In order to construct a long-term series of soil moisture in the Yangtze River Delta, which is frequently used for the validation of various satellite products and agricultural production, the linear regression and BP neural network models were constructed respectively to describe the quantitative relationship between soil moisture and meteorological factors. Based on the paired data of in-situ soil moisture and meteorological observation in the Yangtze River Delta including the city of Shanghai and the provinces of Jiangsu, Anhui and Zhejiang, four effective principal components were obtained by principal component analysis as the input of the established models, and the accuracies of these models were evaluated systematically. The results showed that, although both based on all paired data from soil moisture and meteorological observation, the prediction accuracy of the single BP neural network model (the R² were all above 0.64, and RMSE and MAE were less than 0.043 m³/m³ and 3.4% separately in the sets of the training, validation and testing data) was better than the single linear regression model (R², RMSE and MAE were 0.34, 0.046 m³/m³ and 3.67% respectively). However, accuracies of both these models were worse than the BP neural network models based on each paired soil moisture and meteorological observation data, with the average of R²above 0.75, and RMSE and MAE less than 0.039 m³/m³ and 3% respectively. By constructing the BP neural network models based on each paired soil moisture and meteorological observation data, a more accurate and stable soil moisture fitting result was obtained.