A Stepwise BRDF-Based Land Surface Reflectance Model Over Rugged Terrain

The land surface reflectance is an essential variable of remote sensing, which is significantly affected by topography and atmosphere. Rugged terrain significantly alters the radiative transfer among the solar-terrain-sensor, resulting in large errors in quantitative remote sensing reflectance retrieval in mountainous regions. In this paper, a stepwise bidirectional reflectance distribution function (BRDF)-based land surface reflectance (SBLSR) model that decouples atmospheric and terrain influences is proposed. SBLSR leverages four-stream radiative transfer theory and mountainous radiative transfer principles to retrieve the reflectance of land surfaces under non-Lambertian conditions. This stepwise framework allows for input from either top of atmosphere (TOA) reflectance or surface reflectance products that neglect topographic effects. It shows great agreement with the validation datasets, yielding an overall RMSE of 0.0014 and coefficient of determination (R2) of 0.974 on the simulated multi-angular reflectance dataset, and an overall root mean square error (RMSE) of 0.0242 and mean absolute percentage error (MAPE) of 13.18% on the field multi-angular observation dataset with Gaofen-1 wide field of view (GF-1/WFV) imagery.