length scales over cloud-free regions. Research-quality data products can be accessed via Earthdata Search:

Because of the long time series of the data, several merged data products have been developed to assess SSH anomalies. The gridded data are derived from the SSH anomalies data of TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 as reference data. Research-quality data products can be accessed via Earthdata Search:

The data can be visualized through Worldview and PO.DAAC's SOTO tool.

The Reconstructed Sea Level dataset contains sea level anomalies derived from satellite altimetry and tide gauges. The satellite altimetric record provides accurate measurements of sea level with near-global coverage, but it has a relatively short time span, since 1993. Tide gauges have measured sea level over the last 200 years, with some records extending back to 1807, but they only provide regional coverage, not global. Combining satellite altimetry with tide gauges, using a technique known as sea level reconstruction, results in a dataset with the record length of the tide gauges and the near-global coverage of satellite altimetry. This data product spans from 1950 through 2009.

Sentinel-6 Michael Freilich, launching November 2020, is a follow on to the Jason missions and is designed to measure the height of the ocean; it will ensure the continuation of a decades-long record of sea level observations until 2030. This pathfinder will be updated with data as they becomes available. NASA is developing the mission with the ESA, the European Organisation for the Exploitation of Meteorological Satellites, and NOAA. The European Commission is providing funding support. CNES is also supporting the mission.

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Find Land Water Storage Data

Find Land Water Storage Data

Water Storage Anomalies | Terrestrial Surface Waters

The ocean plays a huge role in the hydrologic cycle, transporting water around the globe. The amount of water on Earth does not change, but the reservoir in which it resides and its state of matter does change as it cycles through evaporation, precipitation, groundwater, rivers and lakes, and the ocean. Humans impact these reservoirs through the construction of dams, creating large, artificial bodies of water on land, reducing outflow to the ocean (GMSL fall). Scientific research (Wada, et al., 2017) estimates that humans have so far captured a total of 10,416 km3 of water behind dams, which represents the equivalent of a 29 mm decrease in GMSL since 1900.

Humans also alter the hydrologic cycle through groundwater withdrawal, depleting regional storage of water on land and contributing more to the ocean (GMSL rise). Scientific research (Wada, et al., 2016) estimates that about 80% of groundwater withdrawal ends up in the ocean. This contribution to GMSL has increased from 0.02 (±0.004) mm/y in 1900 to 0.27 (±0.04) mm/yr in 2000.

Water Storage Anomalies

Changes in land water storage (and ice and ocean mass changes) can be measured from space using the GRACE and GRACE-FO sensors. Data are available from 2002 to the present; the data track anomalies (changes from the mean) and so are not representative of total water storage. Note that the resolution of the data are greater than 150,000 km2 so the sensors only measure change within large reservoirs. The value of GRACE data is evident when doing regional studies to determine general trends in land water storage.

GRACE and GRACE-FO Ocean, Ice, and Hydrology Equivalent Water Height dataset is gridded monthly global water storage/height anomalies relative to a time-mean. The data are processed at NASA's JPL using the Mascon approach. Mascons are essentially another form of gravity field basis functions to which GRACE's inter-satellite ranging observations are fit. Using "mascons" rather than the standard spherical harmonic approach, which has been the standard for the first decade of GRACE/GRACE-FO observations, offers several key advantages. For more information on this approach, view Monthly Mass Grids.

Data are represented as Water Equivalent Thickness (WET), which is a way of representing changes in the gravity field in hydrological units. The WET represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (including rivers, lakes, and reservoirs), as well as groundwater and aquifers. A GIA correction has been applied.

​Water Equivalent Thickness (WET)​ from data run with each algorithm, that from the GeoForschungsZentrum Potsdam (GFZ), the Center for Space Research at the University of Texas, Austin (CSR) and the Jet Propulsion Laboratory (JPL)​. Th final is the arithmetic mean of the three, calculated in a GIS program.

Water Equivalent Thickness (WET) from GRACE data run with each algorithm, that from the GeoForschungsZentrum Potsdam (GFZ), the Center for Space Research at the University of Texas, Austin (CSR) and the Jet Propulsion Laboratory (JPL). Th final is the arithmetic mean of the three, calculated in a GIS program.

Research-quality data products can be accessed via Earthdata Search; datasets are available as NetCDF files which can be opened using Panoply or imported into a GIS system.

The data can be visualized through Worldview and PO.DAAC's SOTO tool. The mascon CRI layer provides global water storage anomalies relative to a time-mean of monthly mass grids as derived from GRACE. This version employs a CRI filter that reduces leakage errors across coastlines. The storage anomalies are given in equivalent water thickness units (cm).

Terrestrial Surface Waters

Surface Water and Ocean Topography (SWOT), targeted to launch early 2022, is a mission jointly developed by NASA and CNES, with contributions from the Canadian Space Agency and the United Kingdom Space Agency. SWOT will make the first global survey of Earth's surface water, observe the fine details of the ocean's surface topography, and measure how terrestrial water bodies change over time.

SWOT will provide the very first comprehensive view of Earth's freshwater bodies from space and will allow scientists to determine changing volumes of fresh water across the globe at an unprecedented resolution. Hydrologists will use the data to calculate the rate of water gained or lost in lakes, reservoirs, and wetlands as well as discharge variations in rivers, globally. These measurements are key to understanding surface water availability and in preparing for important water-related hazards such as floods, droughts, and sea level change.

The Pre-SWOT Hydrology is part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. The Pre-SWOT Hydrology project provides precursor SWOT products for global hydrologic changes from a combination of satellite imagery and multi-mission satellite radar altimetry data, including virtual river station heights, lake/reservoir surface water area extent, and lake/reservoir water height spanning from 1992 (TOPEX/Poseidon launch) to present, with the potential to be extended up to the launch of the SWOT mission planned for 2022.

Research-quality data products can be accessed via Earthdata Search; datasets are available as NetCDF files which can be opened using Panoply or imported into a GIS system.

Find Vertical Land Motion Data

Find Vertical Land Motion Data

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 subsidence in an area. 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; ESA's Sentinel-1, with a radar length of 6 cm has contour lines indicating ground deformation of 3 cm.

Research-quality data products can be accessed via Earthdata Search or from NASA's Alaska Satellite Facility DAAC (ASF DAAC).

The upcoming NASA-Indian Space Research Organisation SAR (NISAR) mission, launching in 2022, will measure Earth's dynamic surfaces and ice masses providing information about natural hazards, sea level rise, and groundwater, and will support a host of other applications. This pathfinder will be updated with data as they becomes available.

To learn more about SAR, read What is SAR?. To learn about processing Level 1 data, view NASA's Applied Remote Sensing Training (ARSET) Introduction to SAR training or the Earthdata webinars, Introduction to SAR Data and Applications of SAR Data in GIS Environments.

Find Glacial Isostatic Adjustment Data

Find Glacial Isostatic Adjustment Data

GRACE/GRACE-FO contemporary geoid rates (in mm/yr) from Glacial Isostatic Adjustment (GIA) as predicted by the ICE6G-D model.

GRACE/GRACE-FO contemporary geoid rates (in mm/yr) from Glacial Isostatic Adjustment (GIA) as predicted by the ICE6G-D model. Credit: NASA

The last Ice Age occurred around 16,000 years ago. At that time, ice sheets, miles thick, covered much of the Northern Hemisphere. The ice sheets melted long ago, but the land underneath is still rebounding as a result of the removal of that burden. This ongoing movement of land is known as GIA. Imagine lying down on a soft mattress and then getting up from the same spot. You see an indentation in the mattress where your body was, and a puffed-up area around the indentation where the mattress rose. Once you get up, the mattress takes a little time before it relaxes back to its original shape. Sea level is still changing as this land rebound happens.

When processing GRACE and GRACE-FO measurements to obtain land water storage, GIA has to be accounted for and removed to truly isolate the water-related mass changes. In doing so, NASA has a fairly good idea of that which was removed. For more information on this process, see GRACE TELLUS GIA.

Research-quality data can be obtained via Earthdata Search; datasets are available in NetCDF format, and are available at 0.5° and 1°.

Find Sea Level Change Impacts Data

Find Sea Level Change Impacts Data

Land Surface Reflectance | Socioeconomic Data | Nighttime Lights | Delta X Field Campaign

Assateague and Chincoteague provide a rare example of overlapping barrier islands. All of them are constantly in motion. June 2, 2019 image from the NASA/USGS Landsat 8 Operational Land Imager (OLI). Credit: NASA Earth Observatory

Assateague and Chincoteague provide a rare example of overlapping barrier islands. All of them are constantly in motion. June 2, 2019 image from the NASA / USGS Landsat 8 Operational Land Imager (OLI). Credit: NASA Earth Observatory

Sea level change, especially in areas where it is on the rise, is having often catastrophic impacts to coastal communities. As many of our barrier islands and beach fronts are populated, human-built infrastructure prevents the island from moving naturally and so with sea level rise, erosion becomes a dangerous effect. In addition, the amount of nuisance, or "sunny day" flooding has increased in the U.S. on average by about 50% since 20 years ago and 100% since 30 years ago, according to NOAA. Other impacts include saltwater intrusion, habitat destruction, and forced migration of often low-income communities.

Land Surface Reflectance

NASA has several products that can be used to qualitatively assess these impacts from sea level rise: NASA/USGS Landsat, MODIS, and VIIRS.

Research-quality (higher-level "standard") data products can be accessed via Earthdata Search or through NASA partner websites:

  • MODIS Surface Reflectance from Earthdata Search
    Note that a false-color image created by combining bands 7 as red, 2 as green, and 1 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. To only choose those bands, see the customization option within Earthdata Search in the Tools for Data Access and Visualization section.
  • Suomi NPP VIIRS Surface Reflectance from Earthdata Search
    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. To only choose those bands, see the customization option within Earthdata Search in the Tools for Data Access and Visualization section.
  • Landsat Data from USGS Earth Explorer
    Landsat is a joint NASA/USGS program that provides the longest continuous space-based record of Earth's land in existence. On the Earth Explorer site, specify your search criteria, then:
    1. Select "Data Sets"
    2. Select Landsat
    3. Select Landsat Collection 1 Level-1
    4. Select Landsat 7 and/or Landsat 8
    These files can be downloaded as Level-1 GeoTIFF Data Products. Note that you will need a USGS login to proceed.

Data (often in near real-time) can be visualized in Worldview:

  • Suomi NPP 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, or M3-I3-M11, 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.
  • NOAA-20 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.

Socioeconomic Data

Population density and low elevation coastal zone areas in greater New York City; data from the Urban-Rural Population and Land Area Estimates Version 2. Credit: Socioeconomic Data and Applications Center (SEDAC)

Population density and low elevation coastal zone areas in greater New York City; data from the Urban-Rural Population and Land Area Estimates Version 2. Credit: Socioeconomic Data and Applications Center (SEDAC)

NASA's Socioeconomic Data and Applications Center (SEDAC) provides data and tools to aid in hazards assessment. SEDAC's POPGRID Viewer enables direct comparison of different population datasets based on different data sources and methodologies. SEDAC's Hazards Mapper enables users to visualize data and map layers related to socioeconomic, infrastructure, natural disasters, and environment and analyze potential impacts and exposure.

Data products, such as sea level impact on wetlands, coastal zone urban-rural population, and more, can be accessed via Earthdata Search:

Nighttime Lights

The VIIRS Day/Night Band (DNB) 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 or changes in nighttime light conditions across an area, due to flood conditions.

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 DNB radiances. Black Marble data can be accessed at NASA's Level-1 and Atmosphere Archive and Distribution System DAAC (LAADS DAAC). Black Marble imagery in Worldview is an image composite that was assembled from clear, cloud-free images for 2012 and 2016.

Delta-X Field Campaign

Millions of people live on the Mississippi Delta, along with a unique ecosystem of plants and animals. On average, one football field of land is lost per hour.

Millions of people live on the Mississippi Delta, along with a unique ecosystem of plants and animals. On average, one football field of land is lost per hour. Credit: NASA's Jet Propulsion Laboratory (JPL)

River deltas build land where a river reaches a shallow coast and deposits large amounts of material. As sea level rises, certain deltas can be inundated, affecting many ecosystem services. The Delta-X mission studies the Mississippi River Delta, the most famous river delta in the United States and the seventh largest river delta on Earth. This area experiences some of the largest sea level rise in the world, at 9–12 mm per year. This land loss is happening to deltas all over the world, but it is happening faster here due to subsidence.

The Delta-X mission uses airborne (remote sensing) and field measurements to look at the water, vegetation, and sediment (soil). Campaigns are scheduled for 2021, but Pre-Delta-X data are available via Earthdata Search:

Other NASA Assets of Interest

Other NASA Assets of Interest

NASA's Sea Level Change: Observations from Space portal provides a wealth of information on the factors contributing to sea level change, globally and regionally. The site gives estimates for sea level rates for different time periods and data on the key indicators of change.

NASA's Climate Time Machine takes viewers on a journey to show how Earth's key climate indicators are changing over time. The sea level indicator contains visualizations to show the effect on coastal regions for each meter of sea level rise, up to 6 meters (19.7 feet).

External Resources

External Resources

NOAA's Sea Level Rise Viewer provides data and maps to illustrate the scale of potential flooding. Water levels are relative to Mean Higher High Water (MHHW) (excludes wind driven tides). Credit: NOAA

NOAA's Sea Level Rise Viewer provides data and maps to illustrate the scale of potential flooding. Water levels are relative to Mean Higher High Water (MHHW) (excludes wind driven tides). Credit: NOAA

NOAA's Argo is an international program that calls for the deployment of 3,000 free drifting profiling floats, distributed over the global ocean, which will measure the temperature and salinity in the upper 2,000 m of the ocean.

NOAA's Sea Level Rise Viewer is a web mapping tool to visualize community-level impacts from coastal flooding or sea level rise (up to 10 feet above average high tides). Photo simulations of how future flooding might impact local landmarks are also provided, as well as data related to water depth, connectivity, flood frequency, socio-economic vulnerability, wetland loss and migration, and mapping confidence.

The Colorado Center for Astrodynamics Research at the University of Colorado at Boulder, funded by NASA's OSTM/Jason mission, has a Sea Level Research Group that continuously monitors satellite altimetry data against a network of tide gauges, subtracting seasonal variations to estimate global mean sea level rate.

Benefits and Limitations of Remote Sensing Data

Benefits and Limitations of Remote Sensing Data

In determining whether or not to use remote sensing data, it is important to understand not only the benefits but also the limitations of the data. Benefits of using satellite data include:

  • Filling in data gaps: The United States is fortunate to have numerous ground-based measurements for assessing water storage, precipitation, and more. However, this is not the case in other countries and even in some of the more remote areas of the United States. Satellite data provide local, regional, and global spatial coverage and are also useful for observing areas that are inaccessible.
  • Monitoring in near-real time: Some satellite information is available 3–5 hours after observation, allowing for a faster response. NASA's LANCE supports users interested in monitoring a wide variety of natural and man-made phenomena in a timely manner.

With satellite data, assessments can be made regarding the land surface, precipitation events, ground movement, and air temperature. In addition, incorporating satellite data with in situ data into modeling programs makes for a more robust and integrated forecasting system.

  • Spatial resolution: While lower resolution data provide a more global view, as with the Aqua/Terra MODIS measurements, the spatial resolution is too coarse for certain assessments. This is not the case for instruments at higher resolutions, like those on the NASA/USGS Landsat series.
  • Temporal resolution: Many satellites only pass over the same spot on Earth every 1–2 days and sometimes as seldom as every 16+ days. This is the satellite's return period.
  • Passive instruments (those that use energy being reflected or emitted from Earth for measurements) are not able to penetrate cloud or vegetation cover, which can lead to data gaps or a decrease in data utility. This is not the case when using data from microwave or thermal sensors (active sensors).

It is difficult to combine all of the desirable features into one remote sensor; to acquire observations with moderate to high spatial resolution (like NASA/USGS Landsat) a narrower swath is required, which in turn requires more time between observations of a given area, resulting in a lower temporal resolution. Researchers have to make trade-offs. Finding a sensor, or sensors, with the spatio-temporal resolution capable of addressing your research, application, or decision-making process needs is a crucial first step to getting started with using remote sensing data. For more information on resolutions, see What is Remote Sensing?

Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | HiTIDE | SOTO | AppEEARS | MODIS/VIIRS Subsetting Tools Suite | Sentinel Toolbox

Earthdata Search

Earthdata Search is a tool for data discovery of Earth Observation data collections from NASA's 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 in NetCDF format 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-East (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.

HiTIDE

HiTIDE allows users to subset and download popular PO.DAAC level 2 datasets. Users can search across a wide variety of parameters, such as variables, sensors and platforms, and filter the resulting data based on spatial and temporal boundaries of interest to the user. HiTIDE goes even further, offering instant previews of variable imagery, allowing users to rapidly find data of interest for download and further, rigorous scientific analysis. A tutorial is provided for new users by clicking on the question mark in the upper right corner.

screen capture of HiTIDE Tool

SOTO

PO.DAAC's SOTO is an interactive web-based tool that generates informative maps, animations, and plots that communicate and prove the discovery and analysis of the state of the oceans.

The suite of tools provide access to a broad range of satellite-derived products and key parameters of interest to the oceanographic community.

screen capture of SOTO Tool

AppEEARS

AppEEARS, from NASA's Land Processes DAAC (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.

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 ESA Sentinel-1 Mission consists of two satellites, Sentinel-1A and Sentinel-1B, with 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 that can penetrate cloud cover and vegetation canopy, and also 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, however, ESA has developed a Sentinel-1 Toolbox to aid with processing and analysis of Sentinel-1 data.

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 Shuttle Range Topography Mission 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.

References:

  • Sea Level Change Observations from Space
  • Hamlington, B. D., Gardner, A.S., Ivins, E., Lenaerts, J.T.M., Reager, J.T., Trossman, D.S., et al. (2020). Understanding of contemporary regional sea‐level change and the implications for the future. Reviews of Geophysics, 58(3), doi: 10.1029/2019RG000672.
  • Hamlington, B.D., Leben, R.R., Kim,K.-Y., Nerem, R.S., Atkinson, L.P., and Thompson, P.R. (2015). The effect of the El Niño‐Southern Oscillation on U.S. regional and coastal sea level, JGR Oceans, 120(6), 3970-3986, doi: 10.1002/2014JC010602.
  • Wada, Y., J.T. Reager, B.F. Chao, J. Wang, M.-H. Lo, C. Song, Y. Li, and A.S. Gardner, (2017). Recent changes in land water storage and its contribution to sea level variations. Surveys in Geophysics, 38(1), 131-152, doi: 10.1007/s10712-016-9399-6.
  • Wada, Y., M. Lo, P. Yeh, J.T. Reager, J.S. Famiglietti, R.-J. Wu, and Y.-H. Tseng, (2016). Fate of water pumped from underground and contributions to sea-level rise. Nature Climate Change, 6, 777–780, doi: 10.1038/nclimate3001.

Published October 30, 2020

Page Last Updated: Nov 12, 2020 at 11:13 AM EST