@article{fdi:010068674,
  title = {{A} software tool for atmospheric correction and surface temperature estimation of landsat infrared thermal data},
  author = {{T}ardy, {B}. and {R}ivalland, {V}. and {H}uc, {M}. and {H}agolle, {O}. and {M}arcq, {S}. and {B}oulet, {G}illes},
  editor = {},
  language = {{ENG}},
  abstract = {{L}and surface temperature ({LST}) is an important variable involved in the {E}arth's surface energy and water budgets and a key component in many aspects of environmental research. {T}he {L}andsat program, jointly carried out by {NASA} and the {USGS}, has been recording thermal infrared data for the past 40 years. {N}evertheless, {LST} data products for {L}andsat remain unavailable. {T}he atmospheric correction ({AC}) method commonly used for mono-window {L}andsat thermal data requires detailed information concerning the vertical structure (temperature, pressure) and the composition (water vapor, ozone) of the atmosphere. {F}or 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. {A}lthough this approach yields accurate {LST} data, results are relevant only near this given coordinate. {T}o meet the scientific community's demand for high-resolution {LST} maps, we developed a new software tool dedicated to processing {L}andsat thermal data. {T}he proposed tool improves on the commonly-used {AC} algorithm by incorporating spatial variations occurring in the {E}arth's atmosphere composition. {T}he {ERA}-{I}nterim dataset ({ECMWF}meteorological 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 {L}andsat images. {T}he atmospheric correction parameters were then estimated on the basis of this reconstructed atmospheric grid using the commercial {RTM}software {MODTRAN}. {T}he needed surface emissivity was derived from the common vegetation index {NDVI}, obtained from the red and near-infrared ({NIR}) bands of the same {L}andsat image. {T}his permitted an estimation of {LST} for the entire image without degradation in resolution. {T}he software tool, named {LANDART}s, which stands for {L}andsat automatic retrieval of surface temperatures, is fully automatic and coded in the programming language {P}ython. {I}n the present paper, {LANDART}s was used for the local and spatial validation of surface temperature obtained from a {L}andsat dataset covering two climatically contrasting zones: southwestern {F}rance and central {T}unisia. {L}ong-term datasets of in situ surface temperature measurements for both locations were compared to corresponding {L}andsat {LST} data. {T}his temporal comparison yielded {RMSE} values ranging from 1.84 degrees {C}-2.55 degrees {C}. {L}andsat surface temperature data obtained with {LANDART}s were then spatially compared using the {ASTER} data products of kinetic surface temperatures ({AST}08) for both geographical zones. {T}his comparison yielded a satisfactory {RMSE} of about 2.55 degrees {C}. {F}inally, a sensitivity analysis for the effect of spatial validation on the {LST} correction process showed a variability of up to 2 degrees {C} for an entire {L}andsat image, confirming that the proposed spatial approach improved the accuracy of {L}andsat {LST} estimations.},
  keywords = {land surface temperature ; {L}andsat ; software tool ; atmospheric ; correction ; thermal infrared remote sensing ; emissivity},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {8},
  numero = {9},
  pages = {art. 696 [24 p.]},
  ISSN = {2072-4292},
  year = {2016},
  DOI = {10.3390/rs8090696},
  URL = {https://www.documentation.ird.fr/hor/fdi:010068674},
}