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Section 1 – Introduction

Section 2 – Observatory Overview

Section 3 – Instrument Calibration

Section 4 – Level-1 Products

Section 5 – Conversion of DNs to Physical Units

Section 6 – Data Search and Access

Appendix A – Known Issues

Appendix B – Metadata File (MTL.txt)


Download Landsat 8 Data User Handbook .pdf (4.3 MB)

Section 4 - Level-1 Products

4.1 Level-1 Product Generation

4.1.1 Overview

The geometric algorithms used by LPGS at EROS were originally developed for the L8 IAS. The overall purpose of the IAS geometric algorithms is to use Earth ellipsoid and terrain surface information in conjunction with spacecraft ephemeris and attitude data, and knowledge of the OLI and TIRS instruments and L8 satellite geometry, to relate locations in image space (band, detector, sample) to geodetic object space (latitude, longitude, and elevation).

These algorithms are used to create accurate L1 output products, characterize the OLI and TIRS absolute and relative geometric accuracy, and derive improved estimates of geometric calibration parameters such as the sensor to spacecraft alignment.

4.1.2 Level-1 Processing System

The Level-1 processing algorithms include the following:

  • Ancillary data processing
  • L8 sensor / platform geometric model creation
  • Sensor LOS generation and projection
  • Output space / input space correction grid generation
  • Systematic, terrain-corrected image resampling
  • Geometric model precision correction using ground control
  • Precision, terrain-corrected image resampling

Figure 4-1 shows LPGS standard product data flow, which includes radiometric and geometric processing.

Figure 4-1. LPGS Standard Product  Data Flow
Figure 4-1. LPGS Standard Product Data Flow

The following paragraphs describe the purpose and function of each of the major LPGS Subsystems. Complete detailed designs for each Subsystem are presented in subsequent sections.

  • Process Control Subsystem (PCS) – The PCS controls work order scheduling and processing. The PCS manages and monitors LPGS resources and provides processing status in response to Operator requests.
  • Data Management Subsystem (DMS)  – The DMS provides data management services for the LPGS and handles the external interfaces for the System. It provides tools for formatting and packaging products. The DMS also maintains LPGS disk space and populates temporary storage with data from ingested files.
  • Radiometric Processing Subsystem (RPS) – The RPS converts the brightness of the L0R image pixels to absolute radiance in preparation for geometric correction. The RPS performs radiometric characterization of L0R images by locating radiometric artifacts in images. The RPS provides the results of characterizations performed to the IAS characterization database. The RPS corrects radiometric artifacts and converts the image to radiance.
  • Geometric Processing Subsystem (GPS) – The GPS creates L1 geometrically corrected imagery (L1G) from L1R products. The geometrically corrected products can be systematic terrain corrected (L1Gt) or precision terrain-corrected products (L1T). The GPS provides the results of characterizations performed to the IAS characterization database. The GPS generates a satellite model, prepares a resampling grid, and resamples the data to create an L1Gt or L1T product. The GPS performs sophisticated satellite geometric correction to create the image according to the map projection and orientation specified for the L1 standard product.
  • Quality Assessment Subsystem  (QAS) – The QAS performs cloud cover assessment and generates the product quality band. The QAS provides tools for visual inspection of images where a problem has been encountered creating the product.
  • User Interface (UI) – The UI provides the Graphical User Interface (GUI) for the LPGS Operator and the Anomaly Analysis Subsystem (AAS). It allows the Operator to monitor the status of work orders and track processing anomalies.

4.1.3 Ancillary Data

The L8 OLI and TIRS geometric correction algorithms are applied to the wideband (data contained in Level-0R (raw) or 1R (radiometrically corrected) products. Some of these algorithms also require additional ancillary input data sets. These include the following:

  • Ancillary data from the spacecraft and Space Inertial Reference Unit (SIRU) provides attitude information for the spacecraft.
  • Ground control / reference images for geometric test sites - used in precision correction, geodetic accuracy assessment, and geometric calibration algorithms.
  • Digital elevation data for geometric test sites - used in terrain correction and geometric calibration.
  • Prelaunch ground calibration results, including band / detector placement and timing, and attitude sensor characteristics.
  • Earth parameters, including static Earth model parameters (e.g., ellipsoid axes, gravity constants) and dynamic Earth model parameters (e.g., polar wander offsets, UT1-UTC time corrections) - used in systematic model creation and incorporated into the CPF.

4.1.4 Data Products

One of the goals of L8 is the provision of high-quality, standard data products. About 400 scenes per day are imaged globally and returned to the United States archive. All of these scenes are processed to a Level-1 standard product and made available for downloading over the Internet at no cost to users.

The L1T available to users is a radiometrically and geometrically corrected image. Inputs from both the sensors and the spacecraft are used, as well as GCPs and DEMs. The result is a geometrically rectified product free from distortions related to the sensor (e.g., view angle effects), satellite (e.g., attitude deviations from nominal), and Earth (e.g. rotation, curvature, relief). The image is also radiometrically corrected to remove relative detector differences, dark current bias, and some artifacts. The Level-1 image is presented in units of DNs, which can be easily rescaled to spectral radiance or TOA reflectance.

Figure 4-2. Level 1 Product Ground Swatch and Scene Size
Figure 4-2. Level-1 Product Ground Swath and Scene Size Product Components

A complete L1 product consists of 13 files, including the 11 band images, a product-specific metadata file, and a Quality Assessment (QA) image. The image files are all 16-bit GeoTIFF images. The OLI bands are Bands 1-9. The TIRS bands are designated as Bands 10 and 11.

The QA image is a 16-bit mask, which marks clouds, fill data, and some land cover types. Subsection 5.4 gives a full description of the L8 QA mask.

The metadata (MTL) file contains identifying parameters for the scene, along with the spatial extent of the scene and the processing parameters used to generate the Level-1 product. This file is a human-readable text file in ODL format. Product Format

The product delivered to L8 data users is packaged as Geographic tagged image file format (GeoTIFF) (a standard, public-domain image format based on Adobe's TIFF)and is a self-describing format developed to exchange raster images. The GeoTIFF format includes geographic or cartographic information embedded within the imagery that can be used to position the image in a geographic information display. Each L8 band is presented as a 16-bit grayscale image. Specifically, GeoTIFF defines a set of TIFF tags, which describes cartographic and geodetic information associated with geographic TIFF imagery. GeoTIFF is a means for tying a raster image to a known model space or map projection and for describing those projections. A metadata format provides geographic information to associate with the image data. However, the TIFF file structure allows both the metadata and the image data to be encoded into the same file.

For details on GeoTIFF format, please download the GeoTIFF Format Specification (PDF) or visit the GeoTIFF Web page. Cloud Cover Assessment (CCA)

The L8 CCA system uses multiple algorithms to detect clouds in scene data. Each CCA algorithm creates its own pixel mask that labels clouds, cirrus, and other classification types. The separate pixel masks are then merged together into the final L1 quality band.

The separate masks are merged together via a weighted voting mechanism. Each algorithm is assigned weights for every class (cloud, cirrus, water, and snow / ice), which indicates how accurate that algorithm is expected to be when classifying that type of target. These weights are defined in the CPF. Then, for each pixel, the confidence value in each mask is used to sum the algorithm weights together:

This final score is assigned to the pixel in the quality band. Note that the mid-confidence score – which is equivalent to 'ambiguous' – wins all ties among the CCA algorithms.

The L8 CCA system was designed to be modular, providing the ability to quickly add or remove CCA algorithms as desired. It is expected that more CCA algorithms will be added to the L8 CCA system in the near future. At launch, there were four CCA algorithms, including:  ACCA, See-5, Cirrus, and AT-ACCA. Automated Cloud Cover Assessment (ACCA)

ACCA is an algorithm used to generate scene-wide cloud scores for previous Landsat instruments. The L8 implementation of ACCA is similar, but it uses only Pass 1, the spectral cloud identification component of the algorithm, which generates a per-pixel cloud mask. Later ACCA passes are used to bias the pass 1 results to create a single cloud score for the entire scene, so they are not useful for creating per-pixel masks.

ACCA works Bands 3-6 of Level-1 imagery that is converted to TOA reflectance using a scene-center solar elevation angle. It also uses TIRS Band 10 imagery that have been converted to top of atmosphere brightness temperature. The formula for these conversions is in Subsection 5.3 of this document.

Once the TOA reflectance for Bands 3-6 and the TOA brightness temperature of Band 10 is available, ACCA uses eight different filters to classify the pixels in the scene:

  1. Filter 1 – Red Brightness Threshold
    1. possible cloud if  ρ4 > 0.8
    2. Each Band 4 (Red) pixel in the scene is first compared to a brightness threshold. Pixel values that exceed the Band 4 threshold, which is set at .08, are passed to filter 3. Pixels that fall below this threshold are identified and are passed to filter 2.
  2. Filter 2 – Red Non-cloud / Ambiguous Discriminator
    1. ambiguous if                ρ4 ≤ 0.7
    2. possible water if ρ4 < 0.7
    3. Comparing each pixel entering this filter to a Band 4 threshold set at .07 identifies potential low-reflectance clouds. Pixels that exceed this threshold are labeled as ambiguous. Those falling below .07 are identified as non-clouds and are flagged as such in the cloud mask. Because this filter is a weak discriminator for non-cloudy water, pixels marked as non-cloudy change to 0.7 by this filter also set the Water bit in the quality band.
  3. Filter 3 – Normalized Difference Snow Index
    1. possible cloud if  -0.25 < NDSI < 0.7
    2. where               
    3. The Normalized Difference Snow Index (NDSI) is used to detect snow (Hall et al., 1995). The reflectances of clouds and snow are similar in the green band, OLI Band 3. However, in OLI Band 6, clouds have high reflectivity, while snow reflectivity is low. Pixels that fall between an NDSI range of -.25 and .7 qualify as potential clouds and are passed to filter 5. Pixels outside this NDSI range are labeled as non-cloud and passed to Filter 4.
  4. Filter 4 – Snow / Ice Threshold
    1. possible snow / ice if NDSI > 0.8
    2. NDSI values above 0.8 are likely to be snow or ice. The snow / ice bit is set in the quality band for these pixels. All pixels that are passed to filter 4 are marked as non-cloud, whether or not they meet the threshold of possible snow / ice.
  5. Filter 5 – Temperature Threshold
    1. possible cloud if    BT > 300 K
    2. The Brightness Temperature (BT) values are used to identify potential clouds. If a pixel value exceeds 300 K – a realistic maximum for cloud temperature – it is labeled as non-cloud. Pixels with a temperature a value less than 300 K are passed to filter 6.
  6. Filter 6 – SWIR / Thermal Composite
    1. possible cloud if    (1- ρ6)BT  < 225
    2. This filter is sensitive to clouds because clouds have cold temperatures (< 300 K) and are highly reflective in the OLI SWIR Band 6 and therefore have low values of (1 – ρ6) BT. It is particularly useful for eliminating cold land surface features that have low Band 6 reflectance such as snow and tundra. Sensitivity analysis demonstrated that a threshold setting of 225 works optimally. Pixels below this threshold are passed to filter 8 as possible clouds. Pixel values above this threshold are examined using filter 7.
  7. Filter 7 – SWIR Non-cloud / Ambiguous Discriminator
    1. ambiguous if                   ρ6 ≥ 0.8
    2. non-cloud if          ρ6 < 0.8
    3. This test admits the possibility of low-reflectance clouds. Pixels below this threshold are labeled as non-cloud, while those that exceed the threshold are labeled as ambiguous.
  8. Filter 8 – NIR / Red Ratio for Growing Vegetation
    1. ambiguous if                  
    2. This filter eliminates highly reflective vegetation and is simply OLI Band 5 (NIR) reflectance divided by OLI Band 4 (Red) reflectance. In the near-infrared, reflectance for green leaves is high because very little energy is absorbed. In red wavelengths, the chlorophyll in green leaves absorbs energy so reflectance is low. This ratio results in higher values for vegetation than for other scene features, including clouds. A threshold setting of 2.35 is used. Pixels that exceed this threshold are labeled ambiguous, while pixels with ratios below this threshold are passed to filter 9.
  9. Filter 9 – NIR/Green Ratio for Senescing Vegetation
    1. ambiguous if                  
    2. In the near-infrared, senescing vegetation often is higher. The NIR / Green Ratio is sensitive to chlorophyll changes and vegetation health, and the ratio tends to be higher for vegetation than many other scene features, including clouds, and thus it can be used to discriminate vegetation from these other features. A threshold setting of 2.16248 works effectively. Pixels that exceed this number are ambiguous, while pixels with ratios below this threshold are passed to filter 10.
  10. Filter 10 – NIR / SWIR Ratio for Soil
    1. ambiguous if                  
    2. cloudy if              
    3. This filter eliminates highly reflective rocks and sands in desert landscapes and is formed by dividing the NIR (OLI Band 5) reflectance by the SWIR (OLI Band 6) reflectance. Rocks and sand tend to exhibit higher reflectance in SWIR bands than in the chlorophyll-sensing NIR band, whereas the reverse is true for clouds. A threshold setting of 1.0 works effectively. Pixels that fall below this threshold are labeled, while pixels with ratios that exceed this threshold are marked as cloudy. (No distinction is made between warm and cold clouds in the L8 implementation of ACCA.)

Figure 4-3. A Diagram of the  First Pass ACCA Algorithm
Figure 4-3. A Diagram of the First Pass ACCA Algorithm

The ACCA algorithm labels each pixel in the Level-1 scene as non-cloud, ambiguous, or cloudy. These labels are analogous to the low, medium, and high confidence cloud designations in the L1 quality band. The CCA control system takes the output from the ACCA algorithm and other CCA algorithms to generate the final L1 quality band. See-5 CCA

See-5 CCA is a cloud algorithm developed using the concept of information entropy to split training data into thresholds that provide the maximum information. Its name comes from the software used to train the algorithm, the C5.0 software package from Rulequest Research.

The See-5 algorithm is a very large decision tree of 244 threshold tests, whose input is OLI Bands 2-7. For every pixel in an L1 scene, See-5 CCA generates a confidence score that is used to label the pixel as low, medium, or high confidence cloud. Masks created by See-5 are very accurate – this algorithm outperforms ACCA in most situations – but the algorithm itself is too convoluted to present in detail. For more information about the See-5 CCA algorithm, see Scaramuzza et al., 2012. Cirrus CCA

High, thin clouds are detected using OLI Band 9, which is centered on shortwave infrared light at 1.38 µm wavelength. Light at this wavelength is strongly absorbed by water vapor in the Earth's atmosphere, so that sunlight that travels too deep into the atmosphere cannot reflect back to the satellite. Cirrus clouds are high in the atmosphere and thus above most of the water vapor, so they are strong reflectors of this 1.38 µm radiation.

The L8 Cirrus CCA algorithm is a simple threshold test for reflectance in OLI Band 9. The threshold is set in the CPF; the at-launch threshold is 2 percent reflectance. Any object with greater reflectance in Band 9 is marked as possible cirrus.

            Cirrus if:         ρ9 > Tcirrus


ρ9        =          TOA reflectance in OLI Band 9.
(Note that this is calculated from the Level-1G product using a scene-center solar elevation angle.)
Tcirrus    =          Cirrus threshold from the CPF. (0.02 at launch)

This test is very permissive, and it detects false positives over high-altitude arid regions and over the poles, where the air is very cold and dry. The cirrus test is very accurate over low to moderate elevation areas in non-polar regions.

Figure 4-4. Temperate Region Affected by Cirrus Clouds. Top image is RGB composite of OLI Bands 4,3,2; bottom image is OLI Band 9, the cirrus detection band.
Figure 4-4. Temperate Region Affected by Cirrus Clouds. Top image is RGB composite of OLI Bands 4,3,2; bottom image is OLI Band 9, the cirrus detection band

The quality band in Level-1 products contains a cirrus flag that reports high, medium, or low probability of cirrus for each pixel in the scene. This is intended as an aid to users, to indicate how each pixel may be affected by thin, high-altitude clouds that may or may not be visible in other bands of the imagery. AT-ACCA

The 'Artificial Thermal' (AT) variant of the ACCA algorithm is intended for use only when thermal data from the TIRS instrument are not available. It uses the reflectance of Bands 2-7, calculated from L1 data using a scene-center solar elevation angle, to approximate the top of atmosphere brightness temperature of the missing thermal band. This AT band is intended only for use in the AT-ACCA algorithm, and is not available to the public. Once the AT band is created, the AT-ACCA algorithm uses it as a replacement for BT in the same threshold filters as the normal ACCA algorithm. Note that ACCA and AT-ACCA are never run together. If a thermal band exists, ACCA is run; if no thermal band exists, then AT-ACCA is used.

A large number of pixels are labeled ambiguous by the filters in the AT-ACCA algorithm. To reduce the number of ambiguous pixels, a second pass is made using an additional suite of disambiguation threshold tests. These disambiguation tests are only applied to pixels labeled ambiguous by the AT-ACCA. The tests are simple thresholds that are statistically unlikely to indicate clouds among data normally marked as ambiguous by ACCA. If a pixel passes any two of these tests, it is labeled as non-cloud. Pixels that pass only one or zero tests remain ambiguous. Similar to the ACCA algorithm, the AT-ACCA's labels of non-cloud, ambiguous, and cloud pixels are analogous to the low, medium, and high confidence cloud designations in the L1 quality band.

For details on the AT band, the AT-ACCA algorithm, and the AT-ACCA disambiguation tests, see Scaramuzza et al., 2012.

4.1.5 Calculation of Scene Quality

The quality algorithm is calculated by the following two formulas:

SIQS = 9 - [ SNF/ANF * ( NDF / DFBP + NCF / CFBP)]


SIQS  =  Scene Image Quality Score
IIQS  =  Interval Image Quality Score
SNF  =  Standard number of video frames in a scene [7001 for OLI, 2621 for TIRS]
ANF  =  Actual number of frames in the scene / interval
NDF  =  Number of dropped frames in the scene / interval
DFBP = Dropped Frame Break Point:  dropped frame count at which the quality score drops by one point. [2]
NCF  =  Number of video frame CRC failures in the scene / interval
CFBP = CRC Failures Break Point:  Video frame CRC failure count at which the quality score drops by one point. [100]

Numbers in brackets are configurable. If changed, the quality algorithm version is updated and the new values documented


Values: 0–9, where
9 = Best
0 = Worst
-1 = quality not calculated or assessed


Values: 0–9, where
9 = Best
0 = Worst
-1 = quality not calculated or assessed

4.2 Level-1 Product Description

4.2.1 Science Data Content and Format

The L8 instruments, OLI and TIRS, represent an evolutionary advance in technology. OLI builds upon Landsat heritage and technologies demonstrated by the ALI. As such, OLI is a push-broom sensor with a four-mirror telescope and uses 12-bit quantization. The OLI collects 30-meter data for visible, near infrared, and short wave infrared spectral bands as well as provides for a 15-meter panchromatic band. New with OLI is the addition of a 30-meter deep blue Coastal Aerosol band (Band 1) for coastal water and aerosol studies and a 30-meter Cirrus band (Band 9) for cirrus cloud detection. Additionally, the bandwidth has been refined (narrowed) for six of the heritage bands.

The TIRS instrument collects data for two narrow spectral bands in the thermal region, formerly covered on previous Landsat instruments by one wide spectral band. Although TIRS is a separate instrument, the 100-meter TIRS data are registered to the OLI data in order to create radiometrically, geometrically, and terrain-corrected 12-bit data products.

These sensors both provide improved SNR radiometric performance quantized over a 12-bit dynamic range. This translates into 4096 potential grey levels in an image compared with only 256 grey levels in previous 8-bit instruments. Additionally, improved signal-to-noise performance enables better characterization of land cover state and condition. Science Data Content

In addition to Table 2‑1 in Section 2, Figure 4-5 compares L8 spectral bands and wavelength to that of L7 ETM+.

Figure 4-5. Landsat 8 Spectral Bands and Wavelengths compared to Landsat 7 ETM+
Figure 4-5. Landsat 8 Spectral Bands and Wavelengths compared to Landsat 7 ETM+ Science Data Format

L8 acquires high-quality, well-calibrated multispectral data over the Earth’s land surfaces. On average, over 500 unique scenes are acquired per day across the globe and sent to the USGS EROS Center for storage, archive, and processing. All of these scenes are either processed to an L1Gt product or to a standard L1T product. The highest available product derivative is made available for download over the Internet at no cost to users. A complete standard Level-1 product consists of 13 files, including  OLI Bands 1-9 (one file per band), TIR Bands 10 & 11 (one file per band), a product-specific metadata file, and a QA file.

LSDS-809 Landsat 8 (8) Level-1 (L1) Data Format Control Book (DFCB)  is an excellent reference for L8 product format and information.  The following paragraphs summarize the Level-1 data format.

For details on GeoTIFF format, please refer to the GeoTIFF Format Specification (PDF) or visit the GeoTIFF Web page.   

In addition to GeoTIFF, the data incorporate cubic convolution resampling, North Up (Map) image orientation, and Universal Transverse Mercator (UTM) map projection (Polar Stereographic projection for scenes with a center latitude greater than or equal to -63.0 degrees) using the WGS84 datum.

The format of the final output product is a tar.gz file. Specifically, the files are written to a tar file format and then compressed with the gzip application. Of note, the tar file does not contain any subdirectory information. Therefore, uncompressing the file places all of the files directly into the current directory location.

Table 4-1 and Table 4-2 address the standard and compressed file-naming conventions for L8 L1 products.

Science Data Format

4.2.2 Metadata Content and Format

The MTL file is created during product generation and contains information specific to the L1 product ordered. The MTL file contains identifying parameters for the scene, along with the spatial extent of the scene and the processing parameters used to generate the Level-1 product. This file is a human-readable text file in ODL format. LSDS-809 Landsat 8 (L8) Level-1 (L1) Data Format Control Book (DFCB) provides a complete description of the metadata file.  In general, the MTL file includes the following parameters:

  • Unique Landsat scene identifier
  • WRS path and row information
  • Scene Center Time of the date the image was acquired
  • Corner longitude and latitude in degrees and map projection values in meters
  • Reflective, thermal, and panchromatic band lines and samples
  • File names included
  • Image attributes including cloud cover, sun azimuth and elevation, and number of GCPs used
  • Band minimum and maximum reflectance and radiance rescaling

4.2.3 Quality Assessment Band

The QA Band contains quality statistics gathered from the image data and cloud mask information for the scene. The QA file is a 16-bit image with the same dimensions as the standard L1T scene. Bits are allocated for some artifacts that are distinguishable after the systematic correction (Level-1G) stage of processing. The first bit (bit 0) is the least significant. Subsection 5.4 provides a full description of the L8 QA band.

Used effectively, QA bits improve the integrity of science investigations by indicating which pixels might be affected by instrument artifacts or be subject to cloud contamination. For example, Normalized Difference Vegetation Index (NDVI) calculated over pixels containing clouds will show anomalous values. If such pixels were included in a phenology study, the results might not show the true characteristics of seasonal vegetation growth. Cloud-contaminated pixels will lower NDVI values, and measures such as the timing of ‘green up’ or peak maturity would appear later than they actually occurred. A worse consequence would be that the reported reduction of vegetation growth would be taken as an indicator of environmental change, potentially prompting unnecessary land management policies or practices.

Rigorous science applications seeking to optimize the value of pixels used in a study will find QA bits useful as a first-level indicator of certain conditions. Otherwise, users are advised that this file contains information that can be easily misinterpreted and it is not recommended for general use. Robust image processing software capable of handling 16-bit data is necessary to compute statistics of the number of pixels containing each of the designated bits.

Figure 4-6 . Quality Band (BQA.TIF)  displayed for Landsat 8 Sample Data (Path 45 Row 30) Acquired April 23, 2013
Figure 4-6 . Quality Band (BQA.TIF) displayed for Landsat 8 Sample Data (Path 45 Row 30) Acquired April 23, 2013

The QA image can be stretched to emphasize the light ("1"s) and dark ("0") pixels for a quick view of general quality conditions. In the Crater Lake, Oregon, image above, the lighter pixels are likely to be affected by snow or clouds.

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