Estimating Broadband Snow Albedo by an Asymptotic Radiative Transfer Model-Constrained Deep Learning Models

Deep learning (DL) has been widely applied to enhance the accuracy and reconstruction of snowpack parameters due to its powerful nonlinear mapping ability. Here, we propose a broadband snow albedo (BA) estimation method that infuses the asymptotic radiative transfer (ART) model into the DL model and thus imposes constraints on the input variables of the DL model and compares it to the MOD10A1 BA product and the BA simulated using the ART model alone. The results show that coupling a physical mechanism-based snow radiative transfer model with a DL model can successfully improve the estimation accuracy of BA. Compared to the MOD10A1 BA product and the BA simulated using the ART model alone, the accuracy of the BA simulated by the convolutional neural networks (CNNs) model under the constraints of the ART model is improved by 12% and 3%, respectively, and the accuracy of the BA simulated by the long short-term memory (LSTM) model under the constraints of the ART model is improved by 14% and 5%, respectively. The coupling paradigm that integrates the snow radiative transfer model into the DL model effectively balances the advantages of both models. This coupling paradigm creates a complementary association between the snow radiative transfer model and the DL model. This research effectively enhances the simulation and predictive capabilities of snow albedo and is expected to promote the in-depth application of remote sensing big data in combination with physical modeling in the field of cryosphere parameter estimation.