Abstract
Abstract. Radiative transfer models (RTMs) are of utmost importance for quantitative remote sensing, especially for compensating atmospheric perturbation. A persistent trade-off exists between approaches that prefer accuracy at the cost of computational complexity, versus those favouring simplicity at the cost of reduced accuracy. We propose an approach in the latter category, using analytical equations, parameterizations and a correction factor to efficiently estimate the effect of molecular multiple scattering. We discuss the approximations together with an analysis of the resulting performance and accuracy. The proposed Simple Model for Atmospheric Radiative Transfer (SMART) decreases the calculation time by a factor of more than 25 in comparison to the benchmark RTM 6S on the same infrastructure. The relative difference between SMART and 6S is about 5% for spaceborne and about 10% for airborne computations of the atmospheric reflectance function. The combination of a large solar zenith angle (SZA) with high aerosol optical depth (AOD) at low wavelengths lead to relative differences of up to 15%. SMART can be used to simulate the hemispherical conical reflectance factor (HCRF) for spaceborne and airborne sensors, as well as for the retrieval of columnar AOD.
Highlights
The terrestrial atmosphere attenuates the propagation of the solar radiation down to the Earth’s surface and back up to a sensor
We propose an approach in the latter category, using analytical equations, parameterizations and a correction factor to efficiently estimate the effect of molecular multiple scattering
We introduced Simple Model for Atmospheric Radiative Transfer (SMART), as well as its approximative radiative transfer equations and parameterizations
Summary
We propose the fast Simple Model for Atmospheric Radiative Transfer (SMART). It is based on approximative analytical equations and parameterizations, which represent an favourable balance between speed and accuracy. Instead of depending on the classic LUT approach, it permits parameter retrieval in near-real-time. This enables the rapid assessment of regional data requiring exhaustive correction, such as imaging spectrometer data. It supports the straightforward inversion of aerosol optical depth (AOD; τλaer) by implementing radiative transfer equations as a function of τλaer. We assess the accuracy and performance of SMART in comparison with 6S
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