Fast and simple model for atmospheric radiative transfer

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 approximative computation of the atmospheric reflectance factor by SMART has an uncertainty ranging from about 5% to 10% for nadir spaceborne and airborne observational conditions. The combination of a large solar zenith angle (SZA) with high aerosol optical depth (AOD) at low wavelengths lead to uncertainties 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

Performance assessment of onboard and scene-based methods for Airborne Prism Experiment spectral characterization

“…However, numerous studies evidenced severe inaccuracies in retrieved reflectance and higher level products due to errors in instrument spectral calibration [3][4][5]. The conversion of at-sensor radiance to physical surface reflectance quantity requires compensating for the presence of the atmosphere and its effects, such as absorption and scattering [6,7]. An erroneous instrument spectral calibration would induce compensation at the wrong wavelengths, causing the appearance of atmospheric residual features in the reflectance spectrum.…”

SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

“…Firstly, the MODIS aerosol product over China is validated to be overestimated [44,52]. Secondly, the parameterization method proposed in this paper works pretty well for AODs range from 0.1 to 1, which may produce some uncertainty for large AODs [53,54]. Thirdly, the underestimation of SSA for aerosol type parameterization may lead to the underestimation of AOD.…”

Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)

“…et al, 2008), and (v) atmospheric physics (Stamnes et al, 1988). Over time and through continuous improvements, these models have increased in realism from simple semi-parametric equations (e.g., Seidel et al, 2010) towards advanced RTMs that allow for explicit 3D representations of complex interactions in the atmosphere. Some examples include: 6SV (Vermote et al, 1997), libRadtran (Mayer and Kylling, 2005;Emde et al, 2016), MODTRAN (Berk et al, 2006(Berk et al, , 2014, MOMO (Fell and Fischer, 2001) and RTTOV (Saunders et al, 2018).…”

“…Given the importance of atmospheric RTMs for remote sensing applications, their intercomparison is one of the main tasks in order to determine their performance and identify the characteristics that differ between models (Kotchenova et al, 2008;Seidel et al, 2010;Proud et al, 2010;Callieco and Dell'Acqua, 2011). The process of comparing various atmospheric RTMs can be a tedious task that requires a good knowledge of the model inputs-outputs and the generation of large database of consistent simulations.…”