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Landsat-8 Sensor Characterization and Calibration
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Landsat-8 Sensor Characterization and Calibration

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2014) | Viewed by 254274

Special Issue Editors

Mr. Brian Markham

E-Mail Website
Guest Editor
NASA/GSFC, NASA/GSFC Code 618, Greenbelt, MD 20771, USA
Interests: radiometry performance and calibration
Mr. James C. Storey

E-Mail Website
Guest Editor
Stinger Ghaffarian Technologies (SGT), Technical Support Services Contractor to USGS EROS, NASA/GSFC Mail Code 618, Greenbelt , MD 20771, USA
Interests: geolocation; geometric calibration satellite photogrammetry
Mr. Ron Morfitt

E-Mail
Guest Editor
Stinger Ghaffarian Technologies (SGT), Technical Support Services Contractor to USGS EROS, Earth Resources Observation and Science (EROS) Center, U.S. Geological Survey, 47914 252nd Street, Sioux Falls, SD 57198, USA
Interests: radiometry performance; calibration and data processing

Special Issue Information

Dear Colleagues,

Landsat-8 (formerly LDCM) was launched on February 11, 2013. There are two new sensors on Landsat-8: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Unlike historically used whisk broom scanners (as on the ETM+ sensor on Landsat-7), these new sensors are examples of push broom technology. In addition to having new instruments that have their individual characteristics and calibrations, the change in sensor technology produces significant differences in data characteristics and quality. This Special Issue aims to provide the user community with a good understanding of the radiometric and geometric properties of the Landsat-8 instruments and their data. This understanding will enable the community to effectively use the data in conjunction with data from other earlier Landsat sensors.

For the Special Issue, we would like to encourage papers on the following topics:

  • OLI Design
  • OLI Spectral Characterization
  • OLI Absolute Radiometric Calibration and Traceability
  • OLI Radiometric Characterization
  • OLI Radiometric Cross calibration
  • OLI Spatial Performance Characterization
  • OLI Geometric Characterization and Calibration
  • TIRS Design
  • TIRS Spectral Characterization
  • TIRS Absolute Radiometric Calibration and Traceability
  • TIRS Radiometric Characterization
  • TIRS Radiometric Cross calibration
  • TIRS Spatial Performance Characterization
  • TIRS Geometric Characterization and Calibration
  • Landsat-8 integrated data product geometric performance
  • Landsat-8 Image Assessment System

Mr. Brian Markham
Mr. James C. Storey
Mr. Ron Morfitt
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Editorial

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602 KiB  
Editorial
Landsat-8 Sensor Characterization and Calibration
by Brian Markham, James Storey and Ron Morfitt
Remote Sens. 2015, 7(3), 2279-2282; https://doi.org/10.3390/rs70302279 - 25 Feb 2015
Cited by 21 | Viewed by 14112
Abstract
Landsat-8 was launched on 11 February 2013 with two new Earth Imaging sensors to provide a continued data record with the previous Landsats. For Landsat-8, pushbroom technology was adopted, and the reflective bands and thermal bands were split into two instruments. The Operational [...] Read more.
Landsat-8 was launched on 11 February 2013 with two new Earth Imaging sensors to provide a continued data record with the previous Landsats. For Landsat-8, pushbroom technology was adopted, and the reflective bands and thermal bands were split into two instruments. The Operational Land Imager (OLI) is the reflective band sensor and the Thermal Infrared Sensor (TIRS), the thermal. In addition to these fundamental changes, bands were added, spectral bandpasses were refined, dynamic range and data quantization were improved, and numerous other enhancements were implemented. As in previous Landsat missions, the National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) cooperated in the development, launch and operation of the Landsat-8 mission. One key aspect of this cooperation was in the characterization and calibration of the instruments and their data. This Special Issue documents the efforts of the joint USGS and NASA calibration team and affiliates to characterize the new sensors and their data for the benefit of the scientific and application users of the Landsat archive. A key scientific use of Landsat data is to assess changes in the land-use and land cover of the Earth’s surface over the now 43-year record. [...] Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Research

Jump to: Editorial

6991 KiB  
Article
Landsat-8 Operational Land Imager (OLI) Radiometric Performance On-Orbit
by Ron Morfitt, Julia Barsi, Raviv Levy, Brian Markham, Esad Micijevic, Lawrence Ong, Pat Scaramuzza and Kelly Vanderwerff
Remote Sens. 2015, 7(2), 2208-2237; https://doi.org/10.3390/rs70202208 - 17 Feb 2015
Cited by 140 | Viewed by 12453
Abstract
Expectations of the Operational Land Imager (OLI) radiometric performance onboard Landsat-8 have been met or exceeded. The calibration activities that occurred prior to launch provided calibration parameters that enabled ground processing to produce imagery that met most requirements when data were transmitted to [...] Read more.
Expectations of the Operational Land Imager (OLI) radiometric performance onboard Landsat-8 have been met or exceeded. The calibration activities that occurred prior to launch provided calibration parameters that enabled ground processing to produce imagery that met most requirements when data were transmitted to the ground. Since launch, calibration updates have improved the image quality even more, so that all requirements are met. These updates range from detector gain coefficients to reduce striping and banding to alignment parameters to improve the geometric accuracy. This paper concentrates on the on-orbit radiometric performance of the OLI, excepting the radiometric calibration performance. Topics discussed in this paper include: signal-to-noise ratios that are an order of magnitude higher than previous Landsat missions; radiometric uniformity that shows little residual banding and striping, and continues to improve; a dynamic range that limits saturation to extremely high radiance levels; extremely stable detectors; slight nonlinearity that is corrected in ground processing; detectors that are stable and 100% operable; and few image artifacts. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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35913 KiB  
Article
Pre- and Post-Launch Spatial Quality of the Landsat 8 Thermal Infrared Sensor
by Brian N. Wenny, Dennis Helder, Jungseok Hong, Larry Leigh, Kurtis J. Thome and Dennis Reuter
Remote Sens. 2015, 7(2), 1962-1980; https://doi.org/10.3390/rs70201962 - 11 Feb 2015
Cited by 30 | Viewed by 9100
Abstract
The Thermal Infrared Sensor (TIRS) for the Landsat 8 platform was designed and built at NASA Goddard Space Flight Center (GSFC). TIRS data will extend the data record for thermal observations from the heritage Landsat sensors, dating back to the launch of Landsat [...] Read more.
The Thermal Infrared Sensor (TIRS) for the Landsat 8 platform was designed and built at NASA Goddard Space Flight Center (GSFC). TIRS data will extend the data record for thermal observations from the heritage Landsat sensors, dating back to the launch of Landsat 4 in 1982. The two-band (10.9 and 12.0 μm) pushbroom sensor with a 185 km-wide swath uses a staggered arrangement of quantum well infrared photodetector (QWIPs) arrays. The required spatial resolution is 100 m for TIRS, with the assessment of crop moisture and water resources being science drivers for that resolution. The evaluation of spatial resolution typically relies on a straight knife-edge technique to determine the spatial edge response of a detector system, and such an approach was implemented for TIRS. Flexibility in the ground calibration equipment used for TIRS thermal-vacuum chamber testing also made possible an alternate strategy that implemented a circular target moved in precise sub-pixel increments across the detectors to derive the edge response. On-orbit, coastline targets were developed to evaluate the spatial response performance. Multiple targets were identified that produced similar results to one another. Even though there may be a slight bias in the point spread function (PSF)/modulation transfer function (MTF) estimates towards poorer performance using this approach, it does have the ability to track relative changes for monitoring long-term instrument status. The results for both pre- and post-launch response analysis show general good agreement and consistency with edge slope along-track values of 0.53 and 0.58 pre- and post-launch and across-track values 0f 0.59 and 0.55 pre- and post-launch. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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49936 KiB  
Article
The Thermal Infrared Sensor (TIRS) on Landsat 8: Design Overview and Pre-Launch Characterization
by Dennis C. Reuter, Cathleen M. Richardson, Fernando A. Pellerano, James R. Irons, Richard G. Allen, Martha Anderson, Murzy D. Jhabvala, Allen W. Lunsford, Matthew Montanaro, Ramsey L. Smith, Zelalem Tesfaye and Kurtis J. Thome
Remote Sens. 2015, 7(1), 1135-1153; https://doi.org/10.3390/rs70101135 - 19 Jan 2015
Cited by 90 | Viewed by 15420
Abstract
The Thermal Infrared Sensor (TIRS) on Landsat 8 is the latest thermal sensor in that series of missions. Unlike the previous single-channel sensors, TIRS uses two channels to cover the 10–12.5 micron band. It is also a pushbroom imager; a departure from the [...] Read more.
The Thermal Infrared Sensor (TIRS) on Landsat 8 is the latest thermal sensor in that series of missions. Unlike the previous single-channel sensors, TIRS uses two channels to cover the 10–12.5 micron band. It is also a pushbroom imager; a departure from the previous whiskbroom approach. Nevertheless, the instrument requirements are defined such that data continuity is maintained. This paper describes the design of the TIRS instrument, the results of pre-launch calibration measurements and shows an example of initial on-orbit science performance compared to Landsat 7. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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3157 KiB  
Article
The Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI
by Jeffrey Czapla-Myers, Joel McCorkel, Nikolaus Anderson, Kurtis Thome, Stuart Biggar, Dennis Helder, David Aaron, Larry Leigh and Nischal Mishra
Remote Sens. 2015, 7(1), 600-626; https://doi.org/10.3390/rs70100600 - 07 Jan 2015
Cited by 151 | Viewed by 11181
Abstract
This paper presents the vicarious calibration results of Landsat 8 OLI that were obtained using the reflectance-based approach at test sites in Nevada, California, Arizona, and South Dakota, USA. Additional data were obtained using the Radiometric Calibration Test Site, which is a suite [...] Read more.
This paper presents the vicarious calibration results of Landsat 8 OLI that were obtained using the reflectance-based approach at test sites in Nevada, California, Arizona, and South Dakota, USA. Additional data were obtained using the Radiometric Calibration Test Site, which is a suite of instruments located at Railroad Valley, Nevada, USA. The results for the top-of-atmosphere spectral radiance show an average difference of −2.7, −0.8, 1.5, 2.0, 0.0, 3.6, 5.8, and 0.7% in OLI bands 1–8 as compared to an average of all of the ground-based measurements. The top-of-atmosphere spectral reflectance shows an average difference of 1.6, 1.3, 2.0, 1.9, 0.9, 2.1, 3.1, and 2.1% from the ground-based measurements. Except for OLI band 7, the spectral radiance results are generally within ±5% of the design specifications, and the reflectance results are generally within ±3% of the design specifications. The results from the data collected during the tandem Landsat 7 and 8 flight in March 2013 indicate that ETM+ and OLI agree to each other to within ±2% in similar bands in top-of-atmosphere spectral radiance, and to within ±4% in top-of-atmosphere spectral reflectance. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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2075 KiB  
Article
Radiometric Non-Uniformity Characterization and Correction of Landsat 8 OLI Using Earth Imagery-Based Techniques
by Frank Pesta, Suman Bhatta, Dennis Helder and Nischal Mishra
Remote Sens. 2015, 7(1), 430-446; https://doi.org/10.3390/rs70100430 - 31 Dec 2014
Cited by 30 | Viewed by 7718
Abstract
Landsat 8 is the first satellite in the Landsat mission to acquire spectral imagery of the Earth using pushbroom sensor instruments. As a result, there are almost 70,000 unique detectors on the Operational Land Imager (OLI) alone to monitor. Due to minute variations [...] Read more.
Landsat 8 is the first satellite in the Landsat mission to acquire spectral imagery of the Earth using pushbroom sensor instruments. As a result, there are almost 70,000 unique detectors on the Operational Land Imager (OLI) alone to monitor. Due to minute variations in manufacturing and temporal degradation, every detector will exhibit a different behavior when exposed to uniform radiance, causing a noticeable striping artifact in collected imagery. Solar collects using the OLI’s on-board solar diffuser panels are the primary method of characterizing detector level non-uniformity. This paper reports on an approach for using a side-slither maneuver to estimate relative detector gains within each individual focal plane module (FPM) in the OLI. A method to characterize cirrus band detector-level non-uniformity using deep convective clouds (DCCs) is also presented. These approaches are discussed, and then, correction results are compared with the diffuser-based method. Detector relative gain stability is assessed using the side-slither technique. Side-slither relative gains were found to correct streaking in test imagery with quality comparable to diffuser-based gains (within 0.005% for VNIR/PAN; 0.01% for SWIR) and identified a 0.5% temporal drift over a year. The DCC technique provided relative gains that visually decreased striping over the operational calibration in many images. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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4546 KiB  
Article
Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)
by Nischal Mishra, Md Obaidul Haque, Larry Leigh, David Aaron, Dennis Helder and Brian Markham
Remote Sens. 2014, 6(12), 12619-12638; https://doi.org/10.3390/rs61212619 - 16 Dec 2014
Cited by 150 | Viewed by 12715
Abstract
This study evaluates the radiometric consistency between Landsat-8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using cross calibration techniques. Two approaches are used, one based on cross calibration between the two sensors using simultaneous image pairs, acquired during [...] Read more.
This study evaluates the radiometric consistency between Landsat-8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using cross calibration techniques. Two approaches are used, one based on cross calibration between the two sensors using simultaneous image pairs, acquired during an underfly event on 29–30 March 2013. The other approach is based on using time series of image statistics acquired by these two sensors over the Libya 4 pseudo invariant calibration site (PICS) (+28.55°N, +23.39°E). Analyses from these approaches show that the reflectance calibration of OLI is generally within ±3% of the ETM+ radiance calibration for all the reflective bands from visible to short wave infrared regions when the ChKur solar spectrum is used to convert the ETM+ radiance to reflectance. Similar results are obtained comparing the OLI radiance calibration directly with the ETM+ radiance calibration and the results in these two different physical units (radiance and reflectance) agree to within ±2% for all the analogous bands. These results will also be useful to tie all the Landsat heritage sensors from Landsat 1 MultiSpectral Scanner (MSS) through Landsat-8 OLI to a consistent radiometric scale. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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6324 KiB  
Article
Landsat-8 Operational Land Imager Radiometric Calibration and Stability
by Brian Markham, Julia Barsi, Geir Kvaran, Lawrence Ong, Edward Kaita, Stuart Biggar, Jeffrey Czapla-Myers, Nischal Mishra and Dennis Helder
Remote Sens. 2014, 6(12), 12275-12308; https://doi.org/10.3390/rs61212275 - 09 Dec 2014
Cited by 198 | Viewed by 14592
Abstract
The Landsat-8 Operational Land Imager (OLI) was radiometrically calibrated prior to launch in terms of spectral radiance, using an integrating sphere source traceable to National Institute of Standards and Technology (NIST) standards of spectral irradiance. It was calibrated on-orbit in terms of reflectance [...] Read more.
The Landsat-8 Operational Land Imager (OLI) was radiometrically calibrated prior to launch in terms of spectral radiance, using an integrating sphere source traceable to National Institute of Standards and Technology (NIST) standards of spectral irradiance. It was calibrated on-orbit in terms of reflectance using diffusers characterized prior to launch using NIST traceable standards. The radiance calibration was performed with an uncertainty of ~3%; the reflectance calibration to an uncertainty of ~2%. On-orbit, multiple calibration techniques indicate that the sensor has been stable to better than 0.3% to date, with the exception of the shortest wavelength band, which has degraded about 1.0%. A transfer to orbit experiment conducted using the OLI’s heliostat-illuminated diffuser suggests that some bands increased in sensitivity on transition to orbit by as much as 5%, with an uncertainty of ~2.5%. On-orbit comparisons to other instruments and vicarious calibration techniques show the radiance (without a transfer to orbit adjustment), and reflectance calibrations generally agree with other instruments and ground measurements to within the uncertainties. Calibration coefficients are provided with the data products to convert to either radiance or reflectance units. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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474 KiB  
Article
On-Orbit Radiometric Performance of the Landsat 8 Thermal Infrared Sensor
by Matthew Montanaro, Raviv Levy and Brian Markham
Remote Sens. 2014, 6(12), 11753-11769; https://doi.org/10.3390/rs61211753 - 27 Nov 2014
Cited by 49 | Viewed by 6528
Abstract
The Thermal Infrared Sensor (TIRS) requirements for noise, stability, and uniformity were designed to ensure the radiometric integrity of the data products. Since the launch of Landsat 8 in February 2013, many of these evaluations have been based on routine measurements of the [...] Read more.
The Thermal Infrared Sensor (TIRS) requirements for noise, stability, and uniformity were designed to ensure the radiometric integrity of the data products. Since the launch of Landsat 8 in February 2013, many of these evaluations have been based on routine measurements of the onboard calibration sources, which include a variable-temperature blackbody and a deep space view port. The noise equivalent change in temperature (NEdT) of TIRS data is approximately 0.05 K @ 300 K in both bands, exceeding requirements by about a factor of 8 and Landsat 7 ETM+ performance by a factor of 3. Coherent noise is not readily apparent in TIRS data. No apparent change in the detector linearization has been observed. The radiometric stability of the TIRS instrument over the period between radiometric calibrations (about 40 min) is less than one count of dark current and the variation in terms of radiance is less than 0.015 \(W/m^2/sr/\mu m\) (or 0.13 K) at 300 K, easily meeting the short term stability requirements. Long term stability analysis has indicated a degradation of about 0.2% or less per year. The operational calibration is only updated using the biases taken every orbit, due to the fundamental stability of the instrument. By combining the data from two active detector rows per band, 100% detector operability is maintained for the instrument. No trends in the noise, operability, or short term radiometric stability are apparent over the mission life. The uniformity performance is more difficult to evaluate as scene-varying banding artifacts have been observed in Earth imagery. Analyses have shown that stray light is affecting the recorded signal from the Earth and inducing the banding depending on the content of the surrounding Earth surface. As the stray light effects are stronger in the longer wavelength TIRS band11 (12.0 \(\mu m\)), the uniformity is better in the shorter wavelength band10 (10.9 \(\mu m\)). Both bands have exceptional noise and stability performance and band10 has generally adequate uniformity performance and should currently be used in preference to band11. The product uniformity will improve with the stray light corrections being developed. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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3255 KiB  
Article
Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration
by Julia A. Barsi, John R. Schott, Simon J. Hook, Nina G. Raqueno, Brian L. Markham and Robert G. Radocinski
Remote Sens. 2014, 6(11), 11607-11626; https://doi.org/10.3390/rs61111607 - 21 Nov 2014
Cited by 308 | Viewed by 22023
Abstract
Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 [...] Read more.
Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 respectively). They have 100 m spatial resolution and image coincidently with the Operational Land Imager (OLI), also on-board Landsat-8. The TIRS instrument has an internal calibration system consisting of a variable temperature blackbody and a special viewport with which it can see deep space; a two point calibration can be performed twice an orbit. Immediately after launch, a rigorous vicarious calibration program was started to validate the absolute calibration of the system. The two vicarious calibration teams, NASA/Jet Propulsion Laboratory (JPL) and the Rochester Institute of Technology (RIT), both make use of buoys deployed on large water bodies as the primary monitoring technique. RIT took advantage of cross-calibration opportunity soon after launch when Landsat-8 and Landsat-7 were imaging the same targets within a few minutes of each other to perform a validation of the absolute calibration. Terra MODIS is also being used for regular monitoring of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/m2·sr·μm or −2.1 K and −4.4 K at 300 K in Band 10 and 11 respectively, where TIRS data was too hot. A calibration update was recommended for both bands to correct for a bias error and was implemented on 3 February 2014 in the USGS/EROS processing system, but the residual variability is still larger than desired for both bands (0.12 and 0.2 W/m2·sr·μm or 0.87 and 1.67 K at 300 K). Additional work has uncovered the source of the calibration error: out-of-field stray light. While analysis continues to characterize the stray light contribution, the vicarious calibration work proceeds. The additional data have not changed the statistical assessment but indicate that the correction (particularly in band 11) is probably only valid for a subset of data. While the stray light effect is small enough in Band 10 to make the data useful across a wide array of applications, the effect in Band 11 is larger and the vicarious results suggest that Band 11 data should not be used where absolute calibration is required. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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2362 KiB  
Article
Development of an Operational Calibration Methodology for the Landsat Thermal Data Archive and Initial Testing of the Atmospheric Compensation Component of a Land Surface Temperature (LST) Product from the Archive
by Monica Cook, John R. Schott, John Mandel and Nina Raqueno
Remote Sens. 2014, 6(11), 11244-11266; https://doi.org/10.3390/rs61111244 - 13 Nov 2014
Cited by 137 | Viewed by 14939
Abstract
The Landsat program has been producing an archive of thermal imagery that spans the globe and covers 30 years of the thermal history of the planet at human scales (60–120 m). Most of that archive’s absolute radiometric calibration has been fixed through vicarious [...] Read more.
The Landsat program has been producing an archive of thermal imagery that spans the globe and covers 30 years of the thermal history of the planet at human scales (60–120 m). Most of that archive’s absolute radiometric calibration has been fixed through vicarious calibration techniques. These calibration ties to trusted values have often taken a year or more to gather sufficient data and, in some cases, it has been over a decade before calibration certainty has been established. With temperature being such a critical factor for all living systems and the ongoing concern over the impacts of climate change, NASA and the United States Geological Survey (USGS) are leading efforts to provide timely and accurate temperature data from the Landsat thermal data archive. This paper discusses two closely related advances that are critical steps toward providing timely and reliable temperature image maps from Landsat. The first advance involves the development and testing of an autonomous procedure for gathering and performing initial screening of large amounts of vicarious calibration data. The second advance discussed in this paper is the per-pixel atmospheric compensation of the data to permit calculation of the emitted surface radiance (using ancillary sources of emissivity data) and the corresponding land surface temperature (LST). Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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4586 KiB  
Article
Landsat 8 Thermal Infrared Sensor Geometric Characterization and Calibration
by James Storey, Michael Choate and Donald Moe
Remote Sens. 2014, 6(11), 11153-11181; https://doi.org/10.3390/rs61111153 - 11 Nov 2014
Cited by 17 | Viewed by 8657
Abstract
The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization [...] Read more.
The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization challenging. TIRS geometric calibration thus relied heavily on on-orbit measurements. Since the two Landsat 8 payloads are complementary and generate combined Level 1 data products, the TIRS geometric performance requirements emphasize the co-alignment of the OLI and TIRS instrument fields of view and the registration of the OLI reflective bands to the TIRS long-wave infrared emissive bands. The TIRS on-orbit calibration procedures include measuring the TIRS-to-OLI alignment, refining the alignment of the three TIRS sensor chips, and ensuring the alignment of the two TIRS spectral bands. The two key TIRS performance metrics are the OLI reflective to TIRS emissive band registration accuracy, and the registration accuracy between the TIRS thermal bands. The on-orbit calibration campaign conducted during the commissioning period provided an accurate TIRS geometric model that enabled TIRS Level 1 data to meet all geometric accuracy requirements. Seasonal variations in TIRS-to-OLI alignment have led to several small calibration parameter adjustments since commissioning. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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3130 KiB  
Article
Landsat 8 Operational Land Imager On-Orbit Geometric Calibration and Performance
by James Storey, Michael Choate and Kenton Lee
Remote Sens. 2014, 6(11), 11127-11152; https://doi.org/10.3390/rs61111127 - 11 Nov 2014
Cited by 195 | Viewed by 13444
Abstract
The Landsat 8 spacecraft was launched on 11 February 2013 carrying the Operational Land Imager (OLI) payload for moderate resolution imaging in the visible, near infrared (NIR), and short-wave infrared (SWIR) spectral bands. During the 90-day commissioning period following launch, several on-orbit geometric [...] Read more.
The Landsat 8 spacecraft was launched on 11 February 2013 carrying the Operational Land Imager (OLI) payload for moderate resolution imaging in the visible, near infrared (NIR), and short-wave infrared (SWIR) spectral bands. During the 90-day commissioning period following launch, several on-orbit geometric calibration activities were performed to refine the prelaunch calibration parameters. The results of these calibration activities were subsequently used to measure geometric performance characteristics in order to verify the OLI geometric requirements. Three types of geometric calibrations were performed including: (1) updating the OLI-to-spacecraft alignment knowledge; (2) refining the alignment of the sub-images from the multiple OLI sensor chips; and (3) refining the alignment of the OLI spectral bands. The aspects of geometric performance that were measured and verified included: (1) geolocation accuracy with terrain correction, but without ground control (L1Gt); (2) Level 1 product accuracy with terrain correction and ground control (L1T); (3) band-to-band registration accuracy; and (4) multi-temporal image-to-image registration accuracy. Using the results of the on-orbit calibration update, all aspects of geometric performance were shown to meet or exceed system requirements. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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27114 KiB  
Article
An Analysis of the Side Slither On-Orbit Calibration Technique Using the DIRSIG Model
by Aaron Gerace, John Schott, Michael Gartley and Matthew Montanaro
Remote Sens. 2014, 6(11), 10523-10545; https://doi.org/10.3390/rs61110523 - 31 Oct 2014
Cited by 18 | Viewed by 7638
Abstract
Pushbroom-style imaging systems exhibit several advantages over line scanners when used on space-borne platforms as they typically achieve higher signal-to-noise and reduce the need for moving parts. Pushbroom sensors contain thousands of detectors, each having a unique radiometric response, which will inevitably lead [...] Read more.
Pushbroom-style imaging systems exhibit several advantages over line scanners when used on space-borne platforms as they typically achieve higher signal-to-noise and reduce the need for moving parts. Pushbroom sensors contain thousands of detectors, each having a unique radiometric response, which will inevitably lead to streaking and banding in the raw data. To take full advantage of the potential exhibited by pushbroom sensors, a relative radiometric correction must be performed to eliminate pixel-to-pixel non-uniformities in the raw data. Side slither is an on-orbit calibration technique where a 90-degree yaw maneuver is performed over an invariant site to flatten the data. While this technique has been utilized with moderate success for the QuickBird satellite [1] and the RapidEye constellation [2], further analysis is required to enable its implementation for the Landsat 8 sensors, which have a 15-degree field-of-view and a 0.5% pixel-to-pixel uniformity requirement. This work uses the DIRSIG model to analyze the side slither maneuver as applicable to the Landsat sensor. A description of favorable sites, how to adjust the maneuver to compensate for the curvature of “linear” arrays, how to efficiently process the data, and an analysis to assess the quality of the side slither data, are presented. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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1803 KiB  
Article
Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor
by Matthew Montanaro, Aaron Gerace, Allen Lunsford and Dennis Reuter
Remote Sens. 2014, 6(11), 10435-10456; https://doi.org/10.3390/rs61110435 - 29 Oct 2014
Cited by 157 | Viewed by 11326
Abstract
The Thermal Infrared Sensor (TIRS) has been collecting imagery of the Earth since its launch aboard Landsat 8 in early 2013. In many respects, TIRS has been exceeding its performance requirements on orbit, particularly in terms of noise and stability. However, several artifacts [...] Read more.
The Thermal Infrared Sensor (TIRS) has been collecting imagery of the Earth since its launch aboard Landsat 8 in early 2013. In many respects, TIRS has been exceeding its performance requirements on orbit, particularly in terms of noise and stability. However, several artifacts have been observed in the TIRS data which include banding and absolute calibration discrepancies that violate requirements in some scenes. Banding is undesired structure that appears within and between the focal plane array assemblies. In addition, in situ measurements have shown an error in the TIRS absolute radiometric calibration that appears to vary with season and location within the image. The source of these artifacts has been determined to be out-of-field radiance that scatters onto the detectors thereby adding a non-uniform signal across the field-of-view. The magnitude of this extra signal can be approximately 8% or higher (band 11) and is generally twice as large in band 11 as it is in band 10. A series of lunar scans were obtained to gather information on the source of this out-of-field radiance. Analyses of these scans have produced a preliminary map of stray light, or ghost, source locations in the TIRS out-of-field area. This dataset has been used to produce a synthetic TIRS scene that closely reproduces the banding effects seen in actual TIRS imagery. Now that the cause of the banding has been determined, a stray light optics model is in development that will pin-point the cause of the stray light source. Several methods are also being explored to correct for the banding and the absolute calibration error in TIRS imagery. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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5413 KiB  
Article
Landsat-8 Operational Land Imager Design, Characterization and Performance
by Edward J. Knight and Geir Kvaran
Remote Sens. 2014, 6(11), 10286-10305; https://doi.org/10.3390/rs61110286 - 24 Oct 2014
Cited by 208 | Viewed by 16070
Abstract
The Operational Land Imager (OLI) on Landsat-8 represents a generational change from heritage Landsat instruments in its design, while it maintains data continuity with the 40+ year Landsat data archive. It preserves the 30-m ground sample distance, 185-km swath width and VIS/NIR/SWIR spectral [...] Read more.
The Operational Land Imager (OLI) on Landsat-8 represents a generational change from heritage Landsat instruments in its design, while it maintains data continuity with the 40+ year Landsat data archive. It preserves the 30-m ground sample distance, 185-km swath width and VIS/NIR/SWIR spectral bands. Furthermore, data continuity resulted from extensive pre-launch and on-orbit calibration and characterization campaigns. This paper presents an overview of the OLI design, the pre-launch characterization results and the on-orbit performance. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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3738 KiB  
Article
The Spectral Response of the Landsat-8 Operational Land Imager
by Julia A. Barsi, Kenton Lee, Geir Kvaran, Brian L. Markham and Jeffrey A. Pedelty
Remote Sens. 2014, 6(10), 10232-10251; https://doi.org/10.3390/rs61010232 - 23 Oct 2014
Cited by 302 | Viewed by 28753
Abstract
Abstract: This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and [...] Read more.
Abstract: This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and their magnitudes are consistent with the results of the pre-launch characterizations. The OLI in-band response was characterized both at the integrated instrument level for a sampling of detectors and by an analytical stack-up of component measurements. The out-of-band response was characterized using a combination of Focal Plane Module (FPM) level measurements and optical component level measurements due to better sensitivity. One of the challenges of a pushbroom design is to match the spectral responses for all detectors so that images can be flat-fielded regardless of the spectral nature of the targets in the imagery. Spectral variability can induce striping (detector-to-detector variation), banding (FPM-to-FPM variation) and other artifacts in the final data products. Analyses of the measured spectral response showed that the maximum discontinuity between FPMs due to spectral filter differences is 0.35% for selected targets for all bands except for Cirrus, where there is almost no signal. The average discontinuity between FPMs is 0.12% for the same targets. These results were expected and are in accordance with the OLI requirements. Pre-launch testing identified low levels (within requirements) of spectral crosstalk amongst the three HgCdTe (Cirrus, SWIR1 and SWIR2) bands of the OLI and on-orbit data confirms this crosstalk in the imagery. Further post-launch analyses and simulations revealed that the strongest crosstalk effect is from the SWIR1 band to the Cirrus band; about 0.2% of SWIR1 signal leaks into the Cirrus. Though the total crosstalk signal is only a few counts, it is evident in some scenes when the in-band cirrus signal is very weak. In moist cirrus-free atmospheres and over typical land surfaces, at least 30% of the cirrus signal was due to the SWIR1 band. In the SWIR1 and SWIR2 bands, crosstalk accounts for no more than 0.15% of the total signal. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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711 KiB  
Article
Radiometric Calibration Methodology of the Landsat 8 Thermal Infrared Sensor
by Matthew Montanaro, Allen Lunsford, Zelalem Tesfaye, Brian Wenny and Dennis Reuter
Remote Sens. 2014, 6(9), 8803-8821; https://doi.org/10.3390/rs6098803 - 19 Sep 2014
Cited by 51 | Viewed by 11714
Abstract
The science-focused mission of the Landsat 8 Thermal Infrared Sensor (TIRS) requires that it have an accurate radiometric calibration. A calibration methodology was developed to convert the raw output from the instrument into an accurate at-aperture radiance. The methodology is based on measurements [...] Read more.
The science-focused mission of the Landsat 8 Thermal Infrared Sensor (TIRS) requires that it have an accurate radiometric calibration. A calibration methodology was developed to convert the raw output from the instrument into an accurate at-aperture radiance. The methodology is based on measurements obtained during component-level and instrument-level characterization testing. The radiometric accuracy from the pre-flight measurements was estimated to be approximately 0.7%. The calibration parameters determined pre-flight were updated during the post-launch checkout period by utilizing the on-board calibration sources and Earth scene data. These relative corrections were made to adjust for differences between the pre-flight and the on-orbit performance of the instrument, thereby correcting large striping artifacts observed in Earth imagery. Despite this calibration correction, banding artifacts (low frequency variation in the across-track direction) have been observed in certain uniform Earth scenes, but not in other uniform scenes. In addition, the absolute calibration performance determined from vicarious measurements have revealed a time-varying error to the absolute radiance reported by TIRS. These issues were determined to not be caused by the calibration process developed for the instrument. Instead, an investigation has revealed that stray light is affecting the recorded signal from the Earth. The varying optical stray light effect is an ongoing subject of evaluation and investigation, and a correction strategy is being devised that will be added to the calibration process. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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1866 KiB  
Article
Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI, for Both Top-of-Atmosphere and Surface Reflectance: A Study in the Australian Landscape
by Neil Flood
Remote Sens. 2014, 6(9), 7952-7970; https://doi.org/10.3390/rs6097952 - 26 Aug 2014
Cited by 129 | Viewed by 12234
Abstract
The new Landsat-8 Operational Land Imager (OLI) is intended to be broadly compatible with the previous Landsat-7 Enhanced Thematic Mapper Plus (ETM+). The spectral response of the OLI is slightly different to the ETM+, and so there may be slight differences in the [...] Read more.
The new Landsat-8 Operational Land Imager (OLI) is intended to be broadly compatible with the previous Landsat-7 Enhanced Thematic Mapper Plus (ETM+). The spectral response of the OLI is slightly different to the ETM+, and so there may be slight differences in the reflectance measurements. Since the differences are a function not just of spectral responses, but also of the target pixels, there is a need to assess these differences in practice, using imagery from the area of interest. This paper presents a large scale study of the differences between ETM+ and OLI in the Australian landscape. The analysis is carried out in terms of both top-of-atmosphere and surface reflectance, and also in terms of biophysical parameters modelled from those respective reflectance spectra. The results show small differences between the sensors, which can be magnified by modelling to a biophysical parameter. It is also shown that a part of this difference appears to be systematic, and can be reliably removed by regression equations to predict ETM+ reflectance from OLI reflectance, before applying biophysical models. This is important when models have been fitted to historical field data coincident with ETM+ imagery. However, there will remain a small per-pixel difference which could be an unwanted source of variability. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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