A Software Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 8; no. 9; p. 696
• Main Authors: Tardy, Benjamin, Rivalland, Vincent, Huc, Mireille, Hagolle, Olivier, Marcq, Sebastien, Boulet, Gilles
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2016; MDPI
• Subjects: Atmosphere; Atmospheric correction; Atmospherics; Computer programs; Computer Science; Continental interfaces, environment; Emissivity; Environmental research; Environmental Sciences; Land surface temperature; Landsat; Mathematical models; Programming languages; Radiative transfer; Remote sensing; Satellite imagery; Sciences of the Universe; Sensitivity analysis; Signal and Image Processing; Software; software tool; Temperature measurement; thermal infrared remote sensing; Water vapor; thermal infrared remote sensing; emissivity; software tool; land surface temperature; atmospheric correction; Landsat
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs8090696

Land surface temperature (LST) is an important variable involved in the Earth's surface energy and water budgets and a key component in many aspects of environmental research. The Landsat program, jointly carried out by NASA and the USGS, has been recording thermal infrared data for the past 40 years. Nevertheless, LST data products for Landsat remain unavailable. The atmospheric correction (AC) method commonly used for mono-window Landsat thermal data requires detailed information concerning the vertical structure (temperature, pressure) and the composition (water vapor, ozone) of the atmosphere. For a given coordinate, this information is generally obtained through either radio-sounding or atmospheric model simulations and is passed to the radiative transfer model (RTM) to estimate the local atmospheric correction parameters. Although this approach yields accurate LST data, results are relevant only near this given coordinate. To meet the scientific community's demand for high-resolution LST maps, we developed a new software tool dedicated to processing Landsat thermal data. The proposed tool improves on the commonly-used AC algorithm by incorporating spatial variations occurring in the Earth's atmosphere composition. The ERA-Interim dataset (ECMWFmeteorological organization) was used to retrieve vertical atmospheric conditions, which are available at a global scale with a resolution of 0.125 degrees and a temporal resolution of 6 h. A temporal and spatial linear interpolation of meteorological variables was performed to match the acquisition dates and coordinates of the Landsat images. The atmospheric correction parameters were then estimated on the basis of this reconstructed atmospheric grid using the commercial RTMsoftware MODTRAN. The needed surface emissivity was derived from the common vegetation index NDVI, obtained from the red and near-infrared (NIR) bands of the same Landsat image. This permitted an estimation of LST for the entire image without degradation in resolution. The software tool, named LANDARTs, which stands for Landsat automatic retrieval of surface temperatures, is fully automatic and coded in the programming language Python. In the present paper, LANDARTs was used for the local and spatial validation of surface temperature obtained from a Landsat dataset covering two climatically contrasting zones: southwestern France and central Tunisia. Long-term datasets of in situ surface temperature measurements for both locations were compared to corresponding Landsat LST data. This temporal comparison yielded RMSE values ranging from 1.84 degree C-2.55 degree C. Landsat surface temperature data obtained with LANDARTs were then spatially compared using the ASTER data products of kinetic surface temperatures (AST08) for both geographical zones. This comparison yielded a satisfactory RMSE of about 2.55 degree C. Finally, a sensitivity analysis for the effect of spatial validation on the LST correction process showed a variability of up to 2 degree C for an entire Landsat image, confirming that the proposed spatial approach improved the accuracy of Landsat LST estimations.