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The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature (T-B(p)) measurements at horizontal (T-B(H)) and vertical (T-B(V)) polarizations. This paper presents a performance assessment of SMAP soil emission model and SM retrieval algorithms for a Tibetan desert ecosystem. It is found that the SMAP emission model largely underestimates the SMAP measured T-B(H) (approximate to 15 K), and the T-B(V) is underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the T-B(p) underestimation (> 5 K). The remaining T-B(H) underestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the T-B(p) measurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the T-B(p) simulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the T-B(V) measurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of T-B(V) underestimation. The products based on the SCA with T-B(H) measurements underestimate the SM due to underestimation of T-B(H), and the dual-channel algorithm overestimates the SM. After implementing a new surface roughness parameterization and improving the soil temperature and texture information, the deficiencies noted above in T-B(p) simulation and SM retrieval are greatly resolved. This indicates that the SMAP SM retrievals can be enhanced by improving both surface roughness and adopted soil temperature and texture information for Tibetan desert ecosystem.