An improved kernel-driven BRDF model coupled with topography: KDCT

Abstract

Rugged terrain complicates the BRDF modeling mainly by the modulation of sun-targetsensor geometry and shadowing effects. An improved kernel-driven BRDF model coupled with topography (KDCT) is put forward by combining the RTLSR model used in the algorithm for MODIS bidirectional reflectance anisotropies of land surface (AMBRALS) and the anisotropic reflectance model for rugged terrain (dESM). The improved model was compared with the original RTLSR model by using the simulated data based on the radiosity approach and the MODIS reflectance data. The validation results revealed that the improved KDCT model outperforms the RTLSR model without topographic consideration and can significantly improve the ability of the kernel-driven model to process the multi-angular reflectance measurements over rugged terrain.