Soil moisture is a key factor affecting the growth of crops, and microwave remote sensing is one of the most important methods for inverse studies of soil moisture in agricultural fields in recent years. Cotton is a typical water-demanding crop in arid zones, and accurate estimation of soil moisture information in cotton fields is extremely important for optimizing irrigation management, improving water use efficiency, and increasing cotton yield. This study focuses on extracting feature sets by combining Sentinel-1 and Gaofen-6 satellite data, constructing convolutional neural network (CNN), random forest, support vector regression, and eXtreme gradient boosting model to estimate the soil moisture in cotton fields in Shihezi area of Xinjiang, and designing eight groups of experiments according to the different input data sources. The experimental results show that the accuracy of the soil moisture estimate in cotton fields in arid areas with multi-source data is significantly better than that of a single data source. Moreover, the CNN was best estimated when using multi-source data feature sets as inputs, with a coefficient of determination of 0.789, a root mean square error of 0.0249 cm3/cm3, and an average absolute error of 0.0198 cm3/cm3 for its CNN model. This result demonstrates the effectiveness of CNN in soil moisture estimation and also provides a new method for the use of multi-source remote sensing data for accurate soil moisture estimation in cotton fields in arid areas, and also explores the application of Gaofen-6 data in soil moisture.