Data Tools
Explore web services, interactive tutorials, and other tools to access and work with NSIDC data
There are many ways to access and work with NSIDC data to accommodate a wide diversity of users. Use the filter menu to explore and narrow down your options. Note that the list defaults to show our Featured tools first, however, you have more options in the Sort by menu.
- To see which tools are available for which data sets, you must go to a specific data set landing page. On the landing page under Data Access and Tools, you will see data-set specific tools to access and work with the data.
- Once you are ready to find a data set, explore our data catalog.
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Search, visualize, and download data from NSIDC's long-term IceBridge archive. The portal provides flight lines, map-based granule extents, browse images (where available), Northern and Southern Hemisphere polar views, spatial and temporal filters, and keyword search options.
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Discover, access, and visualize data from NASA's ICESat and ICESat-2 missions.
Supported software languages:
Python
Type: Web Application
Customization Capabilities:
spatial subsetting
temporal subsetting
Output Formats:
ASCII
CSV
HDF5
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Search and order data from all NASA DAACs using spatial and temporal filters in a map interface. Reformatting, reprojecting, and subsetting options are available for some data sets.
Type: Web Application
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GIS
Browse and display global satellite imagery layers from NASA GIBS. Download raw data, share screenshots, and create animations from the imagery.
Type: Web Application
Customization Capabilities:
data reformatting
Output Formats:
GEOTIFF
GIF
JPEG
KML
PNG
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GIS
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Visualize and download data from the GLIMS Glacier Database using a map-based interface.
Type: Web Application
Output Formats:
KML
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GIS
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Extract point and area samples from geospatial data via spatial, temporal, and/or parameter subsetting. Preview and interact with samples before downloading. The Application for Extracting and Exploring Analysis Ready Samples tool is only available for select NSIDC data products.
Type: Web Application
Customization Capabilities:
data reformatting
reprojection
spatial subsetting
temporal subsetting
variable subsetting
Output Formats:
CSV
GEOTIFF
HDF-EOS2
HDF5
JSON
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Search the Glacier Photograph Collection by glacier name, location, date, photographer, and more. View lower-resolution photographs online and order high-resolution versions.
Output Formats:
ASCII
JPEG
TIFF
Visualize
Browse Arctic- and Antarctic-wide changes in sea ice. View daily and monthly averages of extent and concentration. View time series graphs of Arctic and Antarctic sea ice.
Output Formats:
PNG
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View and download the most recent daily observations of sea ice extent and ice edge boundary for the entire Arctic and/or a specific region. Compare time series of sea ice extent from the most recent four weeks to the same four-week period in the previous four years.
Output Formats:
CSV
GEOTIFF
NETCDF-4
PNG
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Customize
Learn how to discover, access, subset, and visualize Arctic sea ice data from Python-based Jupyter Notebooks. Tutorial comes with open-source libraries to harmonize sea ice height from ICESat-2, and ice surface temperature from MODIS.
Supported software languages:
Python
Customization Capabilities:
spatial subsetting
temporal subsetting
variable subsetting
reprojection
Output Formats:
HDF-EOS2
HDF5
Visualize
Display browse images for five different parameters from three different NOAA@NSIDC Arctic sea ice products in a spreadsheet-like format.
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See how Arctic/Antarctic sea ice extent changes throughout the calendar year using a customizable graph in a web browser. Compare extents for all years since 1979, and click on any point in any year's trajectory for a map of daily sea ice concentration.
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Demonstrates using the earthaccess python package to search for and directly access cloud-hosted ICESat-2 data from an Amazon Compute Cloud (EC2) instance. This python-based Jupyter notebook uses Land Ice Height (ATL06) granules as an example.
Supported software languages:
Python
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Utilize the Jupyterbook for the latest Hackweek virtual event and learn about the ICESat-2 satellite, data products, data-access tools, and more. Access past hackwork tutorials from the ICESat-2 Hackweek Github Organization listed in the Quick Links.
Supported software languages:
Python
Type: Downloadable Software
Customization Capabilities:
spatial subsetting
temporal subsetting
variable subsetting
Visualize
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Customize
Spatially and temporally filter Greenland Ice Mapping Project (GIMP) image and velocity data, guided by a Python-based Jupyter Notebook. Spatially subset data and download in NetCDF format. This tool creates an interactive plot of time series data at selected points.
Supported software languages:
Python
Type: Downloadable Software
Last updated:
Customization Capabilities:
spatial subsetting
variable subsetting
data reformatting
Output Formats:
NETCDF-4
Visualize
Calculate past and future satellite overpasses, based on latitude/longitude or map location.
Type: Web Application
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Visualize changes in Arctic air temperatures, frozen ground, sea ice concentration and age, snow cover duration, vegetation greenness, and water vapor. Animate maps and time series graphs.
Analyze
Visualize
Analyze monthly or daily sea ice concentration and extent. Plot monthly extent anomalies. Display concentration trend images based on date, climatology, and trend range.
Output Formats:
JPEG
PNG
Visualize
View animated maps of monthly sea ice extent, concentration, anomalies and trends for the Northern and Southern Hemisphere.
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Compare sea ice extent for any two days or months between 1979 and present.
Output Formats:
PNG
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Search for and download NSIDC DAAC data using the earthaccess python package. This python-based Jupyter notebook also demonstrates geospatial operations to crop and resample one GeoTIFF based on the extent and pixel size of another GeoTIFF, with the end goal of plotting one on top of the other. Two data sets from the NASA MEaSUREs program are used as examples.
Supported software languages:
Python
Customization Capabilities:
spatial subsetting
resampling
Output Formats:
GEOTIFF
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Download, bulk download, or visualize SMAP L3 and L4 data, guided by Python-based Jupyter Notebooks. These notebooks include examples of applying recommended quality flags for SMAP data.
Supported software languages:
Python
Output Formats:
HDF5
Analyze
Visualize
Search & Discover
GIS
Customize
A Python-based Jupyter Notebook demonstrating how to access and visualize coincident snow data from the NSIDC DAAC across in-situ, airborne, and satellite platforms from NASA's SnowEx, ASO, and MODIS data sets, respectively.
Supported software languages:
Python
Customization Capabilities:
data reformatting
reprojection
spatial subsetting
Output Formats:
CSV
GEOTIFF
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