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Increasing radiometric accuracy and spectral resolution of the new aerospace optical imagers for Earth observation could allow a better characterization of the environment. This is true if accurate radiometric calibrations of the sensor are performed and atmospheric effects on the acquired data are carefully accounted for. In order to obtain spectral surface reflectance maps from the at-sensor radiance images, an improved atmospheric correction procedure have to be implemented. The availability of data acquired at high spectral resolution allows the detection of different spectral features of many atmospheric constituents. An iterative estimation algorithm based on high resolution data has been developed using the MODTRAN 5 radiative transfer code. The default atmospheric profiles available in that code have been firstly refined through at-ground level measurements of some parameters, like temperature, pressure, humidity, and solar irradiance. Then an iterative procedure has been started tuning the abundance of some atmospheric gases and of aerosol. The MODTRAN 5 code is executed several times with different atmospheric parameters (H2O, CO2, CO, O3, and aerosol abundances) until the calculated ground irradiance matches the in-field measurements and the estimated ground spectral reflectance map is free from the related spectral signatures. To test and validate the method both simulated at-sensor radiance images have been utilized. First results are presented and discussed taking into account the feasibility of avoiding in-field measurements.