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In this paper, an algorithm for the retrieval of aerosol and\udland surface properties from airborne spectropolarimetric measurements –\udcombining neural networks and an iterative scheme based on Phillips–Tikhonov\udregularization – is described. The algorithm – which is an extension of a\udscheme previously designed for ground-based retrievals – is applied to\udmeasurements from the Research Scanning Polarimeter (RSP) on board the NASA\udER-2 aircraft. A neural network, trained on a large data set of synthetic\udmeasurements, is applied to perform aerosol retrievals from real RSP data,\udand the neural network retrievals are subsequently used as a first guess for\udthe Phillips–Tikhonov retrieval. The resulting algorithm appears capable of\udaccurately retrieving aerosol optical thickness, fine-mode effective radius\udand aerosol layer height from RSP data. Among the advantages of using a\udneural network as initial guess for an iterative algorithm are a decrease in\udprocessing time and an increase in the number of converging retrievals

Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter