Derivation and validation of the stray light correction algorithm for the thermal infrared sensor onboard Landsat 8

Gerace, Aaron; Montanaro, Matthew

doi:10.1016/j.rse.2017.01.029

Cited by 97 publications

(68 citation statements)

References 9 publications

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“…This is apparent by the positive bias during the summer period in this study (Figure 3). After this study was carried out, the CVT released a new stray light correction method for the TIRS that reduces the TIRS uncertainty to under 0.5% [81], reducing the errors from 2 K @ 300 K with no correction to 0.3 K with the stray light correction for band 10. The study also found out that light was impinging on the detectors from a ring about 13 • outside of the field of view, suggesting that the overall higher bias during the hot days might be attributable to hot desert soil around the study area reaching the TIRS.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

2017

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Abstract:The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance data from which LST can be determined. One of the most complete records of data comes via the Landsat series of satellites, which provide a continuous sequence that extends back to 1982. However, for much of this time, Landsat thermal data were provided through a single broadband thermal channel, making surface temperature retrieval challenging. To fully exploit the valuable time-series of thermal information that is available from these satellites requires efforts to better describe and understand the accuracy of temperature retrievals. Here, we contribute to these efforts by examining the impact of atmospheric correction on the estimation of LST, using atmospheric profiles derived from a range of in-situ, reanalysis, and satellite data. Radiance data from the thermal infrared (TIR) sensor onboard Landsat 8 was converted to LST by using the MODTRAN version 5.2 radiative transfer model, allowing the production of an LST time series based upon 28 Landsat overpasses. LST retrievals were then evaluated against in-situ thermal measurements collected over an arid zone farmland comprising both bare soil and vegetated surface types. Atmospheric profiles derived from AIRS, MOD07, ECMWF, NCEP, and balloon-based radiosonde data were used to drive the MODTRAN simulations. In addition to examining the direct impact of using various profile data on LST retrievals, randomly distributed errors were introduced into a range of forcing variables to better understand retrieval uncertainty. Results indicated differences in LST of up to 1 K for perturbations in emissivity and profile measurements, with the analysis also highlighting the challenges in modeling aerosol optical depth (AOD) over arid lands and its impact on the TIR bands. Days with high AOD content (AOD > 0.5) in the evaluation study seem to consistently underestimate in-situ LSTs by 1-2 K, suggesting that MODTRAN is unable to accurately simulate the aerosol conditions for the TIR bands. Comparisons between available in-situ and Landsat 8 derived LST illustrate a range of seasonal and land surface dynamics and provide an assessment of retrieval accuracy throughout the nine-month long study period. In terms of the choice of atmospheric profile, when excluding the in-situ data, results show a mean absolute range of between 1.2 K to 1.8 K over bare soil and 3.3 K to 3.8 K over alfalfa for the different meteorological forcing, with the AIRS profile providing the best reproduction over the studied arid land irrigation region.

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Section: Discussionmentioning

confidence: 99%

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

2017

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“…The correction algorithm of Montanaro et al (2015), refined by Gerace and Montanaro (2017), was applied to the TIRS band 10 data to further reduce the effects of stray light. This stray light correction algorithm has since been implemented operationally into the Landsat Product Generation System in early 2017 by the USGS (Gerace and Montanaro, 2017), and methods are currently being developed to continually improve the accuracy of SST retrievals from the Landsat 8 TIRS. Herein, TIRS data are used solely to detect relative SST differences between plume and nonplume waters due to current limitations in TIRS-derived SST accuracy and lack of in situ skin temperature data collection during the diversion.…”

Section: Landsat 8 Tirs Retrievals Of Sea Surface Temperaturementioning

confidence: 99%

Application of Landsat 8 for Monitoring Impacts of Wastewater Discharge on Coastal Water Quality

et al. 2017

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“…Uncertainties due to a stray light error were removed in a reprocessing step by the data provider (valid for data downloaded after 5 May 2017). Thereby, the estimated error was reduced from 4 K to less than 1 K [48]. In our data, we found a temperature shift from old data to stray-light-corrected data ranging from 0.3 to 2.1 K, with average differences around 1 to 1.5 K. The connections between LULC and LST are first shown using box plots and bar plots combining percentages of LULC and average LST.…”

Section: Thermal Infrared Remote Sensing Problemsmentioning

confidence: 58%

“…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]. This paper makes use of the stray light corrected L8 TIR data.…”

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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Derivation and validation of the stray light correction algorithm for the thermal infrared sensor onboard Landsat 8

Cited by 97 publications

References 9 publications

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

Application of Landsat 8 for Monitoring Impacts of Wastewater Discharge on Coastal Water Quality

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

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