Improving Soil Moisture Estimation by Integrating Remote Sensing Data into HYDRUS-1D Using an Ensemble Kalman Filter Approach

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R 2 value of 0.7964 and a root mean square error (RMSE) of 0.021 cm 3 ·cm −3 . HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm 3 ·cm −3 . The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm 3 ·cm −3 , with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions.