ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 6
Data set id:
ATL08
DOI: 10.5067/ATLAS/ATL08.006
This is the most recent version of these data.
Version Summary
Version Summary
Changes for Version 6 are as follows:
- Added a quality check to reject segments for which the canopy photons fall more than 150 m below the reference DEM
- Calculated the background noise rate and number of noise photons within a canopy segment and adjusted the canopy radiometric rate accordingly
- For segments with a solar elevation angle > 20°, if the background noise rate is < 0.98 of the canopy rate, then reassign the canopy photons as noise photons
- Incorporated the YAPC photon weights from the ATL03 data product into the ground-finding approach
- Reduced the number of labeled photons required to report the canopy or terrain heights within each segment for the strong and weak beams, resulting in more ATL08 reported values
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):
TERRAIN ELEVATION
Platform(s):
ICESat-2
Sensor(s):
ATLAS
Data Format(s):
HDF5
Temporal Coverage:
14 October 2018 to present
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.
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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.