Accurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for better understanding of land surface and sub-surface characteristics. In this study, global instantaneous LSE is estimated for the 9-year period from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and for the 5-year period from the Advanced Microwave Scanning Radiometer - 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by using an updated algorithm that minimizes the discrepancy between the differences in penetration depths from microwave and infrared remote sensing observations. Concurrent ancillary data sets such as skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) and profiles of air temperature and humidity from the Atmospheric Infrared Sounder (AIRS) are used. The latest collection 6 of MODIS skin temperature is used for the LSE estimation, and the differences between collections 6 and 5 are also comprehensively assessed. Our analyses reveal that the differences between these two versions of infrared-based skin temperatures could lead up to 0.015 differences in passive microwave LSE values especially in arid regions. The comparison of global mean LSE features from the combined use of AMSR-E and AMSR2 against an independent product - Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM2) shows spatial pattern correlations of the order 0.92 at all the frequencies. However, there are considerable differences in magnitude between these two LSE estimates possibly due to differences in incidence angles, frequencies, observation times, and ancillary data sets.