| Land emissivity is a key boundary condition particularly when used in Numerical Weather Prediction (NWP) models. Land surface emissivity can be also used to monitor changes in soil moisture and land use/land cover conditions. Especially in presence of clouds, since microwave radiation can penetrate clouds. Microwave emissivity over ocean is well understood by scientists, but over land this parameter still is not clear because of different land covers and different physical properties. The accuracy of this parameter minimizes the errors in retrieving air temperature, water vapor, and surface temperature.The community has recently requested developments of microwave land emissivity data sets and models that can be directly used in satellite radiance assimilation. Over land, microwave surface emissivity models can be derived only under limited surface conditions. For a land surface where the emissivity model is less accurate, the emissivity can be directly estimated from satellites. The most prominent advantage of Satellite remote sensing is to provide a wide range of space, a continuous surface emissivity spectrum. Therefore, a variety of retrieval methods have been proposed to calculate the surface emissivity from satellite measurements.In this study, the characteristics of the AMSR-E Radio-frequency interference (RFI) signals are statistically analysised, and an algorithm to detect, identify and correct RFI contaminated measurements is developed. Then, the surface emissivity under clear sky conditions over Northern Africa are retrieved from AMSR-E brightness temperature. Microwave emissivity spectra over various surface conditions calculated from Weng et al. (2001) models. The surface emissivity model has been developed and successfully integrated into the Community Rdiative Transfer Model (CRTM). Simulated emissivities were compared with retrievals, and also simulated brightness tempenatures were compared with satellite observations, thus the model was accessed.(1) Analysis of the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Aqua satellite observations reveals very strong and widespread RFI contaminations on the C-and X-band data. In this study, a new algorithm is proposed for RFI detection and correction. The simulated brightness temperature is used as a background signal (B) and a departure of the observation from the background (O-B) is utilized for detection of RFI. It is shown that AMSR-E measurements have better agreement with simulations in a variety of surface conditions after the RFI-correction algorithm.(2) Land surface emissivity products over Northern Africa (latitude 4°N～38°N, longitude 18°W～60°E) using cloud free AMSR-E data and NCEP/GDAS outputs are developed. The retrieved land surface emissivities of different soil types under different frequencies, polarizations and seasons conditions according to the soil texture classification database of NOAH LSM (land surface model) is investigated. The results show that the land surface emissivity is closely related with soil texture, and is significantly varies with soil composition.(3) A new land emissivity data base with a frequency range from 6 to 89 GHz using AMSR-E after RFI contaminated data are corrected is developed. The data base will be a pentad, monthly composited. These data base will be used as part of CRTM to improve microwave sensor data assimilation.(4) Based on the global soil texture classification database of NOAH LSM including 19 kinds of soil types, improve acurcy of the model's inputs, such as soil texture, soil partical seize.(5) The existed surface emissivity model was expanded to multi-layer land emissivity model, which is a radiative transfer scheme for deriving microwave emissivity and reflectivity for a vertically stratified soil and vegetation boundary. Vegetation canopy is handled as a single layer scattering medium with an effective emissivity and reflectivity and surface skin temperature as a lower boundary condition.
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