One of the initial steps in the preprocessing of remote sensing data is the atmospheric correction of the at-sensor radiance images, i.e., radiances recorded at the sensor aperture. Apart from the accuracy in the estimation of the concentrations of the main atmospheric species, the retrieved surface reflectance is also influenced by the spectral calibration of the sensor, especially in those wavelengths mostly affected by gaseous absorptions. In particular, errors in the surface reflectance appear when a systematic shift in the nominal channel positions occurs. A method to assess the spectral calibration of hyperspectral imaging spectrometers from the acquired imagery is presented in this paper. The fundamental basis of the method is the calculation of the value of the spectral shift that minimizes the error in the estimates of surface reflectance. This is performed by an optimization procedure that minimizes the deviation between a surface reflectance spectrum and a smoothed one resulting from the application of a low-pass filter. A sensitivity analysis was performed using synthetic data generated with the modtran4 radiative transfer code for several values of the spectral shift and the water vapor column content. The error detected in the retrieval is less than for spectral shifts smaller than , and less than for extreme spectral shifts of . A low sensitivity to uncertainties in the estimation of water vapor content was found, which reinforces the robustness of the algorithm. The method was successfully applied to data acquired by different hyperspectral sensors.
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