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The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy. Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP, SMOS, AMSR2, FY3B, and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture. We further evaluated our product, the official soil moisture products of the five satellites, and the ensemble mean (i.e., arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province, China. The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas. The root mean square error (RMSE ) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province, respectively. Relative to the ensemble mean of the five satellite products, our product improves the accuracy by 9.1％ and 57.7％ in Central Tibet and Anhui Province, respectively. This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.