This study evaluates the impact of assimilating soil moisture data from NASA's Soil Moisture Active Passive (SMAP) on short-term regional weather and air quality modeling in East Asia during the Korea-US Air Quality Study (KORUS-AQ) airborne campaign. SMAP data are assimilated into the Noah land surface model using an ensemble Kalman filter approach in the Land Information System framework, which is semi-coupled with the NASA-Unified Weather Research and Forecasting model with online chemistry (NUWRF-Chem). With SMAP assimilation included, water vapor and carbon monoxide (CO) transport from northern-central China transitional climate zones to South Korea is better represented in NUWRF-Chem during two studied pollution events. Influenced by different synoptic conditions and emission patterns, impact of SMAP assimilation on modeled CO in South Korea is intense (>30 ppbv) during one event and less significant (<8 ppbv) during the other. SMAP assimilation impact on air quality modeling skill is complicated by other error sources such as the chemical initial and boundary conditions (IC/LBC) and emission inputs of NUWRF-Chem. Using a satellite-observation-constrained chemical IC/LBC instead of a free-running, coarser-resolution chemical IC/LBC reduces modeled CO by up to 80 ppbv over South Korea. Consequently, CO performance is improved in the middle-upper troposphere whereas degraded in the lower troposphere. Remaining negative CO biases result largely from the emissions inputs. The advancements in land surface modeling and chemical IC/LBC presented here are expected to benefit future investigations on constraining emissions using observations, which can in turn enable more accurate assessments of SMAP assimilation and chemical IC/LBC impacts.