An Ocean Emissivity Model Trained from Polarization BRDF Matrix through Multilayer Perceptron Neural Network

The ocean surface emissivity model is crucial for satellite data assimilation and the retrieval of ocean physical parameters. In our earlier studies, we developed a physical emissivity model with a polarized Bidirectional Reflectance Distribution Function (pBRDF-E) that could ensure the consistency between the surface emission and reflection parameters. However, the model has a low computational efficiency.  In this study, a fast ocean emissivity model (OceanEM) is developed based on the emissivity data output from the pBRDF-E model by using the multilayer perceptron neural network. It can compute the polarization emissivity vector with the incidence angle ranging from 0 to 80°, the wind speeds from 2 to 50 m s-1, the sea surface temperatures from −2 to 30°C, the sea surface salinities from 0 to 40 psu, and the frequency from 1.4 to 410 GHz. Along with the FAST Microwave Emissivity Model (FASTEM6) and SURface Fast Emissivity Model for Ocean (SURFEM-ocean), the OceanEM is integrated into the Advanced Radiative Transfer Modeling System (ARMS). To validate the accuracy of OceanEM against the FASTEM6 and SURFEM-ocean, the WindSAT, a polarimetric radiometer, onboard the Coriolis satellite is employed. It is shown that the three models have good consistency in simulating the brightness temperatures of the WindSAT, except for the situation at high wind speeds and low sea surface temperatures. For channels at 6.8 GHz,10.7 GHz with both horizontal and vertical polarization, and 18.7GHz with vertical polarization, the accuracy of OceanEM is higher than that of FASTEM6 but lower than that of SURFEM-ocean, while for channels of 18.7 GHz with horizontal polarization, 23.8 GHz, and 37.0 GHz with both horizontal and vertical polarization, OceanEM outperforms both FASTEM6 and SURFEM-ocean.