OPENAIRE OpenAire Sygma HAL INRAE Bielefeld Academic Search Engine (BASE) UnpayWall HAL-INSU HAL-IRD Horizon / Pleins textes Crossref Microsoft Academic Graph European Union Open Data Portal Remote Sensing of Environment Open Access 2016-11-26 2016-12-08 Abstract The remotely sensed land surface temperature (LST) is a key parameter to monitor surface energy and water fluxes but the strong impact of topography on LST has limited its use to mostly flat areas. To fill the gap, this study proposes a physically-based method to normalize LST data for topographic - namely illumination and elevation - effects over mountainous areas. Both topographic effects are first quantified by inverting a dual-source soil/vegetation energy balance (EB) model forced by 1) the instantaneous solar radiation simulated by a 3D radiative transfer model named DART (Discrete Anisotropic Radiative Transfer) that uses a digital elevation model (DEM), 2) a satellite-derived vegetation index, and 3) local meteorological (air temperature, air relative humidity and wind speed) data available at a given location. The satellite LST is then normalized for topography by simulating the LST using both pixel- and image-scale DART solar radiation and elevation data. The approach is tested on three ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) overpass dates over a steep-sided 6 km by 6 km area in the Atlas Mountain in Morocco. The mean correlation coefficient and root mean square difference (RMSD) between EB-simulated and ASTER LST is 0.80 and 3 °C, respectively. Moreover, the EB-based method is found to be more accurate than a more classical approach based on a multi-linear regression with DART solar radiation and elevation data. The EB-simulated LST is also evaluated against an extensive ground dataset of 135 autonomous 1-cm depth temperature sensors deployed over the study area. While the mean RMSD between 90 m resolution ASTER LST and localized ibutton measurements is 6.1 °C, the RMSD between EB-simulated LST and ibutton soil temperature is 5.4 and 5.3 °C for a DEM at 90 m and 8 m resolution, respectively. The proposed topographic normalization is self-calibrated from (LST, DEM, vegetation index and in situ meteorological data) data available over large extents. As a significant perspective this approach opens the path to using normalized LST as input to evapotranspiration retrieval methods based on LST. [SDE] Environmental Sciences 550 Topographic normalization DEM 0207 environmental engineering Energy balance 02 engineering and technology 01 natural sciences ASTER 13. Climate action [SDE]Environmental Sciences DART Land surface temperature 0105 earth and related environmental sciences Malbéteau, Yoann, Merlin, Olivier, Gascoin, Simon, Gastellu-Etchegorry, Jean-Philippe, Mattar, C, Olivera-Guerra, Luis Enrique, Khabba, S, Jarlan, Lionel, 2017-02-01 journalpaper Abstract The remotely sensed land surface temperature (LST) is a key parameter to monitor surface energy and water fluxes but the strong impact of topography on LST has limited its use to mostly flat areas. To fill the gap, this study proposes a physically-based method to normalize LST data for topographic - namely illumination and elevation - effects over mountainous areas. Both topographic effects are first quantified by inverting a dual-source soil/vegetation energy balance (EB) model forced by 1) the instantaneous solar radiation simulated by a 3D radiative transfer model named DART (Discrete Anisotropic Radiative Transfer) that uses a digital elevation model (DEM), 2) a satellite-derived vegetation index, and 3) local meteorological (air temperature, air relative humidity and wind speed) data available at a given location. The satellite LST is then normalized for topography by simulating the LST using both pixel- and image-scale DART solar radiation and elevation data. The approach is tested on three ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) overpass dates over a steep-sided 6 km by 6 km area in the Atlas Mountain in Morocco. The mean correlation coefficient and root mean square difference (RMSD) between EB-simulated and ASTER LST is 0.80 and 3 °C, respectively. Moreover, the EB-based method is found to be more accurate than a more classical approach based on a multi-linear regression with DART solar radiation and elevation data. The EB-simulated LST is also evaluated against an extensive ground dataset of 135 autonomous 1-cm depth temperature sensors deployed over the study area. While the mean RMSD between 90 m resolution ASTER LST and localized ibutton measurements is 6.1 °C, the RMSD between EB-simulated LST and ibutton soil temperature is 5.4 and 5.3 °C for a DEM at 90 m and 8 m resolution, respectively. The proposed topographic normalization is self-calibrated from (LST, DEM, vegetation index and in situ meteorological data) data available over large extents. As a significant perspective this approach opens the path to using normalized LST as input to evapotranspiration retrieval methods based on LST. Normalizing land surface temperature data for elevation and illumination effects in mountainous areas: A case study using ASTER data over a steep-sided valley in Morocco 2552941062 https://doi.org/2552941062 645642