Skip to Main Content
The retrievals of atmospheric water vapor column and surface reflectance from air- or spaceborne hyperspectral imagery require accurate spectroradiometric calibration and a radiative transfer (RT) code. Since RT codes are too time consuming to be run on a per-pixel basis, a common technique employs the offline compilation of an atmospheric database and its subsequent use for the atmospheric correction of the image cube. The challenge is to design the size of the database as small as possible for a requested retrieval accuracy. We present a methodology to compile the database for a specified retrieval accuracy in water vapor and surface reflectance for a given set of input surface reflectance spectra and a chosen RT algorithm. The method is applied as a case study conducted for the planned German imaging spectrometer EnMAP. Some tradeoff considerations are also discussed. For the specified range of columnar water vapor (0.5-4.5 cm), results demonstrate that five water vapor grid points in the database are sufficient to achieve the requested relative root-mean-square retrieval accuracies of 2% and 3% in water vapor and surface reflectance, respectively. It should be pointed out that this is not intended as a general claim of retrieval accuracy achievable under typical remote sensing conditions, but these figures apply only to the theoretical conditions of the calculation, i.e., assuming the same conditions for forward simulation and retrieval. Nevertheless, these figures are indispensable for the design of a database, which is an important step for the atmospheric correction of imaging spectrometer data and the sole topic of this paper.