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

Barsi, Julia A.; Schott, John R.; Hook, Simon J.; Raqueño, Nina G.; Markham, Brian L.; Radocinski, R. G.

doi:10.3390/rs61111607

Cited by 328 publications

(170 citation statements)

References 19 publications

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“…Prior to the early-2014 update, users might subtract 0.29 W/(m 2 ·sr·μm) from every TIRS Band 10 calibrated radiance value and 0.51 W/(m 2 ·sr·μm) for every TIRS Band 11 calibrated radiance value to provide values closer (on average) to the actual radiances. The root mean square (RMS) variability in the required adjustment was roughly 0.12 W/(m 2 ·sr·μm) (0.8 K) for Band 10 and 0.2 W/(m 2 ·sr·μm) (1.75 K) for Band 11 [15]. Moreover, the USGS pointed out that, given the larger uncertainty in the Band 11 values, users should work with TIRS Band 10 data as a single spectral band (like Landsat 7 Enhanced Thematic Mapper Plus (ETM+)) and should not attempt a split-window correction using both TIRS Bands 10 and 11.…”

Section: Introductionmentioning

confidence: 99%

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

et al. 2015

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Abstract:The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for OPEN ACCESSRemote Sens. 2015, 7 4269 the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

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Section: Introductionmentioning

confidence: 99%

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

et al. 2015

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“…Based on the existing study, even if the spatial resolution of the Landsat-8 TIRS (100 m) was only 100 m, it was resampled to 30 m in the delivered data product [33], and spectral mixing plus the heat diffusion of the built-up area blurred the boundary of the built-up area. Thus, a finer spatial resolution image of the vis-infrared built-up indices may help sharpen the boundary of the extracted built-up area from the NDSTI index, or by adopting a resolution merging method to enhance the overall spatial resolution [51][52][53][54][55].…”

Section: Discussionmentioning

confidence: 99%

“…Each had little noise from clouds and was downloaded from the United States Geological Survey website [32]. In terms of selecting the thermal infrared band, TIRS Band 10 was only used as the calibration parameters of the TIRS 11 thermal infrared band are still unstable and have noise interference [33]. Therefore, this study selected respective three-date Landsat-8 images of four areas that could reflect the distinctive thermal pattern characteristics of the target built-up area (Table 1).…”

Section: Data Sources and Pre-processingmentioning

confidence: 99%

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A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

et al. 2017

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Abstract:Recently, studies have focused more attention on surface feature extraction using thermal infrared remote sensing (TIRS) as supplementary materials. Innovatively, in this paper, using three-date (winter, early spring, and end of spring) TIRS Band 10 images of Landsat-8, we proposed an empirical normalized difference of a seasonal brightness temperature index (NDSTI) for enhancing a built-up area based on the contrast heat emission seasonal response of a built-up area to solar radiation, and adopted a decision tree classification method for the rapidly accurate extraction of the built-up area. Four study areas, including one major experimental study area (Tangshan) and three verification areas (Minqin, Laizhou, and Yugan) in different climate zones, respectively, were used to empirically establish the overall strategy system, then we specified constrained conditions of this strategy. Moreover, we compared the NDSTI to the current built-up indices, respectively, for extracting the built-up area. The results showed that (1) the new index (NDSTI) exploited the seasonal thermal characteristic variation between the built-up area and other covers in the time series analysis, helping achieve more accurate built-up area extraction than other spectral indices; (2) this strategy could effectively realize rapid built-up area extraction with generally satisfied overall accuracy (over 80%), and was especially excellent in Tangshan and Laizhou; however, (3) it may be constrained by climate patterns and other surface characteristics, which need to be improved from the view of the results of Minqin and Yugan. In summary, the method developed in this study has the potential and advantage to extract the built-up area rapidly from the multi-seasonal thermal infrared remote sensing data. It could be an operative tool for long-term monitoring of built-up areas efficiently and for more applications of thermal infrared images in the future.

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“…Ultimately, a solution was achieved that relates knowledge of the stray-light sources in the TIRS out-of-field to additional signal on the TIRS focal plane, 3,4 according to Eq. (1).…”

Section: Introductionmentioning

confidence: 99%

Leveraging intercalibration techniques to support stray-light removal from Landsat 8 Thermal Infrared Sensor data

Gerace

Montanaro

Connal

2017

J. Appl. Remote Sens

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Abstract. Since its launch in 2013, the Thermal Infrared Sensor (TIRS) onboard Landsat 8 has exhibited artifacts in its image data that can be attributed to stray-light. A 3-year effort was initiated to develop a stray-light correction algorithm to support TIRS calibration. A methodology was developed to predict the additional (stray-light) signal on each detector from an estimate of the stray-light source locations in the sensor's out-of-field area. The initial version of the algorithm estimated the magnitude of out-of-field radiance sources through the use of geostationary wide-field thermal band imagers. However, this methodology necessitated a strong effort to cross calibrate the two sensors. Ultimately, a variation of the algorithm was implemented operationally into the United States Geological Survey ground system that utilizes image data from TIRS itself as an estimate of the out-of-field stray-light sources. This paper highlights the intercalibration techniques investigated while developing the stray-light correction algorithm. The impact of differing view-angles, spectral responses, and collection times on at-sensor radiance was considered to assess the feasibility of using data from Geostationary Operational Environmental Satellite geostationary instruments to estimate the out-of-field stray-light radiance incident on the TIRS detectors. Results of the studies presented here illustrate the complexities associated with intercalibration in the thermal and provide justification for the current form of the TIRS stray-light correction algorithm.

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

Cited by 328 publications

References 19 publications

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

Leveraging intercalibration techniques to support stray-light removal from Landsat 8 Thermal Infrared Sensor data

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