Abstract
Abstract. Surface reflectance has a central role in the analysis of land surface for a broad variety of agricultural, geological and urban studies. An accurate atmospheric correction, obtained by an appropriate selection of aerosol type and loading, is the first requirement for a reliable surface reflectance estimation. The aerosol type is defined by its micro-physical properties, while the aerosol loading is described by optical thickness at 550 nm. The aim of this work is to evaluate the radiative impact of the aerosol model on the surface reflectance obtained from CHRIS (Compact High Resolution Imaging Spectrometer) hyperspectral data over land by using the specifically developed algorithm CHRIS@CRI (CHRIS Atmospherically Corrected Reflectance Imagery) based on the 6SV radiative transfer model. Five different aerosol models have been used: one provided by the AERONET inversion products (used as reference), three standard aerosol models in 6SV, and one obtained from the output of the GEOS-Chem global chemistry-transport model (CTM). As test case the urban site of Bruxelles and the suburban area of Rome Tor Vergata have been considered. The results obtained encourages the use of CTM in operational retrieval and provides an evaluation of the role of the aerosol model in the atmospheric correction process, considering the different microphysical properties impact.
Highlights
Hyperspectral remote sensing is used in a wide variety of topics for atmospheric, climatological, environmental and land surface applications (Paronis et al, 2010)
The atmospheric correction algorithms for land have evolved over the years, from earlier scene-based empirical approches (Kruse et al, 1985; Roberts et al, 1986; Conel et al, 1987) to more recent methods based on rigorous radiative transfer modeling (Gao et al, 1993; Ritcher et al, 1996)
The aerosol radiative impact was investigated comparing the reflectance obtained by applying the CHRIS@CRI algorithm with different aerosol models: one using AERONET data, three standard types implemented in 6SV, and one extracted from the detailed simulations of the chemistry-transport model Goddard Earth Observing System (GEOS)-Chem
Summary
1.1 IntroductionHyperspectral remote sensing is used in a wide variety of topics for atmospheric, climatological, environmental and land surface applications (Paronis et al, 2010). The atmospheric correction algorithms for land have evolved over the years, from earlier scene-based empirical approches (Kruse et al, 1985; Roberts et al, 1986; Conel et al, 1987) to more recent methods based on rigorous radiative transfer modeling (Gao et al, 1993; Ritcher et al, 1996). Several studies have shown the crucial role of aerosol optical thickness at 550 nm in the atmospheric transfer modeling (Kauffman et al, 1997; Kokhanovsky et al, 2007, 2008, 2010, Bassani et al, 2010) and on the atmospheric correction of multi and hyperspectral data for ocean and land properties retrieval (Kotchenova et al, 2008; Vermote et al, 1997b; Kauffman et al, 2002; Gaunter et al, 2007; Gao et al, 2009; Goetz et al, 1985). The microphysical properties (size distribution, real and imaginary part of the refractive index), and the optical properties (single scattering albedo and asymmetry parameter) from sunphotometer data are considered as reference
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