Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor

Montanaro, Matthew; Gerace, Aaron; Lunsford, Allen; Reuter, Dennis C.

doi:10.3390/rs61110435

Cited by 157 publications

(95 citation statements)

References 9 publications

(17 reference statements)

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“…Though Landsat 8 TIRS has two thermal bands (10 and 11), only data from Band 10 are suitable at the moment for LST retrieva1 due to the larger uncertainty in the Band 11 values [13][14][15]. Therefore, we improved the mono-window algorithm in the following form for LST retrieval from the Landsat 8 TIRS Band 10 data:…”

Section: Development Of the Imw Algorithm For Landsat 8 Datamentioning

confidence: 99%

“…Jiménez-Muñoz et al [12] also adapted their single-channel (SC) algorithms and split-window algorithms to Landsat 8 TIRS data for LST retrieval. However, several artifacts, including banding and absolute calibration discrepancies that violate the requirements in some scenes [13], had been observed in the TIRS data. Recently, the United States Geological Survey (USGS) issued a notice on its websites relating to the calibration of Landsat 8 TIRS thermal bands [14].…”

Section: Introductionmentioning

confidence: 99%

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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: Development Of the Imw Algorithm For Landsat 8 Datamentioning

confidence: 99%

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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“…The algorithm proposed by Ref. 3 suggested utilizing wide-field image data from the GOES-N series imagers to estimate the out-of-field stray-light radiance for TIRS.…”

Section: Discussionmentioning

confidence: 99%

“…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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“…Due to stray light anomalies within the two thermal bands of the TIRS, the U.S. Geological Survey (USGS) advised users to work only with TIRS band 10 with lower expected errors compared to band 11 before 24 April 2017 [46,47]. In the meantime, NASA recomputed all L8 TIR data according to the algorithm of Gerace and Montanaro (2017), which reduces the error of both TIR bands to less than 1 K [48].…”

Section: Problems With Thermal Infrared Datamentioning

confidence: 99%

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Wicki

Parlow

2017

Remote Sensing

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Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data.

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Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor

Cited by 157 publications

References 9 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

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

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

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