A unified C-band and Ku-band geophysical model function determined by neural network approach

The geophysical model function (GMF) describes the relationship between backscattering and sea surface wind,so that wind vec-tors can be retrieved from backscattering measurement.The GMF plays an important role in ocean wind vector retrievals,its per-formance will directly influence the accuracy of the retrieved wind vector.Neural network (NN) approach is used to develop a unified GMF for C-band and Ku-band (NN-GMF).Empirical GMF CMOD4 and QSCAT-1 are used to generate the simulated training data-set,and Gaussian noise at a signal noise ratio of 30 dB is added to the data-set to simulate the noise in the backscat-tering measurement.The NN-GMF employs radio frequency as an additional parameter,so it can be applied for both C-hand and Ku-band.Analyses show that the σ0 predicted by the NN-GMF is comparable with the σ0predicted by CMOD4 and QSCAT-1.Al-so the wind vectors retrieved from the NN-GMF and empirical GMF CMOD4 and QSCAT-1 are comparable,indicating that the NN-GMF is as effective as the empirical GMF,and has the advantages of the universal form.