Abstract

This paper describes the first part of a series of investigations to develop retrievals of aerosol parameters and surface reflectance simultaneously from a newly developed hyperspectral instrument Geostationary Trace gas and Aerosol Sensor Optimization (GEO-TASO) in the visible bands by taking full advantage of available hyperspectral measurement information. We describes the theoretical framework of an inversion algorithm for the hyperspectral remote sensing of the aerosol particles’ optical properties, in which major Principal Components (PCs) for surface reflectance is assumed known, and the wavelength-dependent aerosol refractive index is assumed to follow an power-law approximation with four unknown parameters (m_r,0, b_r m_i,0 and b_i). New capabilities for computing the Jacobins of four Stokes parameters of reflected solar radiation at the top of atmosphere with respect to these unknown parameters and the weighting coefficient for each surface reflectance PC are added into the Unified Linearized Radiative Transfer Model (UNL-VRTM), which in turn facilitates the optimization in the inversion process. Theoretical derivations of the formulas for these new capabilities are provided, and the analytical solutions of Jacobians are shown to differ with the finite-difference calculations by less than 0.2%. Finally, self-consistency check of the inversion algorithm is conducted for the idealized cases of two types of surfaces spectrally characterized by the USGS digital spectral library: green vegetation and rangeland. PC analysis shows that the first six PCs can yield the reconstruction of spectral surface reflectance with errors less than 1%. Assuming that aerosol properties can be accurately characterized, the inversion yields a retrieval of hyperspectral surface reflectance with an uncertainty of 2% (and root-mean-square error of less than 0.003), which suggests self-consistency in the inversion framework. The next step of using this framework to study the information content for retrieving the particle size distribution and wavelength-dependence of refractive index is also discussed.

© 2016 Optical Society of America