Abstract:The Hebei Plain, located in the central part of the North China Plain, is an important grain production area in China and one of the most productive areas worldwide for winter wheat and summer corn. Soil water is foundation of material transportation and energy transmission; and participates in the carbon-water cycle and energy exchange between the land surface and atmosphere. It is also a direct water source and key element of crop growth, which has an important impact on agricultural production, weather forecasting, and drought prediction. Although multisource soil moisture products have been extensively developed and widely utilized, a comprehensive evaluation of the applicability of these products in the Hebei Plain is lacking. Evaluating the applicability of soil moisture products and using them to understand the soil moisture dynamics of the Hebei Plain are of great significance for agricultural production, moisture monitoring, and irrigation decision-making. To compare and analyze the specific performance of the soil moisture products of SMOS, SMAP, FY3B, ERA-Land, GLDAS, and GLEAM in typical farmland in the Hebei Plain, in-situsoil moisture data of surface soil moisture from Wangdu, Bazhou, Weixian, and Luancheng stations in the Hebei Plain from January 2018 to October 2019 were analyzed by considering correlation coefficients, biases, root mean square errors, and unbiased root mean square errors (ubRMSE). Overall, except the data of FY3B in summer, all soil moisture products underestimated the actual soil water contents of different stations in the Hebei Plain. The average correlation coefficient of each soil moisture product during the study period was ranked as GLEAM > FY3B > ERA-Land > GLDAS > SMAP > SMOS, and the average ubRMSE was ranked as GLEAM < GLDAS < SMAP < ERA-Land < SMOS < FY3B. The specific performance of each soil moisture product showed that 1) based on assimilated multi-source data, the accuracies of GLDAS, GLEAM, and ERA-Land were better than those of SMOS and SMAP, with high correlation coefficients and low ubRMSE. The inversion data of GLDAS, GLEAM, and ERA-Land were relatively close to the in-situdata when the water content was high in summer. 2) Many missing data and large fluctuation ranges were found in the FY3B product, but FY3B had a good relationship with the in-situdata with an average correlation coefficient of 0.43 m ³∙m ⁻³. The soil water content was generally overestimated in summer and underestimated in the other seasons. The correlation coefficient of FY3B in summer was low, but the opposite was true in autumn. 3) Overall, the data accuracy of SMAP was higher than that of SMOS. The correlation coefficient between SMAP and in-situdata was higher in summer, but the ubRMSE was higher at the same time; however, they had opposite values in autumn. SMAP could capture dynamic changes in soil moisture when the soil moisture content is high. The data accuracy was better when the measured soil water content was between 0.30 m ³∙m ⁻³and 0.40 m ³∙m ⁻³. 4) Owing to radio frequency interference and other reasons, SMOS greatly underestimated the soil moisture content and performed the worst at each station. The average correlation coefficient of each station was only 0.20 m ³∙m ⁻³, and the biases were all greater than 0.10 m ³∙m ⁻³.