DeepSCS+C: A Physics-Regularized Network for Adaptive Topographic Correction in Mountainous Forests

Topographic radiation distortion, induced by terrain relief, is a significant challenge limiting the accuracy of quantitative analysis in mountainous remote sensing applications. Although semi-empirical topographic correction models, such as the widely used SCS+C, are prevalent, they often exhibit significant overcorrection tendencies and spectral distortions in steep terrain, presenting unresolved challenges. This study proposes DeepSCS+C, a physics-regularized network for adaptive topographic correction in mountainous forests. By characterizing a spatially varying C-field from multiscale terrain features, this framework utilizes SCS+C outputs as weakly supervised proxy labels to guide optimization while effectively suppressing intrinsic over-correction noise. Subsequently, using Sentinel-2 imagery of mountainous forested areas, the performance of the DSC model was systematically validated, and multidimensional evaluation criteria confirmed its robust performance. Experiments demonstrated that the DSC framework mitigates the terrain shadow effect with an efficacy comparable to traditional models and leverages the nonlinear mapping capabilities of deep learning to restore the intrinsic spectral characteristics of surface features accurately. The model’s effectiveness is evidenced by several key metrics: the correlation between reflectance and illumination conditions was reduced to near zero (R2−2sr−1μm−1) across all bands. The findings of this research present a promising and advanced solution for high-precision topographic correction.