Study On Sea Surface Salinity Remote Sensing Retrieval By Microwave Radiometer In The South China Sea

Sea surface salinity(SSS) is an important parameter in study of marine influences on the global climate change, and is one of the important factors which decides the basic properties of the seawater, Observing SSS can promote better understanding of the global water cycle. Obtaining sea surface brightness temperature data on inversion of sea surface salinity by spaceborne microwave radiometer can realize large-scale and continuous observation.In this paper, based on the theory of SMOS satellite algorithm,developing the work of satellite data accuracy evaluation and localized correction of RFI detection and mitigation algorithm in the South China Sea,in order to improve the accuracy of the SSS satellite remote sensing.Takes the South China Sea for study area, At first, matching satellite SSS data with the in-situ ARGO data,evaluating the accuracy of SMOS and Aquarius/SAC-D satellite data products by using statistics methods. Second, developing RFI source detection in coastal regions of study area by using angle domain and the Stokes parameters detection algorithm,and proceeding RFI detection for SMOS satellite data, and then according to the contrasting results of SMOS satellite data products V317(no RFI detect) and V500(RFI detect), analysising the feasibility of RFI detection to improve the accuracy of satellite data.Last, correcting the parameter Tg_num_RFI_max RFI in RFI detection algorithm by using statistics methods, and comparing with previous data,through the statistical number of the effective inversion data and Fg_ctrl_suspect_rfi coverage area,comprehensive evaluating the feasibility of RFI detection algorithm modification plan.The results show that The RMSE of SMOS satellite SSS data V317, V500and Aquarius/SAC-D satellite SSS data VI.3in the South China Sea is1.13,0.47and0.62respectively, The accuracy of SSS data resulting from the application of RFI mitigation is improved about0.66, which greatly improve the quality and numbers of the valid data, and achieve the purpose of improving the accuracy of the SMOS satellite SSS data products. Multiple RFI sources exist in the coast of northern South China Sea, and the strong and weak RFI emission sources may be broadcast television microwave station and airport respectively,under the action of aliasing, almost all the grids brightness temperature(multiple angle of incidence) are contaminated by RFI. Tg_num_RFI_max is equal to44%which is obtained by statistical analysis, to a certain extent, the correcting parameter solves the problem of the RFI misjudgment result in data loss,and increases the number of effective data.Monthly data evaluation results show that2011monthly data products RMSE is0.39,which exists a certain differences with the theoretical value,but as far as the current situation of research, the data accuracy to meet the requirements of the related research.