correction for the impact of the surface characteristics on the estimation of the effective emissivity at fine resolution in urban areas

Most of the methods used to retrieve land surface temperature (LST) from thermal infrared (TIR) satellite data in urban areas do not take into account the complexity of the surface. Cities are characterized by high surface roughness and one of the main constraints to estimate LST over those areas is the difficulty to define an effective emissivity for a given pixel at a given scale. When working with mixed pixels, the emissivity used to estimate the LST is an effective emissivity composed of the emissivities of each basic element constituting the pixel. In urban areas, the surface geometry has a strong impact on this effective emissivity. Its estimation from TIR satellite data must be carried out considering multiple surface reflections and diffusions within the urban canopy in order to retrieve accurate LST values. The objective of this study is then to evaluate the impact of the surface geometry within the pixel on effective emissivity estimation and to propose a method to derive an effective emissivity corrected for those effects. Emissivity can be derived at 90 m of spatial resolution from the TIR data acquired by ASTER. To evaluate the impact of the geometry at the scale of an ASTER pixel, several urban canyon configurations are designed to develop and test the correction method. The basic principle behind the method is to accurately estimate the downwelling TIR radiation received by a pixel integrating contributions from both the atmosphere and the scene inside this pixel and then derive the corrected effective emissivity from ASTER data using the TES (temperature emissivity separation) algorithm. First, the total downwelling TIR radiation is estimated from the geometric characteristics of the scene, using morphological indicators and integrating the non-isothermal behavior of the pixel thanks to 3D thermo-radiative model simulations. The validation of those estimations for each canyon configuration provides a maximum RMSE (Root Mean Square Error) value of 2.2 W·m−2. The validation performed over a district extracted from the 3D numerical model of Strasbourg (France) shows a RMSE of 2.5 W·m−2. Once the method to estimate the total downwelling TIR radiation is validated, LSE and LST maps are retrieved from an ASTER image over three districts of Strasbourg, showing that accounting for the surface geometry highlights thermal behavior differences inside districts, and that the impact of the geometry seems more influenced by building height than street width or building density.