Landsat Missions

U.S. Landsat Analysis Ready Data (ARD) Artifacts

Artifacts discovered in Landsat Collection 1 U.S. ARD are documented below. Each artifact listed below will indicate if these existing artifacts are also present in Landsat Collection 2 (C2) U.S. ARD. Artifacts discovered in C2 ARD will be added to this page when details are available. 

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Example of the Quality Assessment Band (Aerosol Bits) Discontinuity Issue in a U.S. Landsat Analysis Ready Tile (h05v02_20131203

The diagonal line visible in this image indicates a discontinuity along the north/south boundary of the Worldwide Reference System 2 (WRS-2) Landsat scenes used in h05v02, December 3, 2013. 


Quality Band Discontinuity

Discontinuities were discovered in U.S. Landsat ARD PIXELQA and SRAEROSOLQA optical thickness bands. The image to the right displays a diagonal line indicating the discontinuity. 

Cloud detection and aerosol retrieval are performed at the Landsat scene level, before ARD tile processing begins. The discontinuities in PIXELQA are likely due to temperature differences between the northern and southern Landsat scenes, resulting in the cloud confidence to be identified differently.

This discontinuity is visible in both Landsat Collection 1 and Landsat Collection 2 U.S. ARD products. 







Incorrect Scaling of Downwelled Radiance

The downwelled radiance used in the Single Channel Surface Temperature algorithm is erroneously not normalized to provide hemispherical radiance, resulting in ~0.5 Kelvin underestimation on average in the Landsat Provisional Surface Temperature product. This underestimation is almost negligible over water surfaces due to high emissivity, while it may be more pronounced over desert and arid regions where the atmospheric relative humidity is high.

Surface Reflectance Cloud Quality Assessment Anomaly (Landsat 4-5 TM, Landsat 7 ETM+)

The false-positive identification of cloudy pixels was discovered in the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm’s internal detection of clouds, which is output as the SRCLOUDQA band in Landsat 4-5 TM and Landsat 7 ETM+ Surface Reflectance data products.  An algorithmic calculation is incorrectly identifying clear irrigated field and mountain shadows as clouds in high desert regions. Analysis has determined that compared to the surrounding ambient temperature, these areas meet the difference criteria used by LEDAPS in its cloud cover assessment, and are therefore incorrectly marked as clouds.

The image below shows an example of an SRCLOUDQA band (left window) compared to a surface reflectance natural color composite image (bands 3,2,1) (right window). 

The misidentified clouds in high desert regions in LEDAPS-based U.S. Landsat ARD products does not significantly impact their scientific integrity.  

This anomaly is visible in both Landsat Collection 1 and Landsat Collection 2 U.S. ARD products. 

Comparison of ARD SRCLOUDQA Band (left) and Surface Reflectance Natural Color Composite Image (bands 3,2,1) (right)

The image is an example of an SRCLOUDQA band (left window) compared to a surface reflectance natural color composite image (bands 3,2,1) (right window). The red rectangle indicates the same region in both windows. In the SRCLOUDQA band, LEDAPS is indicating clouds (dark grey polygons), adjacent cloud (white boundary lines), and cloud shadows (light grey polygons) are present in the scene. As evident in the surface reflectance natural color composite, no clouds are visible.

​Unused Landsat Scene Listed in Collection 1 Landsat ARD Tile XML Metadata File

Unused Landsat Scene Listed in U.S. Landsat ARD Tile XML Metadata File

Outlined in red and displayed with scenes noted as input is U.S. Landsat ARD Tile h027v010. Scene 1 is beyond the boundary of the ARD tile and does not appear in the LINEAGEQA image, which correctly notes only Scenes 2 and 3 as input sources. 

The Lineage index band (LINEAGEQA) identifies which Landsat Collection 1 Level-2 Albers scene was the source for each pixel in a U.S. Landsat ARD tile by referencing scene identifiers listed in an associated XML metadata file. However, a small number of Landsat 8 and Landsat 7 ARD​​ XML metadata file​s processed prior to April 16, 2019 have been found to list the incorrect Level-2 Albers input scenes. ​ 

​This image illustrates the unused Level-2 Albers input scene listed in ARD tile (LC08_CU_027010_20170923_20181130_C01_V01; outlined in red) XML metadata file. Scene 1 and Scene 2 are incorrectly listed in the XML as contributing to ARD tile h027v010, whereas the correct input scenes listed should be Scene 2 and Scene 3.

Landsat Collection 1 U.S. ARD tiles created after April 16, 2019 are not affected by this error, and Collection 2 U.S. ARD tiles are not affected. 

















Incorrect Precision of WGS84 Axis Values

An error that defines map projection geotag values in Landsat Collection 1 Level-2 Albers scenes resulted in assigning inaccurate values to the semi-major and semi-minor axes, lengthening them by ~3-meters (m).  This error is carried through only in the Landsat Collection 1 science product Albers production stream, but subsequently does appear in all Landsat Collection 1 ARD tiles. 

​This geotagging issue does not result in any resampling or impact the validity of the ARD pixel values. The error manifests itself in a sub-2-m offset in each ARD tile.  The offset values range geographically across the United States from about 0.75-m in Chesapeake Bay to about 1.5-m in Portland, Oregon.

​​Minimal impacts on data analysis have been observed, but there is a potential for issues that involve high-resolution sensors (i.e., LIDAR) or vector-based analyses. Any attempts to reproject other data to match an ARD tile using the definitions for WGS84 CONUS Albers will not be successful due to this issue. Any tool, application or software package that utilizes projection information and conducts cross-projection operations and analyses may also be affected by this issue.

Landsat Collection 2 U.S. ARD is not affected by this issue. 


Incomplete File Names in the U.S. Landsat ARD Tile XML Metadata

All Landsat Collection 1 U.S. ARD tiles contain incomplete file names in the XML metadata file. The <file_name> parameter in the tile_metadata section of the  XML are missing the Tile ID and file format of the band file name.

Below is a correct and incorrect example of Collection 1 Landsat 4 ARD tile h019v011 for surface reflectance band 1. 

Correct:  <file_name>LT04_CU_019011_19830104_20190206_C01_V01_SRB1.tif</file_name>

Incorrect: <file_name>SRB1</file_name>

This artifact does not affect the U.S. Landsat ARD LINEAGEQA band.

This will be corrected in Landsat Collection 2 U.S. ARD. 


Cloud Shadow Placement Shift Between Projections

Cloud shadows are identified in Landsat Collection 1 and Collection 2 Level-1 and Level-2 products by the C Function of Mask (CFMask) algorithm, and then projecting cloudy pixels onto where their shadows should fall on the earth's surface. In different geometric projections (e.g., Albers vs. UTM), cloud shadows will be projected into slightly different positions on the earth's surface. This has the effect of changing the estimate of cloud height, which can then cause a large shift in the position of the final cloud shadow identification. This happens most often over high-altitude, partially transparent clouds such as cirrus. Because of this behavior, the position of cloud shadows in UTM and Albers products may differ. The identification of cloudy pixels themselves is not affected.

Landsat ARD UTM-to-Albers Cloud Shadow Placement Shift Between Projections

This image displays areas that indicate differences in cloud shadow placement shifts when processed in UTM, vs. Albers. Left side: false color image and pixel quality assessment panes for a Landsat scene (path 15/row 35) in the Albers Equal Area Conic projection. Right side: the same products for the same scene in the Universal Transverse Mercator (UTM) Zone 18N projection.

In the pixel QA images purple and brown represent clouds, beige represents cloud shadows, and cyan represents clear land. The yellow ellipsoid is an example of a region in the scene where the known cloud shadow placement shift between two projections appears.

This issue does not significantly impact the scientific integrity of Landsat products, and is visible in both Landsat Collection 1 and Landsat Collection 2 U.S. ARD products. 

Incorrect Unit of Atmospheric Transmittance in XML Metadata

The data unit of the Atmospheric Transmittance (ATRAN) band in the Landsat Collection 1 U.S. ARD XML metadata is incorrect. Atmospheric Transmittance is an intermediate band of the Provisional Landsat Surface Temperature Science Product and is unitless.

Correct: <data_units>Unitless</data_units>

Incorrect: <data_units>radiance (W m^(-2) sr^(-1) mu^(-1))</data_units>

This will be corrected in Landsat Collection 2 U.S. ARD. 


Incorrect Classification of Dark Lava

The land areas around dormant volcanoes that are covered by dark lava may be incorrectly classified as water in the U.S. Landsat ARD Pixel Quality Assessment (PIXELQA) band. This is a known issue of the C Function of Mask (CFMask) algorithm. The low reflectance at the near infrared and red wavelengths is the reason for this commission error in the CFMask water detection function.

The image below shows the PIXELQA over an area in Hawaii where the brown/dark lava pixels are incorrectly classified as water.

This classification error can be found in both Landsat Collection 1 and Landsat Collection 2 U.S. ARD products. 

Comparison of Landsat ARD-based PIXLQA Band (left) and Surface Reflectance Natural Color Composite Image (right)

This image shows a U.S. Landsat ARD-based PIXLQA Band (left) with a Surface Reflectance Natural Color Composite Image (OLI bands 4,3,2) of the same area (right). 

Underestimation of Surface Temperature Uncertainty

Due to a miscalculation in the propagation of uncertainty in the Landsat Collection 1 Provisional Surface Temperature product, the uncertainty value (STQA) is underestimated by approximately 0.5 Kelvin on average. The magnitude of the uncertainty underestimation for any given pixel is dependent on the emissivity standard deviation obtained from the ASTER GED, land cover, and distance to the cloud at that pixel. Additional information about ST uncertainty and the standard error propagation can be found at