Sensitivity of Landsat 8 surface temperature estimates to atmospheric profile data: A study using MODTRAN in dryland irrigated systems

Jorge Rosas*, Rasmus Houborg, Matthew F. McCabe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


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.

Original languageEnglish (US)
Article number988
JournalRemote Sensing
Issue number10
StatePublished - Oct 1 2017

Bibliographical note

Funding Information:
Acknowledgments: Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). We greatly appreciate the logistical, equipment and scientific support offered to our team by Jack King, Alan King and employees of the Tawdeehiya Farm in Al Kharj, Saudi Arabia, without whom this research would not have been possible.

Publisher Copyright:
© 2017 by the authors.


  • AIRS
  • Atmospheric correction
  • IGRA
  • Land surface temperature
  • Landsat
  • MOD07
  • NCEP
  • Remote sensing
  • Thermal infrared

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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