Abstract: Soil moisture content (SMC) in farmland is one of the most important indicators to optimize the water sources for high irrigation efficiency in sustainable agriculture. However, only a single source from remote sensing data cannot fully meet the requirements of the SMC inversion. Spatiotemporal coverage and information dimensions are often required to accurately monitor the SMC in saline-alkali farmland. In this study, a multi-source image fusion framework was constructed to realize complementary multi-sensor information and feature enhancement using Landsat 9 OLI and Sentinel-2 MSI data. Saline-alkali farmland was taken from the Hetao Plain. A feature system was established to couple the spectral indices with environmental variables. SMC inversion models were developed using extreme gradient boosting (XGBoost), random forest (RF), and convolutional neural networks (CNN). Shapley additive explanations (SHAP) were then employed to determine the variable contributions. The optimal model was used to generate SMC spatial maps for the area statistics. The results indicate that: 1) Deep fusion of multi-source imagery effectively balanced spatiotemporal continuity with information richness, in terms of spatial resolution and spectral consistency, compared with the single-sensor data. Specifically, while Landsat 9 imagery represented the surface distribution at a regional scale, due to its radiometric stability and tonal continuity; Conversely, Sentinel-2 imagery with high spatial resolution was suitable for the field boundaries and surface textures, but there were spectral fluctuations and brightness inconsistencies in localized areas. Both data types were effectively integrated after deep fusion. The fused imagery significantly enhanced the spatial representation with the spectral continuity. Thereby, the performance of SMC inversion was improved during the bare soil period. 2) A comparison was made on the accuracy of SMC inversion models. Among them, the CNN model shared the best overall performance, with a coefficient of determination R²=0.798, root mean square error (RMSE) of 2.79%, and relative percentage difference (RPD) of 2.10. The RF model ranked second, with R², RMSE, and RPD values of 0.775, 3.08%, and 2.03, respectively. While XGBoost performed the worst, with R², RMSE, and RPD of 0.641, 3.21%, and 1.95, respectively. The SMC-CNN inversion shows that the SMC in the study area was primarily concentrated in the 10%–30% range. 3) SHAP analysis indicated that the precipitation was the most critical driving factor, where the spectral indices contributed the most (46.87%), followed by climatic factors (27.22%). While single-band, soil texture, and topographic factors made relatively limited contributions, at 10.99%, 8.29%, and 6.70%, respectively. The spectral indices were determined to effectively capture key information with the SMC. Furthermore, climate change played a significant role in SMC variations, whereas topographies had little impact on SMC distribution in plain areas. Multi-source image fusion was combined with coupled modeling of environmental variables. The finding can provide a technical approach and theoretical foundation for the large-scale, efficient, and interpretable SMC monitoring during the bare soil period in saline-alkali farmland.