ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 5
Data set id:
ATL08
DOI: 10.5067/ATLAS/ATL08.005
There is a more recent version of these data.
Version Summary
Version Summary
Changes for this version include:
- Added terrain_best_fit_geosegment parameter at the 20 m (geosegment) rate. This represents the best estimate of terrain at the 20 m along-track resolution.
- Added h_canopy_geosegment which represents the 20 m estimate of the 98% relative canopy height.
- Added latitude_20 and longitude_20 to the data product for geolocation of the 20 m terrain and canopy height estimates
- Added a segment_woody_vegetation_fractional cover to the ATL08 data product. This product is derived from the Copernicus fractional forest and fraction shrub data products
- Updated the segment_landcover with the 2019 Copernicus landcover. This update replaces the MODIS landcover value which was derived from the 2014 MODIS product. See the version section in the user guide for more information.
- Updated the urban_flag parameter with the DLR Global Urban Footprint (GUF) as a potential indicator of man-made/built structures. See the version section in the user guide for more information.
Overview
This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.
Parameter(s):
CANOPY HEIGHTTERRAIN ELEVATION
Platform(s):
ICESat-2
Sensor(s):
ATLAS
Data Format(s):
HDF5
Temporal Coverage:
14 October 2018 to 13 October 2022
Temporal Resolution:
- 91 day
Spatial Resolution:
- Not Specified
Spatial Reference System(s):
WGS 84
EPSG:4326
Spatial Coverage:
N:
90
S:
-90
E:
180
W:
-180
Blue outlined yellow areas on the map below indicate the spatial coverage for this data set.
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This article covers frequently asked questions about the NASA NSIDC DAAC's Earthdata cloud migration project and what it means to data users.
This short article describes the customization services available for ICESat-2 data using Earthdata Search.
How to Articles
Many NSIDC DAAC data sets can be accessed using the NSIDC DAAC's Data Access Tool. This tool provides the ability to search and filter data with spatial and temporal constraints using a map-based interface.Users have the option to
Harmony API Quickstart Guide: Customizing NASA NSIDC DAAC data in Earthdata Cloud
To convert HDF5 files into binary format you will need to use the h5dump utility, which is part of the HDF5 distribution available from the HDF Group. How you install HDF5 depends on your operating system.
Learn the basic steps for using OpenAltimetry to browse and download ICESat-2 data products.
This guide will provide an overview of the altimetry measurements and data sets across the missions, as well as a guide for accessing the data through NASA Earthdata Search and programmatically using an Application Programming Interface (API).
The NASA Earthdata Cloud is the NASA cloud-based archive of Earth observations. It is hosted by Amazon Web Services (AWS). Learn how to find and access NSIDC DAAC data directly in the cloud.
All data from the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) is directly accessible through our HTTPS file system using Wget or curl. This article provides basic command line instructions for accessing data using this method.
This article highlights the NSIDC DAAC data sets available with customization options and outlines a workflow for searching, ordering, and customizing data in NASA Earthdata Search. This approach is ideal for users who want to download data to their local machine.
This webinar introduces the ICESat-2 mission and shows you how to explore, access and customize ICESat-2 data with the OpenAltimetry application, using NSIDC DAAC tools, and shows you how to subset, reformat and analyze the data using Python.