This presentation investigates the use of remote sensing image processing techniques to estimate input data for air quality models. These models actually use a lot of input data, whose collection can be inaccurate or costly. Thanks to their high spatial and temporal resolutions and their rich spectral content, remote sensing data can be analysed to estimate some input data in an objective and accurate way. This research is led at INRIA (AIR project) in collaboration with GMD Institute for Computer Architecture and Software Technology (FIRST), Berlin within the ERCIM working group Environmental Modelling.