Toward an operational stray light correction for the Landsat 8 Thermal Infrared Sensor

Montanaro, Matthew; Gerace, Aaron; Rohrbach, Scott

doi:10.1364/ao.54.003963

Cited by 37 publications

(32 citation statements)

References 7 publications

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“…Unfortunately, these discrepancies are not consistent across the focal plane, making a correction a challenging task [26,79]. Since these errors were reported, several calibration and reprocessing efforts have been carried out [79,80].…”

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 magnitude of this extra signal can be ∼ 8 % or higher (band 11) and is generally twice as large in band 11 as in band 10. Correction algorithms for this artifact have been developed and applied in the new version of level L1T Landsat-8 data (Montanaro et al, 2015), and the stray light artifact in the current product is reduced by half on average (Gerace and Montanaro, 2017). However, the artifact could be amplified in a surface temperature map when SWA is used, with a temperature error of 0-2 K or more (Gerace and Montanaro, 2017), which would certainly impact lead detection from IST maps.…”

Section: Ist Retrievalmentioning

confidence: 99%

Estimation of turbulent heat flux over leads using satellite thermal images

Pang

Zhao

et al. 2019

The Cryosphere

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Abstract. Sea ice leads are an important feature in pack ice in the Arctic. Even covered by thin ice, leads can still serve as prime windows for heat exchange between the atmosphere and the ocean, especially in the winter. Lead geometry and distribution in the Arctic have been studied using optical and microwave remote sensing data, but turbulent heat flux over leads has only been measured on-site during a few special expeditions. In this study, we derive turbulent heat flux through leads at different scales using a combination of surface temperature and lead distribution from remote sensing images and meteorological parameters from a reanalysis dataset. First, ice surface temperature (IST) was calculated from Landsat-8 Thermal Infrared Sensor (TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images using a split-window algorithm; then, lead pixels were segmented from colder ice. Heat flux over leads was estimated using two empirical models: bulk aerodynamic formulae and a fetch-limited model with lead width from Landsat-8. Results show that even though the lead area from MODIS is a little larger, the length of leads is underestimated by 72.9 % in MODIS data compared to TIRS data due to the inability to resolve small leads. Heat flux estimated from Landsat-8 TIRS data using bulk formulae is 56.70 % larger than that from MODIS data. When the fetch-limited model was applied, turbulent heat flux calculated from TIRS data is 32.34 % higher than that from bulk formulae. In both cases, small leads accounted for more than a quarter of total heat flux over leads, mainly due to the large area, though the heat flux estimated using the fetch-limited model is 41.39 % larger. A greater contribution from small leads can be expected with larger air–ocean temperature differences and stronger winds.

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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.…”

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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Toward an operational stray light correction for the Landsat 8 Thermal Infrared Sensor

Cited by 37 publications

References 7 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

Estimation of turbulent heat flux over leads using satellite thermal images

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

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