Field scale crop growth simulation based on the assimilation of observation data and crop growth models is an important method for optimizing field management, agricultural auxiliary decision-making, and crop growth evaluation, which is of great significance for precise management of farmland. In order to construct a numerical model that can accurately simulate the growth and yield of spring wheat in arid areas, this study combined the SWAP (soil water atmosphere plant) model with the IES (iterative ensemble smoother) algorithm to construct a SWAP-IES assimilation simulation system suitable for simulating spring wheat growth in arid areas. Using field test data from 2019 to 2020, the roles of soil water content (SW), leaf area index (LAI), and soil water combination in simulating the growth and yield of spring wheat in arid areas were evaluated, and the impact of assimilation data observation on the yield estimation accuracy of the SWAP-IES system was analyzed and evaluated. The results indicate that: 1) When only assimilating SW, the SWAP-IES system had the highest accuracy in simulating soil moisture (R²=0.87), indicating that assimilating soil moisture can lay the foundation for accurately simulating water stress conditions, evapotranspiration, etc. 2) When there was no assimilation, the R² of the SWAP-IES system for simulating LAI, plant height, and biomass of spring wheat ranged from 0.31 to 0.67, and all treatments were below medium accuracy. The accuracy of LAI simulation for spring wheat was significantly improved when assimilating LAI (R² between 0.76 and 0.96 for each treatment), while LAI+SW had the highest simulation accuracy for spring wheat biomass (R² between 0.73 and 0.92 for each treatment). There was no obvious pattern in plant height, and the simulation of plant height by assimilating LAI and LAI+SW achieved high accuracy (R² ranging from 0.71 to 0.96). Thus, it was necessary to select appropriate observation variables for assimilation simulation based on the research purpose. 3) The accuracy of the SWAP-IES system in predicting spring wheat yield without assimilation was relatively low, with R² of 0.45 and S_RE ranging from 10.89% to 40.34%. The yield estimation had improved when assimilating SW, but the results were still of medium accuracy. The accuracy of yield estimation significantly was improved when assimilating LAI (R² was 0.79), while the overall accuracy of spring wheat yield simulation is the highest when assimilating LAI+SW (R² was 0.87). During the two years, T1, T2, and T3 treatments have the lowest estimated S_RE when assimilating LAI+SW (S_RE ranging from 3.87% to 8.38%), while T4 and T5 treatments had the lowest estimated S_RE when assimilating LAI (all within 10%). 4) The effect of assimilating LAI+SW at flowering stage in single growth period observation data on improving the yield estimation accuracy of SWAP-IES system was the greatest (R² increased from 0.45 without assimilation to 0.74), followed by assimilating observation data at jointing stage and booting stage. Assimilating observation data from multiple growth stages can significantly improve the accuracy of the model's yield estimation. When assimilating LAI+SW observation data from jointing and flowering stages, the estimated yield R² was 0.79, while when assimilating LAI+SW observation data from jointing, booting, and flowering stages, the estimated yield R² reached 0.83. The SWAP-IES assimilation simulation system constructed in this study can effectively simulate the growth and yield formation process of spring wheat under different water conditions by integrating LAI+SW observation data of key growth stages such as flowering and jointing stages, especially under water stress conditions. The results can provide valuable information for using observation equipment such as drones and ground cameras to carry out spring wheat growth monitoring, yield estimation, and precise management under different water management conditions in arid areas.