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Description
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN 5.2 radiative transfer model). This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. AG100 data are available globally at spatial resolution of 100 meters.
The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.
Known Issues
Known issues are provided in Section 4, starting on page 8 of the User Guide.
Version Description
The following changes and improvements were implemented for ASTER GED Version 3 data products: (1) Expansion of spatial extent to a global scale, and (2) Inclusion of mean climatology of all ASTER data for the 2000-2008 temporal extent.
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
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Citation Copied
NASA JPL. (2014). ASTER Global Emissivity Dataset, 100-meter, HDF5 [Dataset]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/COMMUNITY/ASTER_GED/AG100.003 Date Accessed: 2026-08-13
@article{dugerdil_joannin_peyron_jouffroy-bapicot_vannière_boldgiv_behling_ménot_2021,
title={New Mongolian-Siberian pollen and brGDGT surface dataset: local calibration for paleoclimate reconstructions},
url={https://doi.pangaea.de/10.1594/PANGAEA.933664},
DOI={10.1594/PANGAEA.933664},
abstractNote={Paleoclimate reconstructions appear to be unavoidable steps in the future climate change understanding and especially the local calibration of the paleoclimate proxies. The Mongolian Plateau in particular, is a scarcely studied area. Here we present a latitudinal transect from the southern Siberian Baikal area to the Mongolian part of the Gobi desert: the New Mongolian-Siberian DataBase (NMSDB). The 49 surface samples presented in this dataset are from different types: moss polsters, surface soil samples and lacustrine top-cores. Two paleoclimate proxies have been carried here: pollen analysis and biomarkers (glycerol dialkyl glycerol tetraethers, GDGTs). The actual bioclimate parameters of each sample sites are derived from the ASTER data (NASA, 2014) for the elevation (m a.s.l.) and the WorldClim2.0 interpolated climate database (Fick et Hijmans, 2017) for the climate parameters. Fick, Stephen E., et Robert J. Hijmans. 2017. ' WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas: NEW CLIMATE SURFACES FOR GLOBAL LAND AREAS ». International Journal of Climatology 37 (12): 4302‑15. https://doi.org/10/gb2jnq. NASA JPL. 2014. ' ASTER Global Emissivity Dataset, 100-meter, HDF5 ». NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/COMMUNITY/ASTER_GED/AG100.003.},
publisher={PANGAEA},
author={Dugerdil, Lucas and Joannin, Sébastien and Peyron, Odile and Jouffroy-Bapicot, Isabelle and Vannière, Boris and Boldgiv, Bazartseren and Behling, Hermann and Ménot, Guillemette},
year={2021},
language={en},
}
NASA JPL. (2014). ASTER Global Emissivity Dataset, 100-meter, HDF5 [Dataset]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/COMMUNITY/ASTER_GED/AG100.003 Date Accessed: 2026-08-13
NASA JPL. “ASTER Global Emissivity Dataset, 100-Meter, HDF5.” NASA Land Processes Distributed Active Archive Center, 2014, https://doi.org/10.5067/COMMUNITY/ASTER_GED/AG100.003. Date Accessed: 2026-08-13
File Naming Convention
AG100.v003.-26.113.0001.h5
The file name begins with Product Short Name (AG100) followed by the Version (v003), the northwest corner Latitude (-26), the northwest corner Longitude (113), the Spatial Resolution Identifier (0001), and the Data Format (h5).
Barreras, Aylin, Alanis de la Rosa, Jose Armando, Mayorga, Rafael, Cuenca, Rubi, MorenoG, Cesar, Godinez, Carlos, Delgado, Carina, SorianoLuna, Maria de los Angeles, George, Stephanie, AldreteLeal, Metzli Ileana, Medina, Sandra, Romero, Johny, Villela, Sergio, Lister, Andrew, Sheridan, Rachel, Flores, Rafael, Crowther, Thomas W., Guevara, Mario
Barros, F.de.V., Lewis, K., Robertson, A.D., Pennington, R.T., Hill, T.C., Matthews, C., Lira-Martins, D., Mazzochini, G.G., Oliveira, R.S., Rowland, L.
Fahlen, Jay E., Brodrick, Philip G., Thompson, David R., Herman, Robert L., Hulley, Glynn, Cawse-Nicholson, Kerry, Green, Robert O., Green, Joseph J., Hook, Simon J., Miller, Charles E.
The table below lists the variables contained within a single granule for this
dataset. Variables often contain observed or derived geophysical measurements
collected from a variety of sources, including remote sensing instruments on
satellite and airborne platforms, field campaigns, in situ measurements, and
model outputs. The terms variable, parameter, scientific data set, layer, and band
have been used across NASA’s Earth science disciplines; however, variable is the
designated nomenclature in NASA’s Common Metadata Repository (CMR).
Variable metadata attributes such as Name, Description, Units, Data Type, Fill
Value, Valid Range, and Scale Factor allow users to efficiently process and analyze
the data. The full range of attributes may not be applicable to all variables.
Additional information on variable attributes is typically available in the data,
user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.