Radiometric Calibration Methodology of the Landsat 8 Thermal Infrared Sensor

Montanaro, Matthew; Lunsford, Allen; Tesfaye, Zelalem; Wenny, Brian N.; Reuter, Dennis C.

doi:10.3390/rs6098803

Cited by 50 publications

(38 citation statements)

References 8 publications

(13 reference statements)

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“…Although, the radiometric and geometric refinements have been implemented in thermal infrared band offsets soon and improved the data accuracy to −2.1 ± 0.8 K for band 10 and −4.4 ± 1.75 K for band 11 at a 300 K brightness temperature respectively, this accuracy still cannot meet the requirements of algorithm application [48,49]. Meanwhile, according to the reprocessing campaign results, this uncertainty is scene-dependent and probably related to out-of-field response in the TIRS instrument, and gives us an obstacle in algorithm validation, for we cannot analyze whether errors are caused by TIRS sensor or the algorithm.…”

Section: Discussionmentioning

confidence: 99%

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

et al. 2015

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This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance-variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

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Section: Discussionmentioning

confidence: 99%

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

et al. 2015

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“…Table 2 lists the types of calibrations that are being used by TIRS on-orbit. Although some of the calibration parameters have changed a bit from their ground-based values [6,7,8,16,17], TIRS performance has been very stable [17] since its initial turn on in orbit.…”

Section: On-orbit Calibration Methodsmentioning

confidence: 99%

“…In what follows, only the basic spatial, spectral and noise requirements are discussed. More detail is given in the other papers in this issue [6][7][8].…”

Section: Requirements Overviewmentioning

confidence: 99%

“…There are non-linearities designed in the QWIP arrays to increase their sensitivity at low light levels. In particular, the output capacitance changes at ~15% of the full well, so that the slope (V/e-) is about four-times higher in the region from 0% to 15% of the full well than it is from 15% to 100% of the full well (see [6] for more details). The first step in the conversion to radiance, therefore, is to linearize the digital output from the arrays for each frame.…”

Section: Tirs Calibration and Pre-launch Performancementioning

confidence: 99%

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The Thermal Infrared Sensor (TIRS) on Landsat 8: Design Overview and Pre-Launch Characterization

Reuter

Richardson²,

Pellerano³

et al. 2015

Remote Sensing

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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.

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“…Note that this is not the actual gain equation. Instrument gain and bias are covered in detail in [12]. Since launch, the SCA-average metric may have a slowly decreasing trend (maximum of −0.28%/year ± 0.005%/year in the worst case) but the total variability over the lifetime is still only 0.08% (1σ) for the worst case ( Figure 4).…”

Section: Internal Calibratormentioning

confidence: 99%

Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration

et al. 2014

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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 OPEN ACCESSRemote Sens. 2014, 6 11608 of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/m 2 ·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/m 2 ·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.

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Radiometric Calibration Methodology of the Landsat 8 Thermal Infrared Sensor

Cited by 50 publications

References 8 publications

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

The Thermal Infrared Sensor (TIRS) on Landsat 8: Design Overview and Pre-Launch Characterization

Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration

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