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Proceedings Paper

Initial validation of atmospheric compensation for a Landsat land surface temperature product
Author(s): Monica J. Cook; John R. Schott
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Paper Abstract

The Landsat series of satellites is the longest set of continuously acquired moderate resolution multispectral satellite imagery collected on a single maintained family of instruments. The data are very attractive because the entire archive has been radiometrically calibrated and characterized so that sensor reaching radiance values are well known. Because of the spatial and temporal coverage provided by Landsat, it is an intriguing candidate for a land surface temperature (LST) product, an important earth system data record for a number of fields including numerical weather prediction, climate research and a number of agricultural applications. Using the Landsat long-wave infrared thermal band, LST can be derived with a well-characterized atmosphere and a known surface emissivity. This work integrates the North America Regional Reanalysis dataset (atmospheric profile data) with ASTER derived emissivity data to perform LST retrievals. This paper emphasizes progress toward atmospheric compensation at each Landsat pixel. Due to differences in temporal and spatial sampling, a number of interpolations are required to compute the radiance due to temperature at each pixel. Radiosonde data and water temperatures derived from buoys are used as ground truth data to explore the error in the final predicted temperature. Preliminary results show consistent errors of less than 1 K in clear atmospheres but higher errors in hotter and more humid atmospheres. Future work will analyze results to predict error in the final retrieved temperatures using atmospheric conditions. The final goal is to report both a predicted LST and a confidence in this value.

Paper Details

Date Published: 18 May 2013
PDF: 13 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874314 (18 May 2013); doi: 10.1117/12.2015320
Show Author Affiliations
Monica J. Cook, Rochester Institute of Technology (United States)
John R. Schott, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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