Clear sky Net Surface Radiative Fluxes over rugged terrain from satellite measurements

Net Surface Radiative Flux is the key parameter for global change studies. In this study, two models designed to directly estimate net surface radiative fluxes over horizontal surfaces are developed based on artificial neural network (ANN).These models not only avoid the error propagation involved in the existing algorithms, but also provide the necessary data for estimating fluxes over rugged terrain. The validation results show that the maximum root mean square error (RMSE) of the ANN models is less than 45W/m² and 25 W/m² for net shortwave and longwave fluxes, respectively. By coupling the outputs of ANN models, the shortwave and longwave topographic radiative models are subsequently proposed to derive the net surface fluxes over rugged terrain. The results indicate that great errors can be detected if the topographic effect is ignored over rugged area, especially for net shortwave radiative fluxes.