Landsat Missions

Landsat 8 (L8) Data Users Handbook - Section 3

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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)

References

Download Landsat 8 (L8) Data User Handbook (.pdf 4.39 MB)


Section 3 - Instrument Calibration

3.1 Radiometric Characterization and Calibration Overview

The Landsat 8 calibration activities began early in the instrument development phases and continued through on-orbit initialization and verification (OIV) and on through mission operations.  This section discusses the instrument calibration activities for the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) from development and pre-flight testing, through OIV, and into nominal mission operations since this is how the verification of instrument performance requirements proceeded.  A summary of the various calibration measurements is provided in Table 3-1.

Table 3-1. Summary of Calibration Activities and Purpose, and How Measurements are Used in Building the Calibration Parameter Files
Table 3-1. Summary of Calibration Activities and Purpose, and How Measurements are Used in Building the Calibration Parameter Files

3.1.1 Instrument Characterization and Calibration

3.1.1.1 Description of Calibration Data Collections

A distinction is made between the calibration activities that measure, characterize, and evaluate instrument and system radiometric performance, and those which are used to derive improved radiometric processing parameters contained in the Landsat 8 Calibration Parameter File (CPF) for use by the Level-1 Product Generation System (LPGS). The measurement and evaluation activities are referred to as characterization operations, while the parameter estimation activities are referred to as calibration. Although both types of activities contribute to the “radiometric calibration” of the Landsat 8 OLI and TIRS instruments, the remainder of this document will use the term characterization to refer to radiometric assessment and evaluation operations and the term calibration to refer those associated with estimating radiometric processing parameters.

Shutter Collects
Shutter collects provide the individual detector dark levels or biases, which are subtracted during ground processing from each detector's response in Earth images.  This removes variations in detector dark current levels reducing striping and other detector-to-detector uniformity issues in the imagery.  These normal shutter collects are acquired before daylight imaging begins and after daylight imaging ends.  An extended shutter collect is acquired about every three months and is about 36 minutes in duration.  These longer shutter collects provide a measure of stability over typical Earth imaging intervals.

Stimulation Lamp Collects
The stimulation lamps are used to monitor the detector stability over days. While incandescent lamps tend to be poor absolute calibration sources, they excel at showing changes in detector response over relatively short periods of time.  There are three sets of stimulation lamps that get used at three different frequencies:  daily; bi-monthly; and every six months.  These different usages enable differentiation between detector changes and lamp changes.

Solar Diffuser Collects
The solar diffuser panels provide reflective references that were characterized prior to launch.  Their regular use of the primary diffuser enables detector stability to be monitored and potential changes in calibration to be fed back into the ground processing system to maintain accuracy of the Earth imagery products.  The second solar diffuser panel is used every six months as a check on stability of the primary (working) diffuser.  Longer, 60 second collects of the working solar diffuser are used to monitor the within scene detector response stability. Diffuser collects are also used to characterize the system noise and SNR performance, absolute radiometric accuracy, uniformity and relative detector gains.

In an additional solar diffuser data collect , the integration time sweep, a series of collects at different detector integration times is performed at a constant signal level.  These collects allow an on-orbit assessment of the OLI detector electronics linearity.  In an attempt to better characterize the OLI’s full detector-electronic chain linearity, two extended (60 second) diffuser collects were performed during solar eclipses (November 3, 2013 and April 29, 2014).  Here, the uniform diffuser signal was obtained at a significantly lower radiance level than normal (about 40% and 10%), to allow evaluating and updating relative non-linearities between detectors.

Lunar Collects
Imaging the moon approximately every 28 days enables an independent measure of the OLI radiometric stability as the moon is an extremely stable source \cite{ref:moon_stability}.  While the lunar surface is very stable, the viewing geometry can vary dramatically, so the lunar irradiance based model, Robotic Lunar Observatory, is used to take the viewing geometry into account.  The lunar collects are also used to evaluate stray light effects and to find any other artifacts that might be visible. The moon is a good source for these artifacts since it's bright compared to the surrounding space.

Side Slither Collects
During a side slither data collect the spacecraft is yawed 90°, so that the normally cross track direction of the focal plane is turned along track.  Here each detector in an SCA tracks over nearly identical spots on the ground.  By performing these side slither maneuvers over uniform regions of the Earth, individual detector calibration coefficients can be generated to improve the pixel-to-pixel uniformity. These maneuvers are performed over desert or snow/ice regions about every three months to monitor and potentially improve the pixel-to-pixel uniformity.

 

Examples of characterization activities include assessments of:

  • the detector response to the solar diffusers, to  radiometric accuracy and calibrating detector response;
  • the 60-second radiometric stability, to evaluate the absolute radiometric uncertainty and detect changes in detector response;
  • the detector response to stimulation lamp , to evaluate and detect changes in gain;
  • radiometric uniformity (Full field of view, banding 1, banding 2, and streaking as defined in the OLI RD) and all artifacts affecting the radiometric accuracy of the data, and SCA discontinuity differences characterized by gathering statistics information in the overlap areas between the SCAs;
  • deep space data and blackbody data to evaluate the absolute radiometric uncertainty of the TIRS instrument and detecting changes in gains and biases to determine radiometric stability;
  • dropped frames per interval, for trending the total number detected, and   excluding flagged dropped frames in all characterization algorithms.

 

Examples of calibration activities include derivation of:

  • bias model parameters for each detector. The bias model for OLI is constructed using data from associated shutter images, video reference pixels, dark (masked) detectors, and associated telemetry (e.g., temperatures). The bias model for TIRS uses data from deep space images, dark (masked) detectors, and associated telemetry (e.g., temperatures). These bias model coefficients are used to derive the bias that needs to be subtracted from detector during product generation.
  • the relative gains for all active detectors, to correct the detector responses for "striping" artifacts;
  • gain determination to enable conversion from DN to radiance and determine the accuracy of the radiometric product;
  • the TIRS background response determination.

3.1.2 Pre-Launch

Operational Land Imager
Preflight instrument performance and data characterization proceeded from subsystem level (e.g. focal plane module and electronics) to fully integrated instrument and observatory testing and analysis. Instrument testing and performance requirements verification were performed at multiple stages of development to ensure the integrity of performance at the component, subsystem, and system levels

For radiometric calibration of the OLI, an integrating sphere was used as the National Institute of Standards (NIST) traceable radiance source.  The OLI was connected to the integrating sphere in a configuration that enables the instrument to measure the output radiances from a prescribed set of illumination levels from xenon and halogen lamps.  In addition, integration time sweeps of full illumination at successively shorter detector exposure times were used to establish the linearity of the detectors and focal plane module electronics. Measurements of the integrating sphere at various radiance levels were also used to characterize the linearity.  These and the integration time sweeps are used to determine the reciprocity between the two.

Ball Aerospace and Technology Corporation (BATC), the manufacturer of the OLI, used a heliostat to facilitate the sun as a calibration source for prelaunch testing. The heliostat captured and directed sunlight from the rooftop of the BATC facility to the solar diffuser panels of the instrument placed in a thermal vacuum chamber inside the facility. 

The spectral reflectance and bidirectional reflectance distribution function (BRDF) of the panels were characterized at the University of Arizona.  A transfer spectroradiometer was used to measure the radiances from the integrating sphere and the solar diffuser panels at the same illumination levels as measured by the OLI to ensure traceability of the measurements compared to those made from the solar diffuser panels.  On orbit, any changes to the spectral response of the instrument are determined by inflight measurements of the solar diffuser panels, and coefficients used to scale the digital numbers of the instrument response to calibrated radiances can be adjusted.

The signal-to-noise ratio (SNR) for each of the OLI spectral bands is characterized at a prescribed radiance level, referred to as Ltypical.   The SNR is defined as the mean of the measured radiances divided by their standard deviation.  A curve is fit to the SNR at the measured radiance levels and is evaluated at the prescribed Ltypical.   The SNR is measured at multiple stages of the instrument build, culminating the testing of the fully integrated instrument.  The high SNR combined with the 12-bit quantization of the OLI radiometric response provides data that enhance our ability to measure and monitor subtle changes in the state and condition of the Earth’s surface.

The pre-launch verification of instrument and spacecraft radiometric performance specifications was carried out as part of the instrument and spacecraft manufacturers’ development, integration, and test programs. The radiometric characteristics of the OLI instrument were measured during instrument fabrication and testing at Ball Aerospace and Technologies Corporation (BATC) facility. Since the TIRS instrument is a NASA in-house development, pre-launch characterization and testing was carried out at the NASA Goddard Space Flight Center. Additional measurements and tests are being performed at Orbital Sciences Corporation (OSC) as the Landsat 8 spacecraft was being fabricated and integrated with the OLI and TIRS payloads.

A Horizontal Collimator Assembly (HCA) and a set of fixed geometric patterns are scanned across the focal plane to build an optical map of the detectors which enables construction of a line of sight (LOS) model from the focal plane to the Earth.  A similar process is used with a different reticle plate to derive line spread functions.  These measurements assist with characterizing the alignment of detectors within a given sensor chip assembly (SCA) as well as with aligning adjacent SCAs.  These are fundamental parameters required for constructing the geometric models to achieve many of the geometric requirements.

Thermal Infrared Sensor
A louvered plate with stringently controlled heating wss used as a flood source and placed within the TIRS field of view so that temperatures across the surface of the plate could be analyzed to characterize the uniformity of the radiometric response across the detectors within the focal plane and across the multiple SCAs.  The flood source was measured at multiple temperature levels and at multiple integration time sweeps, in order to characterize the linearity of the detector responses. 

A blackbody of known temperature was used as a calibration source to provide radiance to the detectors from which output voltages were converted to DNs.  TIRS also measured a space-view port with a cold plate set at 170 K mounted on it, and the DNs output from the instrument were converted to radiance.  The blackbody radiances were scaled by a “view factor” that was determined by viewing through the Earth-view (nadir) port.

Earth view measurements were made at several temperature settings in order to establish a relationship between the temperature, DN levels, and radiance. These measurements were then combined with the blackbody and space-view measurements to derive a final set of coefficients for scaling DNs to radiances.   Detector linearization is performed prior to the bias removal for TIRS because temperature contributions from instrument components are also being captured.

Similar to the approach taken with OLI, a wide-field collimator and a set of fixed geometric patterns are scanned across the focal plane to build an optical map of the detectors which enables construction of a line of sight (LOS) model from the focal plane to the Earth.  Instead of these targets being viewed under fixed illumination settings, these targets are presented and contrasted by controlled temperature settings. These measurements assist with characterizing the alignment of detectors within a given sensor chip assembly (SCA) as well as with aligning adjacent SCAs.  These are fundamental parameters required for constructing the geometric models to achieve many of the geometric requirements.

3.1.3 Post-Launch

Radiometric characterization and calibration will be performed over the life of the mission using the software tools developed as part of the Landsat 8 Image Assessment System (IAS) and the Calibration Validation Toolkit (CVTK). The IAS provides the capability to routinely perform radiometric characterization, to verify and monitor system radiometric performance and to estimate improved values for key radiometric calibration coefficients.  On-orbit activities include those that occur during the On-Orbit Initialization and Verification (OIV) period characterization and calibration and post-OIV nominal operations.   

Characterization activities include:

  • Noise – characterizing the OLI response to shutter, lamp, diffuser and lunar acquisitions, and the TIRS response to deep space views, On-Board Calibrator (OBC) collects and lunar acquisitions are used to assess various detector noise characteristics including coherent, impulse, signal-to-noise ratio (SNR), and noise equivalent delta radiance (NEDL), and ghosting.
  • Stability - characterizing the response of OLI to the solar diffusers, stim lamps, and lunar acquisition;, and TIRS to the (OBC) for assessing the transfer-to-orbit response, the short term (within-orbit) and long-term stability, diffuser and lunar acquisition reproducibility for OLI and post-maneuver recovery reproducibility for TIRS.

 

Calibration activities include:

  • Absolute Radiometric Response – characterizing the OLI solar diffuser, lunar irradiance, Pseudo Invariant Calibration Sites (PICS), underfly acquisitions with Landsat 7, and TIRS OBC, PICS and underfly acquisitions with Landsat 7, to assess the absolute radiometric response and deriving the initial on-orbit absolute gain CPF values.
  • Relative Radiometric Response - characterizing the OLI diffuser, yaw, and PICS;, and TIRS OBC, yaw and PICS sites for assessing the SCA to SCA  and pixel to pixel relative response/uniformity.  Special OLI diffuser and TIRS OBC integration time sweep collects are characterized to assess detector linearity, and possible updates to the CPF linearity calibration coefficients.

 

Key radiometric CPF parameters that may need updates are: absolute gains; relative gains, bias (default values), linearity lookup tables (LUTs), diffuser radiances (OLI), lamp radiances (OLI), OBC LUTs (TIRS), diffuser non-uniformity (OLI), OBC non-uniformity (TIRS), inoperable detectors, out-of-spec detectors, and detector select mask.

3.1.4 Operational Radiometric Tasks

The goals of these tasks are:

  • To demonstrate that the Landsat 8 mission meets or exceeds all radiometric requirements, particularly those that were deferred for formal verification on-orbit;
  • To perform an initial on-orbit radiometric calibration (relative and absolute) that makes it possible to achieve the previous goal, and that prepares the mission for routine operations;
  • To initialize and continue the process through nominal operations of evaluating OLI Key Performance Requirements (KPRs) by establishing an initial on-orbit performance baseline;
  • To trend radiometric characterization parameters throughout the mission.

3.1.4.1 OLI Characterization Tasks

A number of OLI characterizations and requirements verifications relate strictly to whether the instrument performance meets specifications, and therefore are not strongly tied to the coefficients stored in the CPF that require on-orbit updates.  These requirements include: stability (60-second, 16-day), noise (overall, impulse, coherent, 1/f), stray light, ghosting, bright target recovery, detector operability, and detectors out-of-specification. 

Three lunar calibration acquisitions were made during the commissioning period; each acquisition comprises 15 individual image scans, performed over two consecutive orbits. Each OLI and TIRS SCA is scanned across the Moon, with one scan repeated in both orbits to provide a check on continuity of the observations. Lunar acquisitions are performed when the Earth-Sun-Moon configuration provides lunar phase angles in the -9 to -5 degree or the +5 to +9 degree ranges. The Moon traverses each phase angle range once per month, with the positive and negative angle ranges occurring approximately one day apart. In routine operations, one phase angle range is selected for all lunar acquisitions but during commissioning, both cases were collected. The timing of the commissioning period leads to three pairs of nominal lunar acquisition opportunities:  one in late March 2013 just before the under-fly of Landsat 7, one in late April, 2013 after achieving the operational orbit, and one in late May 2013.

3.1.4.2 OLI Calibration Tasks

The OLI instrument was radiometrically calibrated prior to launch.  The OLI viewed an integrating sphere monitored by a spectrometer that had been calibrated relative to a source that is traceable to a reference in the National Institute of Standards (NIST) Facility for Spectroradiometric Calibrations (FASCAL) facility.  The gains (DN/radiance) from this calibration are stored in the CPF. The OLI’s response to the diffuser panels was measured using the sun as the source through a heliostat.  Using the OLI gains, the radiance of the diffuser was measured and was corrected for the heliostat and the atmospheric transmittance to obtain a “predicted” top-of-atmosphere (TOA) radiance for the diffuser.  Additionally, the OLI diffuser’s directional reflectance factors for the on-orbit illumination and view geometries were measured in the laboratory, prior to launch, giving the diffuser a reflectance calibration. These data were used to derive “prelaunch” TOA radiance calibration coefficients. The first on orbit measurements of the solar diffuser panel were compared to the “predicted” and “prelaunch” values to perform the transfer to orbit analysis. 

A fundamental requirement of the Landsat program is to provide a record of consistently calibrated image data.  Thus OLI data need to be consistent with data from the previous Landsat sensors. Although the instruments are calibrated consistently prior to launch and monitored prior to launch through on-orbit commissioning, there is still a need to check the calibration relative to previous Landsat instruments and update the calibration parameters as necessary.

Procedures were developed to characterize any shift in calibration that may occur through launch and into orbit. Two methods of validating the absolute radiometric calibration: cross calibration with Landsat-7 ETM+ via simultaneous observations and using the pseudo-invariant calibration sites (PICS). 

Although generating OLI images as free of striping and banding as is practicable is one of the goals of the ground system, it cannot be expected that OLI images over uniform areas will be completely free of banding or striping, especially when extreme contrast stretches are applied. There are a number of contributors to banding and striping: inadequately characterized differential non-linear responses among detectors; inadequately characterized relative gain and bias parameters; instability in gain, bias or non-linearity; and spectral differences across or between SCAs.

3.1.4.3 TIRS Characterization Tasks

A number of TIRS characterizations and requirements verifications relate strictly to whether the instrument meets performance specifications, and therefore are not strongly tied to the coefficients stored in the CPF that require on-orbit updates.  These requirements include: stability (60 second, 16-day); noise (overall, impulse, coherent, 1/f); stray light; ghosting; bright target recovery; detector operability; and detectors out-of-specification. 

Three lunar acquisitions were performed during the commissioning period. Each acquisition comprises 15 individual image scans, performed over two consecutive orbits. Each TIRS SCA is scanned across the Moon, with one scan repeated in both orbits to provide a check on continuity of the observations. As with OLI, the TIRS lunar acquisitions are performed when the Earth-Sun-Moon configuration provides lunar phase angles in the -9 to -5 degree or the +5 to +9 degree ranges.).  During the operational mission lifetime lunar acquisitions will be performed monthly as defined for the OLI.

3.1.4.4 TIRS Calibration Tasks

Thus TIRS data need to be consistent with data from the previous sensors, so by extension, Landsat 8 TIRS data needs to be cross-calibrated with Landsat-7 ETM+ data.  At least two techniques will be used for this comparison and calibration i.e. simultaneous data acquisitions collected during the under-fly of Landsat 7, and non-simultaneous observation of well-characterized targets.  TIRS was carefully characterized and calibrated prior to launch and procedures were developed to be able to characterize any shift in calibration that may occur during launch and insertion into orbit. TIRS Relative Response - Uniformity Evaluation

As with the OLI, there are a number of factors that may contribute to banding and striping in TIRS imagery: inadequately characterized differential non-linearity among detectors; inadequately characterized relative gain and bias; instability in gain, bias or non-linearity; and spectral differences across or between SCAs.

3.2 Geometric Calibration Overview

This section describes the geometric characterization and calibration activities that are performed over the life of the Landsat 8 mission using the software tools developed as part of the Landsat 8 Image Assessment System (IAS). The IAS provides the capability to routinely perform four types of geometric characterization to verify and monitor system geometric performance, and four types of geometric calibration to estimate improved values for key system geometric parameters.  These are the parameters contained in the Calibration Parameter File (CPF) for use in the Level-1 product generation. The measurement and evaluation activities are referred to as characterization operations, while the parameter estimation activities are referred to as calibration.

The geometric characterizations include:

  • Assessment of the absolute and relative geodetic accuracy of Level-1G data (Geodetic Characterization);
  • Assessment of the geometric accuracy of Level-1T products (Geometric Characterization);
  • Assessment of the accuracy of multi-temporal OLI image-to-image registration (Image-to-Image Registration Characterization);
  • Assessment the accuracy of OLI and TIRS band-to-band registration (Band-to-Band Registration Characterization).

 

A fifth characterization algorithm will be used to evaluate OLI spatial performance on-orbit. This modulation transfer function (MTF) bridge characterization algorithm uses images of selected ground targets (e.g., the Lake Pontchartrain Causeway) to estimate the OLI sensor edge response.  The characterizations performed during the commissioning period were used to establish an OLI performance baseline to serve as a basis for comparison in evaluating the OLI key performance requirements (KPRs) throughout the life of the mission. The characterizations of primary interest as KPRs are OLI band registration accuracy and spatial performance.

The geometric calibrations include:

  • Determination of the alignment between the spacecraft navigation reference frame and the OLI payload line of sight (Sensor Alignment Calibration);
  • Determination of corrections to the pre-launch OLI panchromatic band lines of sight, including relative alignment of the OLI sensor chip assemblies (OLI Focal Plane Calibration);
  • Determination of the alignment between the TIRS and OLI sensors, including the relative alignment of the TIRS sensor chip assemblies (TIRS Alignment Calibration).
  • Determination of corrections to the band location field angles for both OLI and TIRS (Band Alignment Calibration).

 

The most critical geometric calibration activities are to measure and verify the Landsat 8 spacecraft, OLI, and TIRS system performance using the geodetic, geometric, band-to-band, image-to-image, and spatial characterization capabilities; to monitor the OLI sensor to spacecraft attitude control system alignment calibration; and to monitor the TIRS-to-OLI alignment calibration. This includes verifying and, if necessary, updating the OLI and TIRS focal plane (SCA-to-SCA), and band alignment calibrations. Monitoring and refining the OLI-to-spacecraft and TIRS-to-OLI alignment knowledge is critical to ensure that the Level-1 product accuracy specifications can be met.  These calibration parameters were not expected to change greatly through launch but may require minor refinement on-orbit. The results of these calibration activities are used to verify that the system is performing within specifications and to create the Calibration Parameter Files used by the IAS and the Level-1 Product Generation System (LPGS) to create Level-1 products which meet the Landsat 8 accuracy requirements.  The calibration activities will continue throughout the life of the mission to monitor the stability of the system’s geometric and spatial performance and to identify and characterize any systematic variations in the system’s geometric parameters as a function of time, temperature, and location.  A longer sequence of calibration observations over a range of conditions will be needed to isolate, model, and characterize these higher-order behaviors.  These activities, along with the pre-launch activities, are summarized in Table 3‑2.

Table 3 2. Summary of Geometric Characterization and Calibration Activities
Table 3-2. Summary of Geometric Characterization and Calibration Activities

3.2.1 Collection Types

Lunar Collects
Due to differences in the viewing geometry between lunar collects and nominal earth collects, lunar collects are used only for measuring the alignment of the cirrus band with the other OLI bands.  

Earth Collects
Geometric characterization and calibration is performed on nominal nadir viewing earth collects.  The major difference associated with these collects and the type of characterization or calibration that is performed depends on the reference imagery for which it is characterized and in some cases eventually calibrated against.  There are three types of reference imagery used for geometric characterization and calibration; the Global Land Survey (GLS), digital orthophotoquad (DOQ) mosaics, and SPOT (Satellite Pour l’Observation de la Terre) mosaics. 

3.2.2 Pre-Launch

OLI Pre-Launch
An initial OLI geometric model which defined the line-of-sight for the detectors on the focal plane was calculated using the nominal design locations of the detectors and telescope optical system.  This initial or nominal design was then updated using edge and line targets that were projected on to the focal plane through the optical system during prelaunch thermal vacuum testing.  This model was then considered an as-built prelaunch set of line of sights for each detector for which each band for each SCA could be adjusted post launch using the IAS geometric characterization and calibration processes.

TIRS Pre-Launch
An initial TIRS geometric model, consisting of detector and sensor chip assembly within the focal plane along with the optics of the telescope, was determined based upon its’ assembly through instrument and component design and final integration to the spacecraft.  This included the focal plane and detector placement, the telescope and optical components, and the TIRS-to-spacecraft alignment measurements.  These components were then updated during prelaunch using measurements taking during thermal vacuum testing.  Targets were projected into the TIRS field of view at operator selectable locations allowing for careful identification of both the target within the focal  plane and the origination of the target itself.  This model was then considered an as-built prelaunch set of line of sights for each detector for which each band for each SCA could be adjusted post launch using the IAS geometric characterization and calibration processes.

3.2.3 OLI Geodetic Accuracy Assessment

The purpose of the geodetic accuracy assessment is to ensure that the Landsat 8 Level-0R data can be successfully processed into Level-1 systematic products that meet the system requirement of 65 meters at a circular error with 90% confidence (CE90) horizontal accuracy.

Pre-defined ground control points (consisting of image chips with known geodetic positions are automatically correlated with data from the OLI SWIR1 (for Global Land Survey 2000-based (GLS2000) control) or panchromatic bands (for digital orthophotoquad (DOQ) control) to measure the discrepancy between the known ground location and the position predicted by the OLI geometric model.

The results of the control point mensuration are used for analysis by the IAS geodetic characterization software. The precision correction software also combines the estimated attitude error from the precision solution with the current best estimate of OLI-to-spacecraft alignment from the CPF, to compute the adjusted alignment that would make the resulting attitude error zero. This apparent alignment is stored in the geometric trending database for subsequent use by the sensor alignment calibration procedures.

The geodetic accuracy characterization software processes the control point residuals (deleting those identified as outliers) to generate summary statistics and a geodetic accuracy analysis report each time the precision correction solution process is successfully completed. The geodetic accuracy results are stored in the geometric characterization trending database with a flag to indicate the control type used (GLS or DOQ).

3.2.4 Sensor Alignment Calibration

The goal of the sensor alignment calibration is to improve the in-flight knowledge of the relationship between the OLI instrument and the spacecraft attitude control system reference frame. Sensor alignment calibration uses the results of the ground control point processing conducted both as part of routine L1T product generation (using GLS2000 control) and as part of calibration/validation analysis activities over geometric calibration sites (using DOQ control). The end-to-end OLI geolocation accuracy error budget assumes that the IAS is able to estimate this alignment to an accuracy of 33 microradians (3-sigma) over periods as short as one 16-day WRS-2 cycle.

The potential need for a sensor alignment calibration updates will be identified by monitoring the geodetic accuracy characterization results. If persistent geolocation accuracy biases are observed, then that would suggest the need for generating an updated sensor alignment matrix for inclusion in the Calibration Parameter File. Automated software tools are used to detect the existence of a new alignment calibration solution and to perform automated testing of the new and old calibration solutions against a set of test scenes extracted from the list of retrieved alignment calibration scenes.  A new alignment matrix will be generated whenever a new version of the CPF is scheduled for release, nominally on a quarterly basis, therefore any slowly varying seasonal alignment variations will be accounted for.

3.2.5 Geometric Accuracy Assessment

The purpose of the geometric accuracy assessment is to evaluate the accuracy of Level-1T image products using an independent set of ground control points.  Although the geodetic accuracy characterization results report both the pre- and post-GCP correction scene accuracy statistics, the post-fit statistics are not an unbiased estimate of the actual accuracy of the corrected scene. An independent geometric accuracy assessment is performed by correlating the final L1T product with a separate set of GCPs that were withheld from the original precision correction solution. Scenes for which the number of available GCPs was too small to permit withholding some from the precision correction process do not have validation points. The geometric accuracy assessment procedure is run as a part of the standard L1T product generation flow.

3.2.6 OLI Internal Geometric Characterization and Calibration

OLI internal geometric accuracy refers to internal geometric distortions within the OLI images due to errors in the relative alignment of the 14 sensor chip assemblies (SCAs), also known as focal plane modules (FPMs), on the OLI focal plane. If the OLI line-of-sight model knowledge of the pointing for each SCA is slightly inaccurate, this will result in internal geometric distortions in the L1T products and, potentially, visible image discontinuities at SCA boundaries. Although the OLI LOS model is carefully characterized pre-launch, tools are available to detect and, if necessary, correct any SCA-to-SCA misalignment that may be observed by updating the OLI LOS model calibration. These tools are implemented in the IAS as the image-to-image registration accuracy characterization and the OLI focal plane calibration algorithms.

The OLI panchromatic band is used as the geometric reference for the entire instrument.

The image-to-image registration accuracy and the pattern of registration errors may indicate the presence of unwanted internal distortions in the OLI image that could be addressed by refining the OLI focal plane calibration.  The following subsections describe the details of the image-to-image registration assessment and OLI focal plane calibration procedures individually.

3.2.6.1 Image-to-Image Registration Assessment

The goal of the image-to-image registration assessment is to verify the Landsat 8 requirement that multi-temporal images of the same WRS scene can be successfully co-registered to an accuracy of 0.4 (LE90) multispectral pixels (i.e., 12 meters). The image-to-image assessment procedure uses ground control points that have either been extracted from a previously generated L1T image, or that match the points used to correct the pre-existing L1T product, to perform precision and terrain correction of a new acquisition to Level-1T. It then performs a point-by-point comparison of the two images using automated image correlation.

Image-to-image registration assessment using the panchromatic band demonstrates the accuracy of the overall precision correction solution as well as the internal geometric fidelity of the images.

3.2.6.2 OLI Focal Plane Calibration

The OLI focal plane calibration is intended to detect and measure systematic deviations of the OLI lines-of-sight for each SCA from the model measured during pre-launch characterization. Any significant deviations detected will be folded back into the Calibration Parameter file as updates to the LOS model Legendre polynomial coefficients.

3.2.6.3 Band-to-Band Registration Assessment

The band-to-band registration assessment measures the relative alignment of the nine OLI and two TIRS spectral bands after processing to L1T to verify that the 4.5 meter (LE90) OLI, 18 meter (LE90) TIRS, and 30 meter (LE90) OLI-to-TIRS band-to-band registration requirements are met.

3.2.6.4 Band Alignment Calibration

The purpose of band placement calibration is to estimate improved values for the locations of the spectral bands on the OLI and TIRS focal planes for inclusion in the Calibration Parameter File. The band locations are embodied in the LOS model Legendre coefficients for each OLI and TIRS band/SCA. OLI and TIRS band alignment use essentially the same algorithm but would be processed separately.

The panchromatic band is used as the reference for the OLI solution since it is the band used to perform the (sensor alignment and focal plane calibrations. TIRS band 10 is used as the reference for TIRS band alignment since it is also used in TIRS alignment calibration The OLI cirrus band is only used for lunar and high altitude terrestrial targets.

3.2.7 TIRS Internal Geometric Characterization and Calibration

The TIRS geometric alignment calibration procedure accomplishes both internal and external geometric alignment calibration for the TIRS instrument. TIRS internal geometric accuracy can be degraded by internal geometric distortions within the TIRS images due to errors in the relative alignment of the 3 sensor chip assemblies (SCAs) on the TIRS focal plane. If the TIRS line-of-sight model knowledge of the pointing for each SCA is slightly inaccurate, this will result in internal geometric distortions in the L1T product images and, potentially, visible image discontinuities at SCA boundaries.

TIRS external geometric accuracy refers to the accuracy with which TIRS data can be registered to corresponding OLI data and to an absolute ground coordinate system. This accuracy is primarily dependent on accurate knowledge of the alignment between the OLI and TIRS instruments.

The alignments of both the OLI and TIRS instruments relative to the spacecraft ACS frame were measured during observatory integration, but due to the accuracy limitations of these measurements and the likelihood of launch shift and zero-G release altering these alignments, on-orbit TIRS alignment estimation was updated to achieve the TIRS geometric accuracy requirements. Although the TIRS LOS model was also carefully characterized pre-launch, TIRS alignment calibration provides the tools needed to detect and, if necessary, correct any SCA-to-SCA misalignment that may be observed while updating the TIRS LOS model calibration.

The TIRS 10.8 micron band (band 10) is used as the geometric reference for aligning the TIRS instrument to the OLI. Band 10 is also used as the reference band in TIRS band alignment calibration, as noted above. As the TIRS geometric reference, internal SCA-to-SCA focal plane alignment is also performed using band 10.

The following subsection describes the details of the TIRS alignment calibration procedure.

3.2.7.1 TIRS Alignment Calibration

The TIRS alignment calibration measures systematic deviations of the TIRS lines-of-sight for each SCA from the model measured during pre-launch characterization while simultaneously measuring the global misalignment of all three TIRS SCAs relative to the OLI. These measurements are used to compute updates to the TIRS-to-OLI and, indirectly, TIRS-to-ACS alignment matrices as well as updates to the TIRS LOS model Legendre polynomial coefficients, with the results being folded back into the Calibration Parameter file.

A control reference image of coincident OLI SWIR bands with good emissive-to-reflective band correlation is used for calibration. The TIRS alignment calibration procedure compares a precision and terrain corrected TIRS band 10 SCA-separated image with a coincident OLI SWIR1 band SCA-combined reference image processed with the same spacecraft geometric model and scene framing parameters. This enables measurement of the overall TIRS-to-OLI alignment, as well as the relative alignment of the individual TIRS SCAs.

3.2.8 OLI Spatial Performance Characterization

OLI spatial performance, expressed as the slope and width of the instrument’s response to a unit edge/step function, was carefully characterized during pre-launch testing. The experience of the Landsat 7 ETM+, which suffered from gradually degrading spatial fidelity over the first several years of on-orbit operations, led to the development of an algorithm to measure and track on-orbit spatial performance. This was done by using long bridge targets to characterize the ETM+ modulation transfer function (MTF), which is the frequency domain representation of the instrument’s spatial response. This algorithm was subsequently adapted for use with push-broom sensors using Advanced Land Imager (ALI) data and it is a variant of this adapted algorithm that will be used for OLI spatial characterization. A method for using lunar scans to characterize ALI spatial performance was also developed but the results were not sufficiently reliable for use in operational performance characterization

There are no calibration activities associated with spatial performance, although the OLI does have ground-commandable focus mechanisms and TIRS focus can be adjusted by changing telescope temperatures. To support on-orbit focus verification, additional focus test sites have been identified to provide qualitative information about the state of OLI and TIRS focus during the commissioning period. These sites are listed in Appendix A  and were selected to contain distinct targets that could be used for visual assessment as well as additional sites for quantitative analysis using the enhanced version of the spatial performance characterization algorithm. Combined with the quantitative results of the spatial performance characterization algorithm, visual inspection of the focus sites adds confidence that the OLI and TIRS are in proper focus. The derivation of any adjustments to the focus mechanism positions or TIRS telescope temperatures that may be required to improve on-orbit spatial performance would require additional analysis that is beyond the scope of this algorithm.

Spatial edge slope performance in all OLI bands (other than the cirrus band) is a Key Performance Requirement (KPR) for OLI on-orbit performance. The derived spatial performance parameters can then be compared to the thresholds specified in the corresponding KPR to evaluate on-orbit performance. The precision of the on-orbit spatial performance estimates is such that repeated measurements are required to establish the validity that the standard performance level of any requirement is not met.

3.2.9 OLI Bridge Target MTF Estimation

The purpose of the OLI bridge target MTF estimation procedure is to use OLI acquisitions of prescribed bridge targets to derive on-orbit estimates of the OLI system transfer function for each OLI spectral band other than the cirrus band. The system transfer function (STF) estimates are then used to compute the corresponding point spread function and edge response slope performance for each spectral band.  The OLI bridge target MTF estimation procedure applies a model of the OLI spatial response (in the form of the system transfer function) to pre-defined models of two bridges, shown in Figure 3‑1, in the Lake Pontchartrain, Louisiana area, to simulate the OLI’s response to each bridge in the direction transverse to the bridge. By comparing these models to oversampled bridge profiles constructed from actual OLI image data by interleaving samples from different points along the bridge, adjusted OLI STF parameters can be estimated.

Figure 3-1. Simulated OLI Image of the Lake Pontchartrain Causeway (left) and Interstate-10 Bridge (right) Targets in WRS 022/039
Figure 3-1. Simulated OLI Image of the Lake Pontchartrain Causeway (left) and Interstate-10 Bridge (right) Targets in WRS 022/039

3.2.10 Geometric Calibration Data Requirements

The geometric characterization and calibration operations require three primary types of supporting information:  ground control points, reference images, and digital terrain data. The required characteristics, potential sources, and preprocessing requirements for each of these support data types are described in the following subsections.

3.2.10.1 Ground Control Points

The IAS uses ground control points (GCPs) for all of its geometric characterization and calibration activities. In all cases, the ground control points are used to perform a precision correction solution that will ensure accurate registration of the image data to a cartographic projection and uses digital elevation data to correct for relief displacement in the process. The IAS and LPGS both access a database of GCPs. These points were adopted from the Landsat 7 mission and include both the global GCP set extracted from the Global Land Survey (GLS) data and the higher precision control points extracted from geometric calibration reference data (e.g., digital orthophotoquad (DOQ) mosaics, SPOT data). GCPs from either or both control sets can be extracted by control type:  GLS or DOQ (DOQ is used as a generic type indicator for all GCPs extracted from geometric calibration sources).

The GLS control database covers essentially every land scene observed from the WRS-2 orbit. The control derived from the GLS is accurate to approximately 20 meters root mean square error (RMSEr) absolute, but since the goal is to make Landsat 8 products that are consistent with the other products generated from the archive of historical data. GLS GCPs include image chips extracted from the ETM+ band 5 at 30 meter GSD and have also been automatically subdivided into separate CONTROL and VALIDATION subsets in scenes where a sufficient number of points were available.

The DOQ control database only covers scenes designated as geometric calibration sites. There are two main clusters of these sites. The first set is in the United States, and was selected to provide at least one acquisition opportunity on every WRS-2 cycle day, including a group in the south-western U.S. that are at approximately the same latitude to provide consistent acquisition conditions (position in orbit, ETM+ time-on). These contain control extracted from reduced resolution DOQ data (panchromatic, 15 meter GSD). A second group of control scenes are in the eastern half of Australia. This control set is based upon mosaics of SPOT panchromatic data provided by GeoScience Australia’s National Earth Observation Group (NEOG). Being in the southern hemisphere, this set provides somewhat different orbital geometry and thermal conditions than the U.S. set. One of the primary purposes of the DOQ control set is to ensure accurate registration between Landsat 8 L1T products and the DOQ- and SPOT-derived reference imagery (discussed below) used for focal plane calibration.

The GLS control points were extracted from the GLS 2000 ETM+ images using an automated interest operator technique that selected points based on a local spatial operator that identified “interesting” points, and a spatial distribution test that decided which points provided the best distribution of control across the scene. A subsequent test was added to weed out points that contained only water. The GLS control has been in routine operational use for generating Landsat 5 and Landsat 7 standard L1T data products. An important note is that the GLS images themselves were generated based upon a global block triangulation of Landsat 7 scenes with a sparse set of ground control provide by the National Geospatial-Intelligence Agency (NGA). The scenes that contained NGA control are more accurate than those that were positioned solely through triangulation. The NGA-controlled scene subset will therefore be given special attention when mining the geodetic accuracy and sensor alignment data for systematic within-orbit effects.

The DOQ control points were extracted from the DOQ mosaic reference images (U.S. set) and the SPOT mosaic reference image (Australia) and therefore inherit the accuracy of those products. Since the primary purpose of these points is to ensure good OLI to reference image registration, the absolute accuracy of these points, though believed to be better than the GLS control, is of less interest. The list of geodetic characterization sites where DOQ control points are available is included in Appendix A.

3.2.10.2 Reference Images

Two types of reference images are used by the geometric super-site calibration operations described above. The first type are previously generated Landsat 8 L1T products used in the image-to-image registration assessment process. These reference images were generated by processing L0R data through the IAS Level-1 processing software after launch, and are not discussed further here. The second type of reference images were constructed pre-launch using a high resolution image source. These images are used to provide the reference for OLI focal plane calibration as described above. These reference images are the subject of the remainder of this section.

The key characteristics of the focal plane calibration reference images are:  1) high absolute geodetic accuracy (including removal of any terrain displacement effects);  2) internal geometric integrity (no systematic internal distortions which could be confounded with OLI focal plane alignment effects);  3) spectral similarity to the OLI panchromatic band;  4) resolution as good or better than the OLI panchromatic band; and  5) availability in areas of minimal seasonal change and low average cloud cover. Geodetic accuracy of one-half of a panchromatic pixel (7.5 meters) should be sufficient although higher accuracy is desirable. The internal geometric accuracy requirement disqualifies ETM+ data as a source of reference imagery, though in an emergency ETM+ reference data (e.g., GLS) would be better than nothing.

High resolution panchromatic imagery from aerial photographs which meet the geodetic accuracy requirement have been available for years. Panchromatic satellite imagery is available from SPOT and a number of high resolution commercial missions, but the cost associated with acquiring the volume of data required to cover a Landsat scene has limited the application of these sources to a set of sites in Australia where GeoScience Australia’s National Earth Observation Group (NEOG) have provided full WRS-2 scene SPOT data coverage.

The preferred source of high resolution reference imagery based upon availability and cost are the Digital Orthophoto Quadrangles (DOQ) produced under contract to the USGS. The DOQs are created by digitizing and orthorectifying panchromatic aerial photography. The DOQ products are distributed as 3.75 arc-minute quarter quads at 1-meter resolution. The DOQ geodetic accuracy is specified to meet National Map Accuracy Standards (NMAS) for 1:24,000-scale maps. This standard calls for a circular error (CE) of 40 feet at the 90% confidence level, which converts to approximately 6 meters CE one sigma, and meets the one-half OLI pixel requirement. DOQ data coverage has improved since the launch of Landsat 7, to the point where it is now possible to generate a DOQ reference image nearly everywhere in the conterminous U.S. Though still time consuming to construct, this has made it possible to assemble sufficient DOQ reference sites to provide at least one acquisition opportunity on every WRS-2 cycle day.

SPOT data, though not quite as accurate as the DOQ data, are available globally. The primary drawback of using SPOT data is the cost. Fortunately, our colleagues at GeoScience Australia were good enough to provide several Landsat scene-sized mosaics of SPOT data in Australia for use in Landsat 5 and 7 bumper-mode calibration. These reference images will continue to be used for OLI focal plane calibration, though they can be expected to become less useful over time as the imagery become outdated. 

3.2.10.3 Terrain Data

Digital terrain data are needed to provide the elevation information used by the IAS (and LPGS) Level-1 terrain correction process. Terrain corrected images are used in all of the geometric calibration operations described above.  The elevation information must completely cover the geometric calibration sites to support the terrain correction process. The height values must be referenced to the WGS84 ellipsoid rather than mean sea level to be consistent with the ground control height values. Vertical accuracy better than 15 meters (one sigma) is desirable. This keeps terrain-induced errors below 0.1 panchromatic pixels at the edges of the OLI field of view. An accuracy of 30 meters (one sigma) is acceptable. For product generation purposes it is also desirable that the elevation data used be consistent with the GLS 2000 reference data set. The digital elevation model (DEM) data used to generate the GLS datasets meets these requirements.

Assembling a global elevation data set suitable for generating a global Landsat image base was a primary objective of the GeoCover (which evolved into the Global Land Survey) project . This resulted in a global DEM constructed from the best available source data, including the USGS National Elevation Dataset (NED) DEM data, the Canadian Digital Elevation Dataset (CDED), the NASA/NGA Shuttle Radar Topography Mission (SRTM) data, and NGA Digital Terrain Elevation Data (DTED) products. The GLS DEM provides a globally (mostly) consistent elevation dataset that corresponds to the GLS imagery that defines the Landsat 8 geometric reference.  Tools were developed to retrieve any specified land area from the global DEM, so the elevation data sets required to process any given scene (nadir-viewing or off-nadir) are extracted and assembled on demand from the GLS archive.  The digital terrain data for the desired output product area are extracted from the GLS DEM archive, as noted above, and then be preprocessed into the output space used for the calibration test scene. This DEM resampling step is part of the normal Level-1 processing sequence.

3.3 Calibration Parameters

The Calibration and Validation Team (CVT) is responsible for the sustained radiometric and geometric calibration of the Landsat 8 satellite and the TIRS and OLI sensors. This is achieved by assessing new imagery on a daily basis, performing both radiometric and geometric calibration when needed, and developing new processing parameters for creating Level-1 (L1) products. Processing parameters are stored in the Calibration Parameter File (CPF), the Response Linearity Look-Up Table (RLUT) and the Bias Parameter File (BPF), which are stamped with effectivity dates and bundled with Level-0R (L0R) products.

3.3.1 Calibration Parameter File

The CVT updates the CPF at least every three months. Updates will likely be more frequent during early orbit checkout and will also occur between the regular three month cycles whenever necessary. Irregular updates will not affect the regular schedule. The timed release of a new CPF must be maintained because of the UT1 time corrections and pole wander predictions included in the file. These parameters span a 180 days  and includes approximately 45 days before and 45 days after the effective start date of each CPF. The IAS maintains an archive of CPFs.

The CPF is time-stamped with an effective date range.  The parameters in the file—Effective_Date_Begin and Effective_Date_End—designate the range of valid acquisition dates and are in YYYY-MM-DDThh:mm:ss format (ISO 8601).  The parameter file used in processing an image requires an effective date range that includes the acquisition date of the ordered image.

Through the course of the mission, a serial collection of CPFs is generated and made available for download. Calibration Parameter Files are replaced when improved calibration parameters for a given period are developed. The need for unique file sequence numbers becomes necessary as file contents change.  Version numbers for all effective date ranges after the launch begin with 01.

 The following is an example of the file naming procedure:

naming procedure

As an example, suppose four calibration files were created on 90-day intervals, the first file updated twice, and the second and third files updated once since 2012; the assigned file names would be as follows: 

four calibration files

The reserved sequence number 00 uniquely identifies the pre-launch CPF. Sequence numbers for subsequent time periods all begin with 01. New versions or updates are incremented by one.  This example assumes the effectivity dates do not change. The effectivity date range for a file can change if an instrument change event is discovered within the nominal three month effectivity range. Assuming this scenario, two CPFs with new names and effective dates are spawned for the time period under consideration. The effective_date_end for a new pre-event CPF would change to the day before the problem occurred. The effective_date_begin remains unchanged. A post-event CPF with a new file name would be created with an effective_date_begin corresponding to the imaging date the problem occurred. The effective_date_end would be set to the original effective_date_end for the time period under consideration. New versions of all other CPFs affected by the parameter change also would be created.  The version numbers continue to increment from the original unsplit CPF.

Using this example, suppose a dead detector is discovered to have occurred on July 25, 2012. Two new CPFs are created that supersede the time period represented by file number three, version 2, and a new version of file number four is created. The new file names and sequence numbers become:

Handbook

3.3.1.1 File Structure

All calibration parameters are stored as American Standard Code for Information
Interchange (ASCII) text using the Object Definition Language (ODL) syntax developed by the NASA JPL. ODL is a tagged keyword language developed to provide a human-readable data structure to encode data for simplified interchange. The body of the file is composed of two statement types:

  • Attribute assignment statement used to assign values to parameters.
  • Group statements used to aid in file organization and enhance parsing granularity of parameter sets.

 

To illustrate consider the first nine parameters in the file, these nine parameters form their own group, which is called FILE_ATTRIBUTES. The syntax employed for this collection of parameters in the CPF appears as:

Handbook

3.3.2 Bias Parameter Files

The bias model calibration algorithms operate on each OLI shutter collect and each TIRS deep space collect to generate bias model parameters for each imaging band, sensor chip assembly (SCA), and detector for a given time interval.  The BPFs are time-stamped the same way the CPFs are time-stamped.  The BPFs are generated automatically about every half orbit, or 50 minutes.  Level-1 processing must wait for these BPFs to be generated prior to processing to ensure radiometric quality of the products.

The file name contains the file identifier, sensor, effective date range, and version number.

Handbook

3.3.3 Response Linearization Lookup Table File

The RLUT file contains parameters used to linearize the response of each detector to ensure the radiometric uniformity of L1 products at all brightness levels.is a listing of floating-point numbers derived from bias-corrected DN values for every active detector.  The file is defined down to the SCA/Band/Detector level with an array of up to 4096 floating point values for each detector.  The file is large and is stored in Hierarchical Data Format (HDF).

Throughout the mission, the file change history is maintained by means of effective begin and end dates plus the assignment of a version number to deal with changes that occur during the effective date period

The file name contains the file identifier, effective date range, and version number.

Handbook

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