Comparison And Validation Of DMRT-QCA Model And DMRT-Bic-NN Model In The Altay Region Of China

An accurate microwave emissions model is essential for the simulating satellite brightness temperature (TB) and developing snow depth retrieval algorithm. The dense media radiative transfer theory based on quasicrystalline approximation (DMRT-QCA) model and the DMRT with bicontinuous (DMRT-Bic) model are currently recognized as representative and highly accurate snow emission models. Meanwhile, the latest development of DMRT-Bic-NN (DMRT-Bic-neural network) model has improved computational efficiency based on DMRT-Bic model. In order to evaluate the performance of the above models in the simulation of brightness temperature, this study compares and validates the capabilities of DMRT-Bic-NN model and DMRT-QCA model to simulate TB at 18.7 GHz and 36.5 GHz with the same input parameters. The results show that the correlation coefficient (R) between the TB simulations of DMRT-Bic-NN and DMRT-QCA is higher at 18.7 GHz than at 36.5 GHz. Moreover, the validation of TB simulations by ground-based observations in the Altay region shows that the DMRT-Bic-NN model has higher simulation accuracy than DMRT-QCA model, and the R / RMSE between the DMRT-Bic-NN simulations and the ground-based microwave radiometer observations TB is 0.35~0.87, 20.46~18.17K at 18.7 GHz and 36.5 GHz with V polarization, respectively. However, during the snow melting season, DMRT-QCA exhibits higher simulation accuracy than DMRT-Bic-NN at 36.5 GHz. This work can provide important guidance for the snow depth retrieval.