An Optical–Thermal Surface–Atmosphere Radiative Transfer Model Coupling Framework With Topographic Effects

Abstract

Most surface–atmosphere radiative transfer models (RTMs) work only for flat surfaces, with the exception being time-consuming 3-D scene-based models. The deficiency of flat-surface RTMs that do not consider topographic effects is that their applications in earth observation and simulation studies are impaired because rugged terrains make up approximately 24% of the global land surface. Another deficiency of most surface–atmosphere RTMs is that they model reflected and emitted (i.e., solar and thermal) radiative transfer processes separately, which limits RTMs in applications, such as fire detection. This study proposes a unified optical–thermal RTM coupling framework (RTM-CF) that considers topographic effects based on the four-stream approximation theory. The framework couples surface–atmosphere RTMs and can simultaneously simulate a set of parameters at the top-of-atmosphere (TOA) and bottom-of-atmosphere (BOA) levels from optical and thermal spectral ranges. These parameters include the TOA directional radiance/reflectance, TOA exitance/albedo, TOA net radiation, surface radiance/reflectance/albedo, surface downward/upward/net radiation, and FAPAR/APAR. The RTM-CF with topographic effects is compared with the well-known 3-D discrete anisotropic radiative transfer (DART) ray-tracing model and validated by field measurements from three steep sites. The evaluation results show that the simulated reflectance, radiance, and radiation fluxes are consistent with the DART results and the field data, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}&gt;0.93$ </tex-math></inline-formula> and scatter points close to the 1:1 line for all parameters. In this RTM-CF, atmospheric and topographic effects are simultaneously incorporated, and the surface anisotropy is also effectively considered. This framework is highly modularized, which enables it to be easily adapted to different submodels.