Abstract:Accurate soil moisture data are essential for regional water resource management and agricultural production. Thus, a comprehensive accuracy analysis and assessment is necessary to identify the soil moisture product with the optimal overall performance in the target region before applying the existing products, which can then serve as the foundational dataset for subsequent research. The accuracy of four soil moisture products (ERA5, GLDAS, SMCI, and SoMo) was evaluated based on the soil moisture data obtained from different observation sites across Loess Plateau of China. The evaluation included spatial accuracy assessment across different regions, quantitative performance estimation at annual and seasonal (dry/wet) scales, and accuracy appraisal under various land cover types. Based on the evaluation results above, a fusion of multiple products was conducted to enhance the overall accuracy of soil moisture estimation on Loess Plateau. The results showed that ERA5, GLDAS, and SoMo products tended to overestimate the actual soil moisture on the Loess Plateau, with Bias values between 0 m^3/m^3 and 0.3 m^3/m^3 at about 85% of the total sites investigated. The GLDAS product had the highest consistency with the observed multi-year average soil moisture, with Bias values of -0.15 m^3/m^3~0.15 m^3/m^3 at more than 96.5% of the total sites and with a median Bias of 0 m^3/m^3. At annual scale and during both dry and wet seasons, the GLDAS product showed the best overall accuracy, while ERA5 performed the worst. The SMCI and SoMo products had comparable performances. The GLDAS product performed better under the land cover types of grassland, forest, and irrigated cropland, while SoMo product performed best under non-irrigated cropland. Through combining the GLDAS and SoMo products, an integrated optimal soil moisture product was obtained for Loess Plateau, which could enhance the dataset's ability to reflect the dynamic changes in soil moisture on Loess Plateau while maintained a small error in values of soil moisture. The median values of the correlation coefficient (R), root mean square error (RMSE), bias (Bias), and unbiased root mean square error (ubRMSE) between the integrated soil moisture product and site measurements on Loess Plateau were 0.66, 0.06 m^3/m^3, 0.02 m^3/m^3, and 0.04 m^3/m^3, respectively. In general, the GLDAS product was recommended as the optimal soil moisture product for ecological, agricultural, and hydrological studies on Loess Plateau.