Surface parameters evaluated from satellite remote sensing images for pollutant atmospheric dispersion modelling

This contribute deals with the use of surface parameters extracted from satellite remote sensing images (SRSI) for the setup of the input dataset required by pollutants atmospheric dispersion models (PATM). Accurate PATM need 2D distributions (grids) of many surface parameters to model turbulence parameters, as roughness length, albedo, leaf area index and Bowen’s ratio. Very often these parameters are set using predefined tables defined as a function of land cover (LC). Usually, this last information is extracted from public dataset, such as, for European countries, the Corine Land Cover (CLC). Several of these parameters can be computed directly from SRSI. Moreover, land cover classification evaluated from SRSI can be used to update existing LC datasets.In the first part of this work it is shown how ASTER remote sensing images have been used to calculate several surface parameters maps, as albedo, vegetation indexes (NDVI, LAI) and net radiation flux on a study area at Porto Marghera (Venice, Italy). This area is located near the coastline of a very complex areas surrounded by Venice lagoon, Adriatic Sea, cultivated, industrialized and habited zones. Moreover, the area is characterised by particular meteorological situation, with heavy sea-breeze phenomena. In the second part, the ASTER image is used to evaluate, using a supervised classification method, the LC of the area. Then, this LC is used to update the CLC. In the last part of the work the importance of these information extracted from the ASTER image is evaluated using the SPRAY lagrangian PATM. SPRAY has been used to simulated the dispersion of an inert generic pollutant emitted from a virtual source located in central part of the area, near the coast, from 29 August to 10 September 2006. For this time period a real (measured) meteorological data set have been used.