Research On Linear Retrieval Model Of 10.65GHz Microwave Land Surface Emissivity

With the development of Fengyun series satellites and the maturity of Satellite Meteorological remote sensing theory and methods,space-borne remote sensors play an increasingly irreplaceable role in numerical weather prediction,climate monitoring and prediction,tropical cyclone monitoring and so on.The radiation it receives includes atmospheric radiation,cloud radiation and surface radiation.Surface radiation is mainly affected by surface emissivity.Because of the complexity and variability of land surface emissivity,the retrieval calculation of land surface emissivity has become a hot and difficult research topic at home and abroad.In order to improve the accuracy of land surface emissivity retrieval,the algorithm model is constructed and developed.The full text is divided into six chapters.The first chapter summarizes the characteristics of emissivity variation and retrieval methods of different surface types,describes the application ways and limitations of various models in detail,expounds the main application of microwave land surface emissivity and the errors existing in the inversion process.The second chapter introduces the radiation transfer mode(CRTM)and the data used in this paper,focusing on the use of CRTM mode to describe radiation.The real state of the transmission process and the flow of the algorithm program.Chapter 3 takes the Taklimakan Desert as the research area,uses Taylor's theorem of multivariate function,deduces the functional relationship between surface factors and microwave land surface emissivity in this area.According to the principle of atmospheric radiation transmission and optimal control theory,use the observed brightness temperature data of FY-3C Microwave Radiation Imager(MWRI)and the brightness temperature data simulated by CRTM model,the cost function is established and the linear retrieval model of land surface emissivity is constructed,the Newton method is used to minimize the cost function,and the functional relationship between the land surface emissivity and the influence factors is obtained.Because the desert area has flat surface,small roughness and sparse vegetation,inthe fourth chapter,the two factors of surface temperature and surface humidity are considered firstly,and then the 2-factors linear retrieval model of land surface emissivity is constructed by putting them into the established model.In order to further study the relationship between land surface emissivity and its influencing factors,in the fifth chapter,based on the linear inversion model of two factors,0.07 m and 0.28 m soil moisture under the surface are added as new influencing factors,and the 4-factors linear retrieval model is constructed,and the retrieval experiment of land surface emissivity is carried out in this area.The results show that the simulated brightness temperature of CRTM model is very sensitive to the change of land surface emissivity;compared with the original model,the simulated brightness temperature calculated by the two linear retrieval models is closer to the observed brightness temperature.The average error between the original model and the observed brightness temperature is 3.059 K,while the error between the 2-factors and 4-factors retrieval models is only 0.913 K and 0.729 K,the land surface emissivity obtained by the retrieval models not only improve the simulation accuracy of brightness temperature,but also coincide with the observation more closely.Furthermore,the spatial and spatial independence of the two linear retrieval models of land surface emissivity is tested.It is found that the simulated brightness temperature of land surface emissivity calculated by these two models is still significantly improved compared with the original land surface emissivity.Therefore,both retrieval models have certain validity and universality for the retrieval of microwave surface emissivity in desert areas.The land surface emissivity varies with time.In order to explore the change of the annual land surface emissivity in the Taklimakan Desert,in the sixth chapter,we choose the data of January,April,July and October of 2014,increased the sample size of the experiment,and studied the seasonal characteristics of the land surface emissivity in the desert.By comparison,it is found that the average deviation between simulated brightness temperature under surface emissivity obtained by the retrieval models and observed brightness temperature is less than 2K,which is improved to less than 60% of the original model and observed average error.The accuracy of landsurface emissivity inversion is improved,and the simulation of brightness temperature in desert area is improved.The improvement of the 4-factors linear retrieval model is higher than that of the 2-factors linear retrieval model and it has higher computational efficiency..Independence tests at different times and locations show that the 2-factor and 4-factor retrieval models are both reasonable and universal.Overall,the 4-factor retrieval model is better than the 2-factor retrieval model.The last chapter summarizes the paper,including the importance of the land surface emissivity and the comparison and analysis of the stability and applicability of the linear retrieval model of the land surface emissivity established in this paper,and prospects the future work from two aspects of impact factors and algorithms.