Global Land Survey 2005 images were acquired from 2003 - 2008 by Landsat 7 ETM+, Landsat 5 Thematic Mapper (TM) and EO-1 ALI. The U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.

Global Land Survey 1990 images were acquired from 1987 to 1997 by Landsat 4-5 TM. The U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.

The U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. NASA and the USGS have again partnered to develop the Global Land Survey 2010 (GLS2010), a new global land data set with core acquisition dates of 2008-2011. This dataset consists of both Landsat TM and ETM+ images that meet quality and cloud cover standards established by the earlier GLS collections. Data acquired in 2011 were used to fill areas of low image quality or excessive cloud cover.

Global Land Survey 1975 images were acquired from 1972 to 1987 by Landsat 1-5 MSS. Landsat 4-5 data were used to fill gaps in the Landsat 1-3 data. The U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.

Global Land Survey 2005 images were acquired from 2003 to 2008 by Landsat 7 ETM+, EO1 ALI, and Landsat 5 Thematic Mapper (TM). The U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) collaborated on the creation of the global land datasets using Landsat data from 1972 through 2008. Each of these global datasets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, the Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+, as well as EO-1 ALI data, in 2005.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Annual (GWELDYR) Version 3 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over annual reporting periods for the 2010 epoch. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics. The GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid Provided in the GWELDYR product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Web-Enabled Landsat Data Annual (GWELDYR) Version 3 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over annual reporting periods for the 2010 epoch. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.The GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile gridProvided in the GWELDYR product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.Known Issues* GWELDYR known issues can be found in Section 4 of the Algorithm Theoretical Basis Document (ATBD). Please note that the version 3.0 GWELD products for 2009, 2010 and 2011 are defined in HDF4 which cannot be read correctly by GDAL/ArcGIS. This is not an issue for the version 3.1 GWELD products.

'The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from five sensors: Operational Land Imager, Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. This newest version combines all of the GLS data into one collection which has all of the combined collections. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. The datasets have been improved in order to give spatial continuity across all decadal collections. Most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This collection provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.'

'The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from five sensors: Operational Land Imager, Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. This newest version combines all of the GLS data into one collection which has all of the combined collections. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. The datasets have been improved in order to give spatial continuity across all decadal collections. Most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This collection provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.'

'The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from five sensors: Operational Land Imager, Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. This newest version combines all of the GLS data into one collection which has all of the combined collections. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. The datasets have been improved in order to give spatial continuity across all decadal collections. Most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This collection provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.'