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Timely acquisition of soil moisture content (SMC) in the root zone of field crops is crucial for achieving precision irrigation. Drone-based multispectral technology and conducted field experiments over two consecutive years (2021—2022) were used to collect SMC data at different soil depths during the soybean flowering stage, as well as corresponding multispectral images from the drone. Vegetation indices and canopy texture features, which are highly correlated with crop parameters, were established. By analyzing the correlation between vegetation indices, texture features, and SMC at various soil depths, parameters with significant correlation coefficients (P<0.05) were selected as input variables for the model (Combination 1: vegetation indices;Combination 2: texture features;Combination 3: vegetation indices combined with texture features). Support vector machine (SVM), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GDBT) models were used to model SMC at different soil depths. The results indicated that compared with soil depths of 20~40 cm and 40~60 cm, vegetation indices and texture features exhibited higher correlations with SMC at the 0~20 cm soil depth. The XGBoost model was found to be the best modeling method for SMC estimation, particularly for the 0~20 cm soil depth. For this depth, the validation set of the estimation model had a determination coefficient of 0.881, a root mean square error of 0.7%, and a mean relative error of 3.758%. The research result can provide a foundation for drone-based multispectral monitoring of SMC in the soybean root zone and offer a reference for rapid assessment of crop growth under water stress conditions.