Abstract Longwave radiation (5–100 μm) is a critical component of the Earth's radiation budget. Most of the existing satellite-based retrieval algorithms are valid only for flat surfaces without accounting for topographic effects. This causes significant errors. Meanwhile, the fixed spatial resolution of remote sensing data makes it difficult to link the satellite-derived longwave radiation to different land models running on various scales. These deficiencies result in an urgent need for topographic modeling and spatial scaling studies of longwave radiation. In this paper, a longwave topographic radiation model (LWTRM) is proposed that quantifies all possible radiation-affecting factors over rugged terrain. For driving the LWTRM, a hybrid method for simultaneously deriving multiple components of longwave radiation from MODIS data is suggested based on artificial neuron networks (ANN) and the radiative transfer simulation. Topographically corrected longwave radiation is then derived by coupling the ANN outputs and LWTRM. Based on this, a general upscaling strategy for longwave radiation is presented. The results demonstrate that: (1) both the proposed LWTRM and the upscaling strategy are rather effective and work well over rugged areas; (2) the ANN-based retrieval method can produce longwave radiation with better accuracy(RMSE