Abstract
Abstract. We develop a method to derive aerosol properties over land surfaces using combined spectral and angular information, such as available from ESA Sentinel-3 mission, to be launched in 2015. A method of estimating aerosol optical depth (AOD) using only angular retrieval has previously been demonstrated on data from the ENVISAT and PROBA-1 satellite instruments, and is extended here to the synergistic spectral and angular sampling of Sentinel-3. The method aims to improve the estimation of AOD, and to explore the estimation of fine mode fraction (FMF) and single scattering albedo (SSA) over land surfaces by inversion of a coupled surface/atmosphere radiative transfer model. The surface model includes a general physical model of angular and spectral surface reflectance. An iterative process is used to determine the optimum value of the aerosol properties providing the best fit of the corrected reflectance values to the physical model. The method is tested using hyperspectral, multi-angle Compact High Resolution Imaging Spectrometer (CHRIS) images. The values obtained from these CHRIS observations are validated using ground-based sun photometer measurements. Results from 22 image sets using the synergistic retrieval and improved aerosol models show an RMSE of 0.06 in AOD, reduced to 0.03 over vegetated targets.
Highlights
Limited understanding of atmospheric aerosol composition, distribution and function contributes the largest uncertainty to current estimates of radiative forcing (RF) and thereby to the uncertainty in future climate predictions (IPCC, 2013)
The RMSE between true and estimated values, the value of r2, the regression coefficients for the slope and for the offset are listed in Table 6 together with the results for the fine mode fraction (FMF) and single scattering albedo (SSA) properties
An example of retrieved surface reflectance values from one of the 560 sets is displayed in Fig. 11, following successful aerosol retrieval
Summary
Limited understanding of atmospheric aerosol composition, distribution and function contributes the largest uncertainty to current estimates of radiative forcing (RF) and thereby to the uncertainty in future climate predictions (IPCC, 2013). GCOS has a target accuracy of 0.01 for aerosol optical depth (AOD) and 0.02 for single scattering albedo (SSA) (GCOS, 2006). In this paper we aim to use recent improvements in the definition of common aerosol components (Holzer-Popp et al, 2013) to show that better atmospherically corrected surface reflectance and AOD should be possible using synergistic retrieval from new satellite observations. Estimation of surface reflectance to enable determination of parameters such as albedo, requires correction of scattering and absorption by aerosol and gases; the chief uncertainty for most shortwave channels is due to aerosol scattering (Vermote et al, 1997a). Recent reviews of retrieval of aerosol properties from existing satellites are found in Kokhanovsky and DeLeeuw (2009), Kokhanovsky et al (2010), de Leeuw et al (2013) and Holzer-Popp et al (2013)
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